Soft Error Rate Simulation and Initial Design Considerations of Neutron

The Pennsylvania State University The Graduate School

SOFT ERROR RATE SIMULATION AND INITIAL DESIGN

CONSIDERATIONS OF NEUTRON INTERCEPTING SILICON CHIP (NISC)

A Dissertation in Nuclear Engineering by Cihangir C¸elik

c 2010 Cihangir C¸elik

Submitted in Partial Fulﬁllment of the Requirements for the Degree of

Doctor of Philosophy

December 2010 The dissertation of Cihangir C¸elik was reviewed and approved∗ by the following:

Kenan Unl¨ u¨ Professor of Mechanical and Nuclear Engineering Department Dissertation Advisor, Chair of Committee

Jack S. Brenizer J. “Lee” Everett Professor of Mechanical and Nuclear Engineering Department

Kostadin Ivanov Distinguished Professor of Mechanical and Nuclear Engineering Department

Arthur T. Motta Professor of Nuclear Engineering and Materials Science and Engineering Chair of the Nuclear Engineering Program

Vijaykrishnan Narayanan Professor of Computer Science and Engineering Department

Tim Z. Hossain Fellow, Cerium Laboratories Special Member

∗Signatures are on ﬁle in the Graduate School. Abstract

Advances in microelectronics result in sub-micrometer electronic technologies as pre- dicted by Moore’s Law, 1965, which states the number of transistors in a given space would double every two years. The most available memory architectures today have sub- micrometer transistor dimensions. The International Technology Roadmap for Semi- conductors (ITRS), a continuation of Moore’s Law, predicts that Dynamic Random Ac- cess Memory (DRAM) will have an average half pitch size of 50 nm and Microprocessor Units (MPU) will have an average gate length of 30 nm over the period of 2008-2012. Decreases in the dimensions satisfy the producer and consumer requirements of low power consumption, more data storage for a given space, faster clock speed, and porta- bility of integrated circuits (IC), particularly memories. On the other hand, these properties also lead to a higher susceptibility of IC designs to temperature, magnetic inter- ference, power supply, and environmental noise, and radiation. Radiation can directly or indirectly affect device operation. When a single energetic particle strikes a sensitive node in the micro-electronic device, it can cause a permanent or transient malfunction in the device. This behavior is called a Single Event Effect (SEE). SEEs are mostly transient errors that generate an electric pulse which alters the state of a logic node in the memory device without having a permanent effect on the functionality of the device. This is called a Single Event Upset (SEU) or Soft Error. Contrary to SEU, Single Event Latchup (SEL), Single Event Gate Rapture (SEGR), or Single Event Burnout (SEB) they have permanent effects on the device operation and a system reset or recovery is needed to return to proper operations. The rate at which a device or system encounters soft errors is deﬁned as Soft Error Rate (SER). The semiconductor industry has been struggling with SEEs and is taking necessary measures in order to continue to improve system designs in nano-scale technologies. Prevention of SEEs has been studied and applied in the semiconductor industry by including radiation protection precautions in the system architecture or by using corrective algorithms in the system operation. De- creasing 10B content (20 % of natural boron) in the natural boron of Borophosphosilicate

iii glass (BPSG) layers that are conventionally used in the fabrication of semiconductor devices was one of the major radiation protection approaches for the system architecture. Neutron interaction in the BPSG layer was the origin of the SEEs because of the 10B (n,α) 7Li reaction products. Both of the particles produced have the capability of ionization in the silicon substrate region, whose thickness is comparable to the ranges of these particles. Using the soft error phenomenon in exactly the opposite manner of the semiconductor industry can provide a new neutron detection system based on the SERs in the semiconductor memories. By investigating the soft error mechanisms in the available semiconductor memories and enhancing the soft error occurrences in these devices, one can convert all memory using intelligent systems into portable, power efficient, direction- dependent neutron detectors. The Neutron Intercepting Silicon Chip (NISC) project aims to achieve this goal by introducing 10B-enriched BPSG layers to the semiconductor memory architectures. This research addresses the development of a simulation tool, the NISC Soft Error Analysis Tool (NISCSAT), for soft error modeling and analysis in the semiconductor memories to provide basic design considerations for the NISC. NISCSAT performs particle transport and calculates the soft error probabilities, or SER, depending on energy depositions of the particles in a given memory node model of the NISC. Soft error measurements were performed with commercially available, off-the-shelf semiconductor memories and microprocessors to observe soft error variations with the neutron flux and memory supply voltage. Measurement results show that soft errors in the memories increase proportionally with the neutron flux, whereas they decrease with increasing the supply voltages. NISC design considerations include the effects of device scaling, 10B content in the BPSG layer, incoming neutron energy, and critical charge of the node for this dissertation. NISCSAT simulations were performed with various memory node models to account these effects. Device scaling simulations showed that any further increase in the thickness of the BPSG layer beyond 2 µm causes self-shielding of the incoming neutrons due to the BPSG layer and results in lower detection efficiencies. Moreover, if the BPSG layer is located more than 4 µm apart from the depletion region in the node, there are no soft errors in the node due to the fact that both of the reaction products have lower ranges in the silicon or any possible node layers. Calcu- lation results regarding the critical charge indicated that the mean charge deposition of the reaction products in the sensitive volume of the node is about 15 fC. It is evident that the NISC design should have a memory architecture with a critical charge of 15 fC or less to obtain higher detection efficiencies. Moreover, the sensitive volume should be placed in close proximity to the BPSG layers so that its location would be within the range of α and 7Li particles. Results showed that the distance between the BPSG layer and the sensitive volume should be less than 2 µm to increase the detection efficiency of the NISC. Incoming neutron energy was also investigated by simulations and the re-

iv sults obtained from these simulations showed that NISC neutron detection efficiency is related with the neutron cross-sections of 10B (n,α) 7Li reaction, e.g., ratio of the thermal (0.0253 eV) to fast (2 MeV) neutron detection efficiencies is approximately equal to 8000:1. Environmental conditions and their effects on the NISC performance were also studied in this research. Cosmic rays were modeled and simulated via NISCSAT to investigate detection reliability of the NISC. Simulation results show that cosmic rays account for less than 2 % of the soft errors for the thermal neutron detection. On the other hand, fast neutron detection by the NISC, which already has a poor efficiency due to the low neutron cross-sections, becomes almost impossible at higher altitudes where the cosmic ray fluxes and their energies are higher. NISCSAT simulations regarding soft error dependency of the NISC for temperature and electromagnetic fields show that there are no significant effects in the NISC detection efficiency. Furthermore, the detection efficiency of the NISC decreases with both air humidity and use of moderators since the incoming neutrons scatter away before reaching the memory surface.

v Table of Contents

List of Figures ix

List of Tables xii

Acknowledgments xv

Chapter 1 Introduction1 1.1 Soft Errors ...... 2 1.2 Radiation Induced Soft Errors ...... 4 1.2.1 Cosmic Rays ...... 5 1.2.2 Alpha Particles ...... 10 1.2.3 Neutron Induced Boron Reactions ...... 10 1.2.4 Spontaneous Fission Sources ...... 14 1.3 Motivation and Statement of Objectives ...... 16

Chapter 2 Basic Design Considerations 19 2.1 Alternative Semiconductor Materials ...... 19 2.2 Alternative Chip Designs ...... 21 2.3 Sample Soft Error Rate Calculation ...... 24 2.4 Overall NISC Design Approaches ...... 26

Chapter 3 Soft Error Rate Measurements 28 3.1 Measurement of Memory Soft Error Rates ...... 28 3.1.1 Experimental Setup ...... 29 3.1.2 Experimental Results ...... 29

vi 3.2 Measurement of Microprocessor Soft Error Rates ...... 30 3.2.1 Experimental Setup ...... 30 3.2.2 Experimental Results ...... 32

Chapter 4 NISC Soft Error Analysis Tool (NISCSAT) 35 4.1 Geant4 ...... 35 4.2 NISCSAT ...... 38 4.2.1 Geometry ...... 38 4.2.2 Material, Temperature, and Electromagnetic Field ...... 39 4.2.3 Physics ...... 40 4.2.4 Source ...... 41 4.2.5 Soft Error Probability ...... 41 4.3 NISC Node Model and NISCSAT Veriﬁcation ...... 42

Chapter 5 Soft Error Analysis of the NISC 45 5.1 NISC Simulation Model and Investigation of the Model Parameters ...... 45 5.1.1 Boron Content in the BPSG Layer ...... 47 5.1.2 Device Scaling ...... 50 5.1.3 Critical Charge ...... 52 5.1.4 Neutron Energy ...... 54 5.1.5 Array Structure ...... 58 5.1.6 Sensitive Volume ...... 63 5.1.7 Intermediate Node Layers ...... 64 5.2 Directional Neutron Detection Capability of the NISC ...... 68 5.2.1 Mono-directional Plane Source Calculations ...... 68 5.2.2 Isotropic Point Source Calculations ...... 78 5.3 Statistics in the NISC Simulations ...... 84

Chapter 6 Cosmic Ray Background and Environmental Effects on the NISC 86 6.1 Cosmic Rays ...... 86 6.1.1 Cosmic Rays Modeling ...... 87 6.1.2 Simulation Results ...... 88 6.2 Environmental Effects ...... 93 6.2.1 Temperature Effects in the NISC Model ...... 93 6.2.2 Humidity Effects in the NISC Model ...... 94 6.2.3 Moderator Effects in the NISC Model ...... 95

vii 6.2.4 Electric and Magnetic Field Effects in the NISC Model . . . . 96

Chapter 7 Conclusions and Future Work 99 7.1 Conclusions ...... 99 7.2 Recommendations and Future Work ...... 103

Appendix A Critical Dimensions for Commercially Available Memories 105 A.1 Microsoft X02170-001 eDRAM ...... 105 A.2 Nanya elixir N2TU51280AF-37B 512 Mbit DDR2 SDRAM . . . . . 108 A.3 PSC A3R12E3GEF G6E 635BLC4M 512 Mbit DDR2 SDRAM . . . 110

Appendix B Additional Tables and Figures for Chapter5 and Chapter6 112 B.1 Additional Tables and Figures for Chapter5...... 112 B.2 Additional Tables and Figures for Chapter6...... 123

Bibliography 133

viii List of Figures

1.1 Basic layout of a transistor structure ...... 2 1.2 Simple layout of the soft error fault tree ...... 5 1.3 Cascade process of the cosmic rays in the atmosphere ...... 6 1.4 Calculated cosmic particle flux at New York City ...... 7 1.5 Cosmic ray neutron flux as a function of energy at sea level ...... 8 1.6 Neutron cross-sections for 28Si...... 8 1.7 Neutron cross-sections for 29Si...... 9 1.8 Neutron cross-sections for 30Si...... 9 1.9 Stopping power and range for an alpha particle in silicon ...... 11 1.10 Neutron cross-sections for 10B...... 12 1.11 Neutron cross-sections for 11B...... 13 1.12 Energy-angle correlation between alpha and lithium from (n,α) reaction for the thermal neutrons ...... 13 1.13 Energy-angle correlation between alpha and lithium from (n,α) reaction for the fast neutrons ...... 14 1.14 The dependence of average energy of neutron spectra E on fissionable parameter Z2/A ...... 15

2.1 Cross sections representative of scaled CMOS structures ...... 23 2.2 A basic memory model for the NISC ...... 23 2.3 Multi-layer NISC model illustration ...... 26

3.1 Schematic drawing of experimental setup at the RSEC for soft error measurements ...... 29 3.2 Memory supply voltage and soft error dependency measurement results 30 3.3 Neutron ﬂux and soft error dependency measurement results . . . . . 31 3.4 Results for boot device as hard drive ...... 33 3.5 Results for boot device as ﬂash drive ...... 34

4.1 Geant4 class categories ...... 37 4.2 NISCSAT block diagram ...... 39

ix 4.3 NISCSAT geometry structure ...... 40 4.4 Calculated α and 7Li trajectories via NISCSAT ...... 44 4.5 Calculated α and 7Li directions in x-y plane via NISCSAT ...... 44

5.1 NISC node model ...... 46 5.2 NISC array model ...... 47 5.3 Soft error variation with 10B content in the BPSG layer ...... 49 5.4 Device scaling effects on the NISC node ...... 50 5.5 Normalized distributions of α, 7Li, and total charge depositions in the NISC model ...... 53 5.6 α and 7Li Energy depositions in the NISC model ...... 53 5.7 Comparison of energy distribution models used in the simulations . . 56 5.8 Soft error simulation results for 3D-array NISC models ...... 59 5.9 NISC intrinsic efficiency curve for 3D-array NISC models ...... 60 5.10 NISC node model for the sensitive volume calculations ...... 63 5.11 NISC node model with an intermediate layer ...... 64 5.12 Simplified CMOS node model (CMOS-1) for NISCSAT simulations . 66 5.13 Modified CMOS node models ...... 67 5.14 Simulation setup for the directional dependency of the NISC . . . . . 69 5.15 Memory soft error maps of the multi-memory system for mono-directional plane source and source location shifted by Dz = 0 cm ...... 70 5.16 Memory soft error maps of the multi-memory system for mono-directional plane source and source location shifted by Dz = 1 cm ...... 71 5.17 Memory soft error maps of the multi-memory system for mono-directional plane source and source location shifted by Dz = 2 cm ...... 72 5.18 Memory soft error maps of the multi-memory system for mono-directional plane source and source location shifted by Dz = 3 cm ...... 73 5.19 Memory soft error maps of the multi-layer memory system for mono- directional plane source and source location shifted by Dz = 50 cm . . 74 5.20 Memory soft error maps of the multi-layer memory system for mono- directional plane source and source location shifted by Dz = 500 cm . 75 5.21 Memory soft error maps of the multi-layer memory system for mono- directional plane source and source location shifted by Dz = 1000 cm . 76 5.22 Memory soft error maps of the multi-layer memory system for mono- directional plane source and source location shifted by Dz = 2000 cm . 77 5.23 Memory soft error maps of the multi-memory system for isotropic point source and source location shifted by Dz = 0 cm ...... 78 5.24 Memory soft error maps of the multi-memory system for isotropic point source and source location shifted by Dz = 0.08 cm ...... 79

x 5.25 Memory soft error maps of the multi-memory system for isotropic point source and source location shifted by Dz = 0.1 cm ...... 80 5.26 Memory soft error maps of the multi-memory system for isotropic point source and source location shifted by Dz = 0.2 cm ...... 81 5.27 Memory soft error maps of the multi-layer memory system for isotropic point source and source location shifted by Dz = 0.08 cm ...... 82 5.28 Memory soft error maps of the multi-layer memory system for isotropic point source and source location shifted by Dz = 0.1 cm ...... 83

6.1 Sea level cosmic ray fluxes, generated by Cosmic Ray Shower Li- brary (CRY) ...... 87 6.2 Sea level cosmic neutron flux modification to CRY using JEDEC standards ...... 88 6.3 Comparison of cosmic neutron fluxes at sea level ...... 89 6.4 Cosmic ray fluxes generated by CRY at 2100 m ...... 90 6.5 Cosmic ray fluxes generated by CRY at 11300 m ...... 90 6.6 Node soft error probability variations with the temperature ...... 93 6.7 Node soft error probability variations with the humidity for the thermal neutron source ...... 94 6.8 Node soft error probability variations with the moderators for the thermal neutron source ...... 96 6.9 Electromagnetic field model for the NISC ...... 97 6.10 Node soft error probability variations with the electric field ...... 97 6.11 Node soft error probability variations with the magnetic field . . . . . 98

B.1 Memory soft error maps of the multi-layer memory system for mono- directional plane source and source location shifted by Dz = 1 cm . . . 120 B.2 Memory soft error maps of the multi-layer memory system for mono- directional plane source and source location shifted by Dz = 100 cm . 121 B.3 Memory soft error maps of the multi-layer memory system for mono- directional plane source and source location shifted by Dz = 200 cm . 122 B.4 Comparison of cosmic muon fluxes at sea level ...... 123 B.5 Comparison of cosmic proton fluxes at sea level ...... 124 B.6 Comparison of cosmic electron fluxes at sea level ...... 124 B.7 Comparison of cosmic neutron fluxes at 11300 m ...... 126 B.8 Comparison of cosmic proton fluxes at 11300 m ...... 126 B.9 Comparison of cosmic electron fluxes at 11300 m ...... 127 B.10 Comparison of cosmic muon fluxes at 11300 m ...... 127

xi List of Tables

1.1 Spontaneous fission rates for selected neutron sources ...... 15 1.2 Average number of neutrons per fission and Watt parameters for spontaneous fission sources ...... 16

2.1 Alternative semiconductor material properties ...... 20 2.2 Neutron cross-sections for (n,α) reaction of possible semiconductor materials and device layer materials ...... 21 2.3 Comparison of range calculations for α and 7Li via SRIM, Geant4, and Nuclenonica ...... 24

3.1 Errors for boot derive as ﬂash drive and hard drive ...... 34

4.1 Calculated α ranges in the semiconductor device materials via NISCSAT 43 4.2 Calculated α ranges in the other semiconductor device materials via Nucleonica ...... 43

5.1 Selected materials for the BPSG layer in the NISC model ...... 48 5.2 Soft error probability calculation results for 10B content in the BPSG layer ...... 49 5.3 Simulation results for neutron energy dependency of the NISC . . . . 54 5.4 Soft error simulation results with various neutron source models and BENR ...... 57 5.5 3D-array NISC simulation results with BENR and thermal neutrons . . 60 5.6 NISC array simulation results for a given ﬁxed memory surface area with BENR ...... 61 5.7 NISC array simulation results for a given ﬁxed memory volume with BENR ...... 62 5.8 Sensitive volume dependent NISC simulation results with BENR .... 63 5.9 NISC simulation results for 100 × 1 × 100 array with an intermediate layer between the BPSG and silicon layers ...... 65 5.10 NISC simulation results for 100 × 5 × 100 array with an intermediate layer between the BPSG and silicon layers ...... 65

xii 5.11 Simulation results for modiﬁed CMOS models with BENR and thermal neutrons ...... 68 5.12 NISCSAT statistics in the simulation results for 2D-array models with 5 µm × ( 2 µm + 1 µm ) × 5 µm node and BENR ...... 85

6.1 Cosmic rays induced soft errors in the NISC node model with different BPSG materials ...... 91 6.2 Cosmic rays induced soft errors in the 3D-array NISC model with BENR as the BPSG material ...... 92

A.1 Microsoft X02170-001 eDRAM package, die, and bond pad sizes . . 105 A.2 Microsoft X02170-001 eDRAM well depths ...... 105 A.3 Microsoft X02170-001 eDRAM transistor horizontal dimensions . . . 106 A.4 Microsoft X02170-001 eDRAM peripheral transistor and polycide vertical dimensions ...... 106 A.5 Microsoft X02170-001 eDRAM metallization dimensions ...... 106 A.6 Microsoft X02170-001 eDRAM dielectric thicknesses ...... 107 A.7 Microsoft X02170-001 eDRAM cell dimensions ...... 107 A.8 Microsoft X02170-001 eDRAM capacitor layer thicknesses ...... 107 A.9 Nanya elixir N2TU51280AF-37B 512 Mbit DDR2 SDRAM horizontal dimensions, minimum pitch metals ...... 108 A.10 Nanya elixir N2TU51280AF-37B 512 Mbit DDR2 SDRAM horizontal dimensions, contacts and vias ...... 108 A.11 Nanya elixir N2TU51280AF-37B 512 Mbit DDR2 SDRAM horizontal dimensions, die, transistors, poly and isolation ...... 109 A.12 Nanya elixir N2TU51280AF-37B 512 Mbit DDR2 SDRAM vertical dimensions ...... 109 A.13 PSC A3R12E3GEF G6E 635BLC4M 512 Mbit DDR2 SDRAM: metals horizontal dimensions ...... 110 A.14 PSC A3R12E3GEF G6E 635BLC4M 512 Mbit DDR2 SDRAM: vias and contacts horizontal dimensions ...... 110 A.15 PSC A3R12E3GEF G6E 635BLC4M 512 Mbit DDR2 SDRAM: transistors, poly, and isolation ...... 111 A.16 PSC A3R12E3GEF G6E 635BLC4M 512 Mbit DDR2 SDRAM: vertical dimensions ...... 111

B.1 Additional results for device scaling simulations with BENR and thermal neutrons ...... 113 B.2 Extended 2D-array simulation results with BENR and constant memory surface area ...... 114

xiii B.3 Extended 3D-array simulation results with BENR and constant memory volume ...... 117 B.4 Extended results for the NISCSAT uncertainty calculations ...... 125 B.5 Extended simulation results for temperature effects on the 2D-array NISC model with the thermal neutron source ...... 128 B.6 Extended simulation results for humidity effects on the 2D-array NISC model with the thermal neutron source ...... 129 B.7 Extended simulation results for humidity effects on the 2D-array NISC model with 2MeV neutron source ...... 130 B.8 Extended simulation results for moderator effects on the 2D-array NISC model with fast and thermal neutron sources ...... 131 B.9 Extended simulation results for electromagnetic ﬁeld effects on the 2D-array NISC model with the neutron source ...... 132

xiv Acknowledgments

This thesis would not have been accomplished without the assistance, help, and kind support of many people whose contributions I gratefully acknowledge. Above all, I would like to thank my advisor Dr. Kenan Unl¨ u¨ for his continued support, guidance, and patience for this research. He has been invaluable on both an academic and a personal level, for which I am extremely grateful. I am very grateful for his motivation, enthusiasm, and immense knowledge. I gratefully acknowledge all the suggestions and guidance of my committee mem- bers, Dr. Jack S. Brenizer, Dr. Kostadin Ivanov, Dr. Vijaykrishnan Narayanan, and Dr. Tim Z. Hossain. I would like to acknowledge the financial, academic and technical support of the Pennsylvania State University, the Radiation Science and Engineering Center at the Pennsylvania State University and its staff. My office mates, Cory Trivelpiece, Kevin Heller, Liang Shi, Sarah Bender, Dundar¨ Uçar, Dagıstan˘ S¸ahin, and Jung Rim deserve special thanks for their cheerful and sup- portive discussions that I will never forget. I owe special debt of gratitude to my mentor Sedat Goluoglu and Nuclear Science and Technology Divison (NSTD) of Oak Ridge National Laboratory (ORNL) where I recently started to work. They were kind enough to give me a flexible schedule for completing this research during the last couple of months. I wish to thank my friends Kurs¸at¨ B. Bekar, Hatice Akkurt, Ali and Esra Akturk,¨ H. Ozlem¨ Ozden,¨ Sacit and Nesrin Çetiner, Ahmet Turhan for all their supports, motivation, and encouragement. I am indebted to my parents, Mehmet and Dursun. They gave me strength and a solid mind to accomplish whatever I may encounter. My brothers, Mustafa and Ali, deserves my gratitude for all their efforts to make me an eldest brother who suits a role model for them. I would like to thank to my mother-in-law Hava, father-in-law Erdal, and brother-in-law Fazlıhan, for helping us whenever we need them particularly after

xv birth of our beloved son. I owe my deepest gratitude to my wife Gonca and my son Bora. It would be impossible to ﬁnish this study without them always being my side and nourishing me with their love. Their very existence alone gives me more than enough willpower to accomplish anything.

xvi Chapter 1

Introduction

As long as the electronic systems are designed to be denser, low power consuming, and more compact, the device scale will continue to decrease. The systems will be more open to influence by the soft errors unless the required preventive precautions are taken. Since the main objective of this study is not to prevent soft errors, but rather to enhance the soft errors taking place in the system, protective measures will not be discussed in this study. As the dimensions and required power for the circuits decrease, reliability of the system is effected more by the internal and external error driving mechanisms. These mechanisms are system noise, voltage marginality, pattern sensitivity, and any external charge-inducing event that can alter the state of the memory node. Radiation induces excess carriers that cause random transient circuit failures also known as soft errors. These external radiation environments are classified as alpha particles emitted from the impurities in the device, alpha particles emitted from the cosmic thermal neutron interaction with boron, and cosmic rays. In this study, only the external radiation effects, more specifically the thermal neutron induced soft errors will be analyzed. Boron content as an impurity in the system architecture is the main source of the 10B (n,α) 7Li reaction in which the energetic α particles induce extra charges into the system and causes to soft errors. In this chapter, sources of the external radiation for the electronic systems and the resulting reactions will be discussed. 2

1.1 Soft Errors

Radiation effects on the semiconductor devices have a broad range of classification depending on the resulting system behavior, location of the particle strike, and location of the device node in the system. Failures from single particles, generally classified as single event effects (SEE), were first investigated to cause data corruption in J-K flip- flops operating in the space environment as early as 1975 [1]. Cosmic neutrons were recognized as a potential source of failures in integrated circuits by Ziegler and Lanford [2]. Alpha-emitting contaminants in packaging materials were identified by May and Woods in 1979 as a reliability concern for dynamic random access memories (DRAM) [3].

Figure 1.1. Basic layout of a transistor structure

As shown in Fig. 1.1, when an ionizing particle strikes the charge sensitive region of the device node (transistor), it generates electron-hole pairs (ehps) as it passes trough the sensitive region. Energy loss by the ion, and the processes of energy absorption in a semiconductor result in a nearly continuous path of free ehps. The energy required to generate a free ehp, Eehp, is approximated as:

Eehp = 2.73 · Eg + 0.55 eV (1.1)

where Eg is the band-gap in electron volts (eV) [4]. For silicon, this translates into approximately 3.6 eV per ehp. The stopping power of the particle, S(E), is defined as the average energy loss of the particle per unit path length. The range of the particle, R(E), is defined as the total path 3 length of the particle while the particle passes through the material until its energy is (almost) zero. Linear energy transfer, (LET ), is defined as the average energy transfer of the ionizing particle during its path in a material. Relation between linear energy transfer, stopping power, and range of the particle is given as:

dE S(E) = − (1.2) dx Z E0 dE0 R(E) = 0 (1.3) Eabs S(E ) 1 LET = S(E) (1.4) ρ

where E is the particle energy, E0 is the initial energy of the particle, Eabs is the energy where the particle is effectively absorbed, and ρ is the material density. Detailed information on calculating stopping power has been gathered by Zielger [5]. For small path lengths, S, the generated charge, Qgen, is given as:

LET · ρ · S Qgen = (1.5) Eehp where Eehp is the energy required to generate a single ehp. Equation 1.5 can be rewritten for the silicon as [6]:

2 Qgen(fC) = 10.8 · S(µm) · LET (MeV · cm /mg) (1.6)

Once carriers are created by an ionizing radiation event, they are subject to drift and diffusion per the semiconductor current equations for holes and electrons, shown in Eq. 1.7 and Eq. 1.8[7]. The total current density for electrons and holes ( Jn and

Jp, respectively) is a result of the combined drift and diffusion components. Carrier drift results from the force exerted by the local electric ﬁeld, E, on that charge, and is proportional to the product of the unit charge (q), the mobility of the carriers (µ), and the carrier concentration (n or p). The diffusion component is also proportional to the dn charge, the diffusion coefﬁcient, D, and the gradient of the carrier concentration, dx or dp dx , shown for the one-dimensional case. 4

dn J = q · µ · n · E + q · D (1.7) n n x n dx

dp J = q · µ · p · E + q · D (1.8) p p x p dx The ion-generated carriers move rapidly from the influence of the electric field from around the junction areas and may diffuse to the field regions due to concentration gradi- ents that are created from ionization and the motion of charge [8]. Detailed examinations of the process of charge transport from single events have been published using technology computer aided design (TCAD) electrical device simulators, which solve the current Eqs. 1.7, 1.8, and the continuity equation for models of semiconductor devices [9–11]. Charge arising from an ionizing event may reach a critical circuit node. The rate at which the charge is collected at the node is a current. The induced current may sufficiently alter the voltage at that node to cause a single event upset (SEU). The details of SEU in SRAM devices have been studied extensively and can be found in [12] and [13]. SEUs are generally considered non-destructive or soft [14], and these are called soft errors. Common SEU-susceptible elements include, but are not limited to, static random access memories (SRAM), dynamic random access memories (DRAM), and latches or flip-flops (FF). The energy transmitted from the energetic particle can also cause permanent damage including Single Event Latchup (SEL), Single Event Burnout (SEB), and Single Event Gate Rupture (SEGR). Such permanent damage events were not examined in this study. Even if the induced charge changes the data in that node, soft errors can be corrected by error prevention or correction mechanisms at the architectural level of the device. A simple fault tree of a single strike on the state bit is shown in Fig. 1.2[15]. Detected, but unrecoverable error (DUE) and silent data corruption (SDC) are classified as soft errors. In this study, only the soft errors will be investigated in the modern silicon memory devices.

1.2 Radiation Induced Soft Errors

Radiation that induces soft errors can come from different radiation sources. Cosmic- rays, alpha particles, and thermal neutrons can be classiﬁed as the main radiation sources. 5

Figure 1.2. Simple layout of the soft error fault tree [15]

Spontaneous ﬁssion sources are not directly considered one of the main radiation sources for the soft errors. However, their detection is of great importance for homeland security and they will be discussed in this study. Among all the possible radiation sources for soft errors, those that emit thermal neutrons are the most important ones for the NISC concept due to high (n,α) cross-section of 10B in the thermal energies.

1.2.1 Cosmic Rays

Cosmic rays do not have an exact scientific definition and are generally described as high energetic galactic particles. As demonstrated in Fig. 1.3[16], cosmic rays interact with the atoms while they pass through the atmosphere and cause a cascade of many types of particles which also interact with the atmosphere and cause more cascade processes. Eventually, most of the particles produced are slowly absorbed into the atmosphere. Mainly the cosmic thermal neutrons will reach to lower altitudes. The calculated theoretical cosmic ray particle flux at New York City is shown in Fig. 1.4 [17]. At terrestrial altitudes, less than 1% of the primary flux reaches sea level and the predominant particles include muons, protons, neutrons, and pions. Since the pions and muons are short-lived and protons are attenuated by Coulombic interactions with the atmosphere, neutrons are the main cosmic radiation to cause an upset in devices at terrestrial altitudes. The cosmic neutron flux at sea level adapted from Ziegler’s data [17] 6 is shown in Figure 1.5[18]. Cosmic thermal neutrons constitute 97% of the cosmic ray particle flux at sea level [15].

Figure 1.3. Cascade process of the cosmic rays in the atmosphere [16]

The primary reaction for high-energy neutrons with silicon that can induce SER is the neutron induced silicon recoil. When a fast neutron collides with a silicon nucleus, it transfers enough of its kinetic energy to knock the silicon from the lattice. Typically the silicon nucleus breaks into smaller fragments, each of which generate charge. The study of atmospheric and terrestrial neutron induced soft errors begins in the late 1980s, with Boeing and IBM who performed a joint study demonstrating neutron soft errors in- 7

Figure 1.4. Calculated particle ﬂux at New York City [17]

ﬂight from dedicated avionics tests [5]. The measurement of neutron induced soft errors is part of commercial parts qualiﬁcation when reliability levels must be guaranteed [19]. The neutron cross-section plots for 28Si, 29Si, and 30Si from ENDF/B-VII are given in Figs. 1.6 to 1.8. The most important isotope of the silicon is 28Si since the abundance of it in the natural silicon is 92.23%. In the natural silicon, abundances of other isotopes are 4.67% 29Si and 3.10% 30Si. Elastic scattering is the dominant interaction of the neutrons with the silicon as demonstrated in the cross-section plots of the silicon isotopes. Natural silicon contains 92.23% 28Si, 4.67% 29Si, and 3.10% 30Si. The average neutron elastic cross-section for 28Si, most abundant, at 0.0253 eV is 2.15 b and at 14 MeV it is 0.76 mb. Natural boron contains 19.9% 10B and 80.1% 11B. The neutron cross-section of (n,α) reaction for 10B is 3837 b at 0.0253 eV and 49 mb at 14 MeV. Considering the difference in the neutron cross-section values of boron and silicon, it clearly shows that the (n,α) reaction in 10B is the dominant one for the thermal neutrons. However, since the 10B (n,α) cross-sections for fast neutron energies are also small (95 mb at 14 MeV), the elastic interactions in the silicon can be important for SERs in the silicon chip. 8

Figure 1.5. Cosmic ray neutron ﬂux as a function of energy at sea level

Figure 1.6. Neutron cross-sections for 28Si 9

Figure 1.7. Neutron cross-sections for 29Si

Figure 1.8. Neutron cross-sections for 30Si 10

1.2.2 Alpha Particles

Alpha particle-induced soft errors from the decay of impurities have long been recognized as a reliability concern by the commercial industry, dating back to the pioneering work of May and Woods in 1979 [3]. Alpha particles are emitted either from the die package [20], from solder [21], or from the basic constituents of the die manufacturing process [22]. Common impurities in these materials include uranium, thorium, and their daughter products. Modern package activity levels can be very low (0.001 α/cm 2·hr) [23] by lowering the activity level through puriﬁcation of the packaging materials, by a choice of alternative solder materials [24], or by the use of encapsulants [20]. Even if these precautions reduce the error rate for a given technology, the trends of decreasing

Q CRIT and increasing packaging density make any level of alpha emitting contaminate a reliability concern. An alpha particle ionizes the atoms in a chip’s substrate through electromagnetic force between itself and the valence electrons. Alpha particle’s range and stopping power in the silicon is shown in Fig. 1.9. Interaction probability of the alpha particle is directly proportional to the density of the material and so is the energy deposition of the alpha particles in the material. The denser the material, the more quickly the alpha particle loses its energy. As an alpha particle loses its kinetic energy, its velocity is also reduced and causes more production of electron-hole pairs since the elapsed time of the alpha particle in the material will be increased. Stopping power increases as energy of α decreases.

1.2.3 Neutron Induced Boron Reactions

The most important source of the soft errors is the 10B (n,α) 7Li reaction for the NISC design. Boron is used in the formation of borophosphosilicate glass (BPSG), a common material used in reﬂow/planarization in semiconductor processing. The BPSG is in close physical proximity to the active semiconductor regions. The maximum possible charge generated in silicon by the lithium and the alpha particles from the 10B (n,α) 7Li reaction is approximately 79 fC that corresponds to full energy deposition of 1.776 MeV α particles. Assuming the reaction products deposit all their energy in the silicon, then the other charge generations are as follow; 45 fC from 1.014 MeV 7Li, 37 fC from 0.840 MeV 7Li, and 65 fC from 1.472 MeV α particles. While 79 fC represents the maximum 11

Figure 1.9. Stopping power (solid triangles) and range (open triangles) for an alpha particle in silicon [18]. possible charge generation, 65 fC represents the most probable charge generation in the silicon from α particles that are produced by the 10B (n,α) 7Li reaction. Thermal neutron induced soft errors became an increasing concern through the 1990s, as circuit critical charges became smaller. The increasing reliability concern has motivated many manufacturers to move away from the use of BPSG. Boron is also used as a p-type dopant and may be found in concentrations of approximately 1021 cm-3 in source/drain implant regions, in which its concentration is comparable to that of BPSG [25]. Thermal neutron induced SER is determined by measurements at a suitable thermal neutron source (e.g., National Institute of Standards and Technology). The experi- mentally measured rate is scaled by the expected abundance of thermal neutrons in the operating environment relative to the thermal neutron beam ﬂux. The test method is straightforward and allows one to directly calculate the SER. 12

 7Li (0.840 MeV) + 4α (1.472 MeV) + γ (0.478 MeV) 94% 1 + 10 −→ 3 2 0n 5B (1.9) 7 + 4α 3Li (1.014 MeV) 2 (1.776 MeV) 6%

Natural boron consists of 19.9% 10B and 80.1% 11B. As shown in Eq. 1.9, not only the alpha particles, but also the lithium recoil has also capability of inducing soft errors in the silicon devices. The contribution of the BPSG layers in the conventional semiconductor devices has been shown as the dominant source of the SERs by earlier studies of Baumann [18, 26–29]. The 10B (n,α) 7Li reaction cross-section is strongly dependent on the incoming neutron energy, for example 3837 b at 0.0253 eV and 49 mb at 14 MeV. Detailed cross-section plots for 10B and 11B are given in Figure 1.10 and Figure 1.11. Dominant reactions in 10B are the (n,α) reaction for the thermal energy region and elastic scattering for the fast energy region. The neutron elastic cross-section of 10B at 0.0253 eV is 2.1 b and 0.9 mb at 14 MeV. Unlike the silicon isotopes, the boron isotopes have no resonance regions at the fast energies and the contribution to the SERs can be much less than the silicon isotopes if the incoming neutron is assumed to have an energy spectrum rather than assuming a mono energetic neutron ﬂux. Neutron induced soft errors have been studied in the literature extensively [6, 28, 30–39].

Figure 1.10. Neutron cross-sections for 10B 13

Figure 1.11. Neutron cross-sections for 11B

The 10B (n,α) reaction is independent of the incoming neutron direction and the direction of the products can be modeled by two-body collision. The simulated 2-body collisions [40] are shown in Fig. 1.12 and Fig. 1.13 for the thermal and fast neutrons. The ﬁgures show that the reaction products, α and 7Li, have exactly the opposite direction for the thermal neutron energies and has a limiting angle of 143.35o from each other when the energy of the incoming neutron, En−→∞.

Kinematically allowed states Fixed event Figure 1.12. Energy-angle correlation between alpha and lithium from 10B (n,α) 7Li reaction o for 0.0253 eV, θα-Li=180.0 ,[40] 14

Kinematically allowed states Fixed event Figure 1.13. Energy-angle correlation between alpha and lithium from 10B (n,α) 7Li reaction o for 20 MeV, θα-Li= 145.6 ,[40]

Since the reaction products will travel in the opposite directions independent from the incoming neutron direction, the assumption of isotropic distribution for both alpha and lithium particles is valid for the thermal energies. Thus, contribution to the soft error events per each 10B (n,α) 7Li reaction is either due to alpha or lithium particle, not both of them at the same time.

1.2.4 Spontaneous Fission Sources

Detection of spontaneous fission sources is a big concern for global and homeland security applications. Therefore, this section provides the essential information of spontaneous fission sources. Spontaneous fission (SF) is a form of radioactive decay char- acteristic of very heavy isotopes, and is theoretically possible for any atomic nucleus whose mass is greater than or equal to 100 amu (elements near ruthenium). In practice, however, spontaneous fission is only energetically feasible for atomic masses above 230 amu (elements near thorium). The elements most susceptible to spontaneous fission are the high-atomic-number actinide elements and the trans-actinide elements. For uranium and thorium, the spontaneous fission mode of decay does occur, but is not seen for the majority of radioactive breakdowns and is usually neglected except for the exact considerations of branching ratios when determining the activity of a sample containing these elements. Spontaneous fission follows the exact same process as nuclear fission, except that it occurs without the atom having been struck by a neutron or other particle. 15

Spontaneous fissions release neutrons as all fissions do, so if a critical mass is present, a spontaneous fission can initiate a chain reaction. Also, radioisotopes for which spontaneous fission is a non-negligible decay mode may be used as neutron sources; 252Cf is often used for this purpose. Spontaneous fission rates for some of the selected sources are given in Table 1.1 and average neutron energies for different sources are given in Figure 1.14[41].

Table 1.1. Spontaneous ﬁssion rates for selected neutron sources [41] Isotope Half-life Fission prob. Neutrons (years) per decay (%) per (g.s) 235U 7.04x108 2.0x10-7 3.0x10-4 238U 4.47x109 5.4x10-5 0.0136 239Pu 2.41x104 4.4x10-10 2.2x10-2 240Pu 6569 5.0x10-6 920 252Cf 2.638 3.09 2.3x1012

Figure 1.14. The dependence of average energy of neutron spectra E on ﬁssionable parameter Z2/A [41]

The energy spectra of the inherent spontaneous ﬁssion sources are represented by the Watt spectrum as;

0 √ W (a, b, E0) = Ce−aE sinh( bE0) (1.10) 16

q b b e 4a 0 where C = π 4a a , and E is the secondary neutron energy. Average number of neutrons per ﬁssion, Watt parameters and average neutron energy for the selected spontaneous ﬁssion sources are given in Table 1.2.

Table 1.2. Average number of neutrons per ﬁssion and Watt parameters for spontaneous ﬁssion sources [42] Isotope ν¯ a b E¯ (MeV) 232Th 2.140 1.25000 4.00000 1.840 232U 1.710 1.12082 3.72278 2.079 233U 1.760 1.16986 4.03210 2.019 234U 1.810 1.29661 4.92449 1.889 235U 1.860 1.29080 4.85231 1.890 236U 1.910 1.36024 5.35746 1.827 238U 2.010 1.54245 6.81057 1.688 237Np 2.050 1.19985 4.24147 1.987 238Pu 2.210 1.17948 4.16933 2.021 239Pu 2.160 1.12963 3.80269 2.073 240Pu 2.156 1.25797 4.68927 1.933 241Pu 2.250 1.18698 4.15150 2.000 242Pu 2.145 1.22078 4.36668 1.961 241Am 3.220 1.07179 3.46195 2.153 242Cm 2.540 1.12695 3.89176 2.097 244Cm 2.720 1.10801 3.72033 2.111 249Bk 3.400 1.12198 3.79405 2.090 252Cf 3.757 0.84746 1.03419 2.130

1.3 Motivation and Statement of Objectives

The main goal of this research was to develop a soft error analysis tool and investigate important design parameters for the NISC by simulating the neutron interactions in the semiconductor memories. The NISC design has the idea of developing a new, miniature, and passive/active neutron sensor/detector especially for homeland security applications. Detection efficiency is mostly dependent on the incoming neutron energy due to 10B (n, α) 7Li neutron cross-section. Although the thermal neutrons can be detected efficiently, NISC 17 efficiency drastically drops for the fast neutrons that are the common neutron sources for spontaneous fission materials and nuclear fuel materials. Conventional neutron detectors generally include a sealed vessel containing a neu- 3 tron sensitive gas, such as He or BF3, and electrical potential of hundreds of volts applied to the anode. In operation, incident neutrons react with the gas to produce charged particles that can be collected in electrodes creating measurable current or voltage. A measurement system coupled to the electrodes measures the electrical pulses and uses this information to indicate the presence of neutrons. These types of neutron detectors are undesirably bulky, require high voltage power supply, acquisition electronics, and are associated with poor sensitivity resulting from operational or environmental noises. In contrast, the NISC has millions of transistors, each containing sensitive areas, and each serving as a tiny neutron detector. It is possible to use the NISC design in the volatile memory (e.g, SRAM, DRAM), as well as in the nonvolatile memory (e.g, flash memory) architectures. Nonvolatile memory architectures do not need any external power supply to keep the stored data and thus provides a passive neutron monitoring system like thermoluminescent detector (TLD). In comparison with the conventional detectors NISC is truly an integrated circuit design. Enriching the BPSG layer with 10B provides a higher density target for the neutron when compared to systems using traditional gas filled detectors. The extremely compact size and solid state architecture of the NISC will allow its integration into other monitoring systems as well as deployed as a passive device which can collect and retain neutron events without human interaction. Integration of the NISC based memories with the modern systems such as cell phones and PDAs would lead to a world wide mobile neutron tracking and detection system since most of these modern systems have been already equipped with Global Positioning System (GPS) as well as the infrared and Bluetooth technologies that enable communication with a central system and as well as one another. In this dissertation, it was intended to achieve the following objectives:

• develop a soft error analysis tool to perform particle transport and energy deposition calculations in a given semiconductor memory model,

• investigate important design parameters for the NISC,

• calculate neutron detection efﬁciency of the NISC, and 18

• investigate environmental effects on the NISC detection efﬁciency.

The organization of this study is as follows. An introduction to soft errors is presented in Chapter1. Basic design considerations including alternative semiconductor materials to silicon and chip design are presented in Chapter2. Various semiconductor materials and their properties besides an analytical calculation of soft errors in a memory are supplied in this chapter. Soft error detection methods and responses in the semiconductor memories for the neutron flux and memory supply voltage at Penn State Breazeale Reactor are presented in Chapter3. NISCSAT development and its verification are supplied in Chapter4. Soft error probabilities for the NISC node and memory models are investigated with device scaling, 10B content, neutron energy, critical charge, and memory array structures in Chapter5. Environmental parameters such as cosmic rays, temperature, air humidity, and electromagnetic fields and their effects on the NISC efficiency are presented in Chapter6. Finally, Chapter7 presents a summary of this study with some conclusions and recommendations for further studies. Chapter 2

Basic Design Considerations

In this chapter, alternative semiconductor materials for silicon and basic chips design considerations are discussed. The feasibility of producing an actual working memory device with these materials and device structure is ignored.

2.1 Alternative Semiconductor Materials

Silicon based semiconductor memories have been proposed for the NISC design because of some advantageous properties. They are easy to produce, the operational data are plentiful, and they are relatively inexpensive to manufacture. As a neutron detector, silicon’s neutronic/electrical properties are not as good as promising new semiconductor candidate materials like boron nitride, germanium, and antimony compounds (indium antimonide, gallium antimonide). Among these new semiconductor materials, boron nitride may be the most outstanding candidate for the NISC design, because 10B can be easily enriched to higher abundances and the boron nitride regions can act as the 10B (n,α) 7Li reaction source in the chip by itself. On the other hand, germanium and all the antimony compounds have low energy gap and high electron/hole mobility. A low energy gap and high electron/hole mobility serves the idea of the using the semiconductor material as a neutron detector. Some alternative materials to silicon and their properties are given in the Table 2.1[43, 44]. The NISC design utilizes the charged particle (like alpha particles) generation in the device and inducing soft errors due to these charged particles. A low energy gap means a low operating voltage, which makes the chip more vulnerable to external charge carriers. A high electron-hole mobility ensures a high re- 20 sponse time for induced charges and enhances the detection efﬁciency. Boron nitride has a higher energy gap relative to other materials, which requires a high operating voltage and is more resistant to external charge induction. However, 10B enriched boron layers will be self alpha particle generators within the device without any addition/modiﬁcation of the layers in the chip.

Table 2.1. Alternative semiconductor material properties [43, 44] Energy Gap Electron/Hole Mobility Material (eV) (cm2/V-sec) Silicon 1.12 1450/450 Germanium 0.66 3900/1900 Indium Arsenide 0.36 22600/200 Indium Antimonide 0.18 80000/1700 Gallium Antimonide 0.72 5000/1000 Boron Nitride 6.40 200/500

Typical semiconductor memory device critical dimensions for commercially available products (Microsoft eDRAM, PSC 512 Megabit DDR2 SDRAM, and Nanya elixir 512 Megabit DDR2 SDRAM) obtained from Chipworks [45] are given in Appendix A. The semiconductor industry keeps most of the information about their technology as proprietary information under the copyright law and it is very difﬁcult to obtain any speciﬁc data. The micron or sub-micrometer scale size for the silicon active region can be assumed when the device structures are investigated. Beside silicon, other device layer materials are aluminum, oxygen, tungsten, boron, phosphorus, titanium, nitrogen, or compounds of these elements. The (n,α) reaction properties given in Table 2.2 are useful when examining possible contribution of these materials, as well as the promising new semiconductor materials to charged particle production. As shown in Table 2.2, there is no favorable reaction probability at the thermal energy other than 10B among the possible semiconductor and device materials for the NISC design. This indicates that the selection of 10B enriched BPSG layer is essential for the NISC design. Regarding the electrical, neutronic, and physical properties of the semiconductor materials, a feasible new material selection can be more advantageous than the silicon for the NISC design. A memory technology that uses previously mentioned possible semiconductor materials such as boron nitride, indium antimonide, and gallium antimonide is not established yet for the semiconductor memories. 21

Table 2.2. Neutron cross-sections for (n,α) reaction of possible semiconductor materials and device layer materials Isotope σ(n, α) Q-Value Material (Nat. Abund. %) @ 0.0253 eV @ 14.0 MeV (MeV) 28Si (92.23 ) 0 b 222 mb -2.65 Si 29Si (4.67 ) 0 b 158 mb -0.03 30Si (3.10 ) 0 b 68 mb -4.20 70Ge (21.23 ) 0 b 31 mb 2.96 72Ge (27.66 ) 0 b 13 mb 1.48 Ge 73Ge (7.73 ) 0 b 8 mb 3.91 74Ge (35.94 ) 0 b 15 mb -0.45 76Ge (7.44 ) 0 b 1 mb -2.16 113In (4.30 ) 0 b 4 mb 3.74 In 115In (95.70 ) 0 b 2 mb 2.73 As 75As (100.00 ) 0 b 10 mb 1.20 121Sb (57.21 ) 0 b 2 mb 3.28 Sb 123Sb (42.79 ) 0 b 1 mb 2.16 69Ga (60.10 ) 0 b 23 mb 2.57 Ga 71Ga (39.90 ) 0 b 3 mb 1.07 10B (19.90 ) 3837 b 49 mb 2.79 B 11B (80.10 ) 0 b 32 mb -6.63 14N (99.63 ) 0 b 60 mb -0.15 N 15N (0.37 ) 0 b 60 mb -0.15 P 31P (100.0 ) 0 b 121 mb -1.94 Al 27Al (100.0 ) 0 b 122 mb -3.13 Ti 48Ti (73.7 ) 0 b 33 mb -2.02 182W (26.5 ) 0 b 2.1 mb ≥ 7.87 183W (14.3 ) 0 b 1.9 mb ≥ 8.10 W 184W (30.6 ) 0 b 720 µb ≥ 5.20 186W (28.4 ) 0 b 1.4 mb ≥ 2.46

2.2 Alternative Chip Designs

The NISC project aims to produce a neutron detector using the basic designs of commercially available memory structures by modifying the BPSG layers and enriching the boron content with 10B. The fact that most of the chip is composed of silicon, which has a neutron mean free path of 10.3 cm, interaction probability of the neutrons in the 22 silicon region is small. The interaction probability of a neutron within a transistor node, with a thickness of 50 µm, is nearly 5x10-4. This probability can be interpreted as only 5 of the incoming 10000 neutrons in the direction of the transistor node will interact in the silicon layers and probably will be scattered away while the rest of the neutrons will penetrate through the device without any interaction. Interaction probability for the natural boron, which has a neutron mean free path of 96 µm, is about 50%. If the boron layer is enriched with 10B up to 90%, then this probability increases almost 5 fold of that of natural boron has. Neutrons have a mean free path of 19 µm in the enriched boron and the neutron interaction probability is 100%. Considering the dimensions of recent memory nodes, which are almost in the sub-micrometer ranges, the neutron interaction probability decreases since the physical cross-section of the device is very small. On the other hand, the decrease in the physical volume also decreases the operating voltage requirements and so enhances the SER events in the device. If the terrestrial cosmic ray induced thermal neutrons (96% of the cosmic ray flux) are considered as the source of the external radiation, which will induce the 10B (n,α) 7Li reactions in the device, the device geometric efficiency and the operating voltage become very important. The reason for this strict neutron economy lies in the fact that the cosmic thermal neutron flux, depending on the location, is about 60 n/hr-cm2 [26, 28]. As mentioned in the previous section, new semiconductor material based memories can be examined as a possible candidate for the new memory device. In addition, to increase the detection efficiency of the NISC, some modification in the available silicon based transistor node structure can be made while ensuring the device performs the basic memory operations by inserting major 10B enriched layers around the sensitive volume, which is defined as the most sensitive region of the transistor node for gathering electron-hole pairs. Figure 2.1 shows an arbitrary demonstration of Complementary Metal-Oxide-Semiconductor (CMOS) model [46, 47] for simulation of the soft errors. Since the other device layer materials have no major contribution in the SER, a simple NISC design model was chosen for investigating the general characteristics of the SER. The model only consisted of a BPSG layer that is located at the top of the active silicon layer as shown in Fig. 2.2. The detailed simulation model results including the effects of the silicon and BPSG layer thicknesses, the 10B content in the BPSG layer, the critical charge, and the incoming neutron energy is discussed in Chapter5. Considering the reaction products from the 10B (n,α) 7Li reaction, both the 7Li ions and α particles 23

Figure 2.1. Cross sections representative of scaled CMOS structures [46, 47] can generate electron-hole pairs in the sensitive volume, which is a silicon layer in the transistor node. Ranges of α particles and 7Li ions from the 10B (n,α) 7Li reaction are given in Table 2.3 for the silicon and boron by using the stopping power application in Nucleonica [48], SRIM 08 [49], and Geant4 [50].

Figure 2.2. A basic memory model for the NISC

As shown in Table 2.3, both the 7Li ions and α particles can travel the same order 24

Table 2.3. Comparison of range calculations for α and 7Li via SRIM, Geant4, and Nuclenonica Range (µm ) Energy Particle SRIM 08 [49] Geant4 [50] Nucleonica [48] (MeV) Si B Si B Si B 1.472 5.15 3.27 5.43 3.83 5.64 3.61 α 1.776 6.35 4.05 6.64 4.72 6.46 4.45 0.840 2.45 1.69 2.55 1.85 2.51 1.88 7Li 1.014 2.79 1.89 2.91 2.08 2.87 2.11

of ranges in boron and silicon materials. The agreement between the code results indicates the reliability of the Geant4. The ranges are also in the same order as the device architecture dimensions which can be inferred as one 7Li ion or α particle can cause multiple upset events in the node. Instead of one array of transistor/memory nodes, if we use multiple layers of transistor/memory nodes, we can enhance the efﬁciency of the device by allowing multiple upset events in different layers. These multiple upsets in the different layers can also be used for predicting the incoming neutron source direction by comparing the soft error locations in the different layers and calculating the direction of the neutrons.

2.3 Sample Soft Error Rate Calculation

In order to demonstrate the basic concept of the SER calculations, the following example calculation is given with the assumptions below:

• the sensitive volume (SV) has the dimensions of 1 µm x 1 µm x 1 µm,

• the critical charge of the device is 10 fC,

• the BPSG layer consists of only uniformly distributed boron (90% 10B enriched), and there are no other layers of other materials between the SV of the device and boron layer,

• the BPSG layer has a thickness of 1 µm,

• no multi bit upsets are created, each particle can contribute to one SER,

• there are a total of 1x106 nodes/memory device, 25

• the incoming neutron ﬂux is 10 n/cm2-sec with thermal energy, and

• the neutrons are mono-directional and are coming towards to the center of the SV with an angle of 0o.

Both of the reaction products will be able to deposit energy in the SV because of their relatively high ranges in the silicon. Produced particles will also keep their direction in silicon due to their heavy masses. However, the reaction products will travel in opposite directions and only one of them can deposit energy in the SV. Energy deposition in the SV and the SER can be calculated by using the assumptions above as;

• The required energy for the critical charge deposition in the device: −→ the required energy for one electron-hole pair in silicon is 3.6 eV/ (ehp) −→ the required energy for generating the critical charge = 3.6 / 1.6022x10-19 x 10 x 10-15 = 0.225 MeV

• The average energy depositions: −→ from the α particles: (1 µm / 5.15 µm x 0.94 x 1.47 MeV +1 µm / 6.35 µm x 0.06 x 1.776 MeV ) = 0.285 MeV −→ from the 7Li ions: (1 µm / 2.45 µm x 0.94 x 0.840 MeV +1 µm / 2.79 µm x 0.06 x 1.014 MeV ) = 0.344 MeV −→ either particle can deposit required energy to cause a soft error.

• Since no multiple upset events are considered, only 10B (n,α) 7Li reactions in the boron layer cause upset events. Therefore, the SER event rate is generally limited by the 10B (n,α) 7Li reaction probability in the device. −→ the number of incoming neutrons is 10 n/cm2-sec x 1 µm x 1 µm = 1.0x10-7 n/sec −→ the 10B (n,α) 7Li reaction probability, neutron mean free path in boron is 19 µm, 1 µm/ 19 µm = 5.2%

• The SER equals 0.052 x 1.0 x10-7 = 5.2 x 10-9 error/sec-node −→ The failure in time (FIT) is deﬁned as: error/109 h/device −→ The SER is 1.872x1010 FIT 26

The calculations above are very optimistic for neutron detection due to the presented assumptions. The actual SER would be expected as 10-7 to 10-3 times the calculated value.

2.4 Overall NISC Design Approaches

NISC design can be improved by enhancing the geometric detection efficiency of the basic device structure. Increasing the node numbers simply by putting a lot of detector arrays together can improve the detection efficiency, e.g. putting such a number of arrays so that they can cover a segment of an area would probably work efficiently to detect the incoming neutron flux. In addition, since both reaction products can travel more than one transistor node, designing a multi-layer of memory structure would increase the detection efficiency as well as the determination of the incoming neutron flux direction. A simple scheme of multi-layer chip design layout is shown in Fig. 2.3.

Figure 2.3. Multi-layer NISC model illustration

NISC design also aims to determine the source of the incoming neutrons. This consideration is not easy to accomplish due to the fact that incoming neutrons slow down in the surrounding materials after neutrons are emitted from the source. Another obstacle to identify the source is the neutron energies after spontaneous ﬁssion event; almost all of the spontaneous ﬁssion neutron sources emit neutrons with an average energy of 2 MeV, as shown in Table 1.2. Even if there were different sources with distinct energies, due to the surrounding materials, neutrons would be moderated to the 27 thermal energies. Except for the neutron emission rates of the different sources, an energy-based comparison can not be done easily with passive interrogation techniques as proposed in the NISC project. Adjusting the device operation frequency/clock to detect the SER events in the system can be a key point for differentiation of the neutron sources since the neutron emission rates are different. Chapter 3

Soft Error Rate Measurements

In this chapter, preliminary experimental results for the SER testing method in semiconductor memories are presented. The experiments on commercially available memories were carried out at Radiation Science and Engineering Center (RSEC) facilities. The Penn State Breazeale Reactor (PSBR) [TRIGA] was used as the neutron source in the experiments. The maximum rated power of the reactor is 1 MW in the continuous mode, and 2000 MW in the pulse mode. The reactor power was adjusted from 10 W to 1 MW to observe the soft error rate dependence on the neutron flux. The average thermal flux at the exit of the beam port is about 3x107 neutrons/cm2·sec, and the fast flux is about three orders of magnitudes smaller. Figure 3.1 shows the experimental setup. In the facility, thermal neutron beam is transported via a tangential beam tube that extends from the D2O moderator tank to beyond the biological shield. A circuit board was placed in front of the beam tube exit.

3.1 Measurement of Memory Soft Error Rates

In order to examine the incoming neutron ﬂux and operating voltage effects on the SER in the semiconductor memories, SER measurements with two different kinds of memories were performed. 29

Figure 3.1. Schematic drawing of experimental setup at the RSEC for soft error measurements

3.1.1 Experimental Setup

The experimental setup for the beam port experiments consisted of a test circuit board setup as shown in Fig. 3.1 and a computer equipped with interface cards for analysis. The experimental setup for the memories consisted of a custom board interfaced with a computer through a General Purpose Interface Bus (GPIB) card (from National Instru- ments). The board itself has off-the-shelf SRAM memory chips. The board is controlled through a LabVIEW [51] interface. The controlling application consists of several simple routines to read and write a user-speciﬁed value across the whole memory. During the readout, the application compares the written value to the value in each address. The total number of errors, which occur due to radiation, can be easily counted by the difference in the number of bits before and after radiation.

3.1.2 Experimental Results

Two different types of memories, 16-kbit and 4-Mbit memories from different vendors (Vendor A and Vendor B), were tested at various supply voltages and reactor power levels. Vendor B’s chip is denser than Vendor A’s chip, therefore it had a higher SER as expected. Figure 3.2 shows the operating voltage dependency of the SER, conﬁrming the exponential dependence of the soft error rate on the device’s operating voltage as pointed out by several authors [52] as; 30

QCRIT /QS SER = φ · A · e ,QCRIT = VCC · C (3.1) where φ is the intensity of the neutron ﬂux, A is the area of cross-section of the node,

QS is the charge collection efficiency, QCRIT is the critical charge, VCC is the supply voltage, and C is the node capacitance. Since the reactor power and the flux at the exit of the beam port are directly proportional to each other, varying the reactor power effectively changed the neutron flux impinging on the test sample, and yielded an increase in the SER. Figure 3.3 presents the neutron flux dependency of the soft errors. These results prove that the soft error rate increases as the reactor power increases.

Vendor A Vendor B

Figure 3.2. Memory supply voltage and soft error dependency measurement results[53]

3.2 Measurement of Microprocessor Soft Error Rates

In order to examine the incoming neutron ﬂux effect and operating voltage/frequency on the SER in the microprocessors, SER measurements with a microprocessor were carried out with both a conventional hard drive and a ﬂash drive.

3.2.1 Experimental Setup

Experimental setup was the same one used in the previous experiment, shown in Fig. 3.1. The system setup consisted of a BitsyXB single board computer using a PXA270 31

Vendor A Vendor B

Figure 3.3. Neutron ﬂux and soft error dependency measurement results [53]

XScale processor from Applied Data Systems [54]. The PXA270 processor is an integrated system-on-chip (SOC) microprocessor for high performance, low power, portable, handhold and handset devices fabricated at 180 nm or lower technologies. It is a 78- stage super pipelined Intel XScale RISC technology designed for high speed and low power applications. The PXA270 microprocessor has the ability for dynamic voltage management (voltage and frequency scaling) with frequency scaling from 104 to 520 MHz. The cache is organized as 32 kB instruction cache, 32 kB data cache and a 2 kB mini-data cache. Both instruction and data caches are parity protected (tags are not protected). The single board computer also supports external memory of 64 MiB syn- chronous DRAM, 32 MiB Flash memories and PCMIA, Type I and II, 3.3 and 5 V and Compact Flash cards. Complete details about the single board computer can be found at the manufacturer’s website [54]. The PXA270 processor, which was a part of a single board computer with a USB drive in which a Debian Linux [55] operating system was installed, was interfaced through a crossover cable and a secure shell client was used to connect to it from a remote host. Also, the debug port messages were obtained using a separate serial cable and terminal client on the remote host. The selected section of the board was tested on-line multiple times in the actual setup before the reactor was started. The board was exposed to the neutron ﬂux after the reactor reached the desired stable power level. 32

3.2.2 Experimental Results

Due to the small size of the on-chip memory, the operating system (OS) used was an elementary version of Debian Linux. An external memory was required to install the full root system of the OS such that the C programs used for testing can be compiled and tested on the system. A complete version of the Debian Linux was installed on both a USB based flash drive and a USB based hard drive for all the tests. This was done since the flash drive was in the vicinity of the beam and could be affected from the exposure since it had a similar architecture as the semiconductor memories. The USB drive was interfaced through a crossover cable and a secure shell client was used to connect it to the remote host. The debug port messages were also obtained using a separate serial cable and terminal client on the remote host. The single board computer was setup in front of the beam with only the processor exposed to the neutron beam and rest of the chip being protected from the beam by a polyethylene and lead shield. The program used for the tests was a simple matrix multiplication where the source matrices were multiplied after creation and the results were stored along with the source matrices in a new file. Suitable delays were chosen such that the read/write delays to the files were negligible. The source matrices along with the result matrices were compared with the original matrices to detect the errors. The results obtained for various test conditions are as follows. Tests were conducted for different operating conditions such as operating voltage and frequency. Tests were also performed by using both flash and hard drives as booting devices. Figures 3.4 and 3.5 show the test results when a hard drive and a flash drive were used as the boot devices for 143 and 80 minutes, respectively. PXA270 processor has the ability for voltage scaling which allows us to change the voltage and frequency of operation. The frequency can be varied from 104 to 520 MHz with the power supply voltage variation from 1.18 to 1.55 V. The key results obtained from these tests indicated that the majority of the errors result in segmentation faults+bus errors or irrecoverable crashes. The segmentation faults and majority of the irrecoverable crashes were due to d-cache parity error. The type of error can be easily categorized by the error report captured using the debug port. The d-cache parity error results from the flip of the bit in the d-cache (data caches are parity protected for single error detection). The corruption in the data is prevented by the inherent property of the kernel to issue segmentation faults when these types of errors occur. 33

Figure 3.4. Results for boot device as hard drive (test time is 143 minutes) [56]

It was also observed that the error rate decreased as the frequency varied from 104 MHz to 520 MHz due to the increase in the operating voltage. Other errors mentioned in Figs. 3.4 and 3.5 include all other types of errors such as connection lost, debug port crashes, file system corruptions, and crashes during boot. Another type of failure, which occurs due to a logic error, results in file system corruption. This failure occurs when the inode fields (store file information such as size, type, date of creation etc) for different files are updated on to the USB memory. As each file in the USB memory is being modified when a process is running, the processor updates the information in these inode files. Thus, the error that could have occurred in the inode fields might be due to a logic error as the errors in the cache lead to segmentation faults. It was also observed that these file system corruptions occurred when hard drives were used as the boot device which might be due to a relatively larger number of write backs/updates of inode fields in case of hard drives. Table 3.1 shows the total error rate comparison between a flash drive and a hard drive. The majority of the errors during the usage of hard drives were due to the segmentation faults (combined with the bus errors) 34

Figure 3.5. Results for boot device as ﬂash drive (test time is 80 minutes) [56] and irrecoverable crashes while the errors were equi-probable when a ﬂash drive was used. Discrepancy in the number of total errors is marginal when the boot devices are different.

Table 3.1. Errors for boot derive as ﬂash drive and hard drive (test time is 340 minutes) Type of Error Flash Drive Hard Drive Segmentation Faults + bus errors 16 24 Irrecoverable Crashes 16 20 Other Errors 15 8 Total Errors 47 52 Chapter 4

NISC Soft Error Analysis Tool (NISCSAT)

In order to model and analyze the NISC, an analysis tool using Geant4 (for GEometry ANd Tracking) [50, 57] as the transport and tracking engine was developed for the simulation of the charged particle interactions in the semiconductor memory model. The analysis tool was named NISC Soft Error Analysis Tool (NISCSAT). In this chapter, an overview of the NISCSAT will be given.

4.1 Geant4

Geant4 is a toolkit for the simulation of the passage of particles through matter using Monte Carlo methods. It is developed by the European Organization for Nuclear Research (known as CERN) and it uses object-oriented programming. The Geant4 applications include high energy, nuclear and accelerator physics, as well as studies in medical and space science. There is an abundant set of physics models to handle the interactions of particles with matter across a very wide energy range. Data and expertise have been drawn from many sources around the world and in this respect, Geant4 acts as a repository that incorporates a large part of all that is known about particle interactions. All aspects of the simulation process have been included in the toolkit:

• the geometry of the system,

• the materials involved, 36

• the fundamental particles of interest,

• the generation of primary events,

• the tracking of particles through materials and electromagnetic ﬁelds,

• the physics processes governing particle interactions,

• the response of sensitive detector components,

• the generation of event data,

• the storage of events and tracks,

• the visualization of the detector and particle trajectories, and

• the capture and analysis of simulation data at different levels of detail and reﬁne- ment.

Geant4 is written in C++ and exploits advanced software-engineering techniques and object-oriented technology to achieve transparency. For example, the way in which cross-sections are input or computed is separated from the way in which they are used or accessed. Similarly, the computation of the final state can be divided into alternative or complementary models, according to the energy range, the particle type, and the material. To build a specific application the user chooses from among these options and implements code in user action classes supplied by the toolkit. Geant4 class categories are shown in Fig. 4.1. A serious problem with previous simulation codes was the difficulty of adding new or variant physics models; development was difficult due to the increased size, complexity and interdependency of the procedure-based code. In contrast, object-oriented methods help manage complexity and limit dependencies by defining a uniform interface and common organizational principles for all physics models. Within this frame- work the functionality of models can be more easily recognized and understood, and the creation and addition of new models is a well-defined procedure that entails little or no modification to the existing code. Accessing and modifying any particle or reaction is very easy compared to other available Monte Carlo codes like MCNP/MCNPX [58, 59] since the users can implement their own tracking algorithms. A particle can 37

Figure 4.1. Geant4 class categories be tracked and modified at each step while it is interacting with the material unlike the MCNP in which the general tally information can only be obtained after the simulations. Another advantage of the Geant4 is that the user defines the any threshold or cut-off values for the simulations. Thus, users can define the overall system behavior according to the model size which can be in the order of nuclear reactor core or in the order of nano-scales as in the case of the semiconductor chips. MCNP, on the other hand, is insensitive for the geometric sizes under 10-5cm (0.1 µm) and therefore is not reliable 38 for sub-micrometer scale simulations. Geant4 has been used particularly to calculate the cascade interactions of cosmic rays within the atmosphere and calculate soft errors in the semiconductor memories in the literature [13, 35, 57, 60–67]

4.2 NISCSAT

NISCSAT was developed for calculating soft errors in particularly NISC, but it can also be used for generic semiconductor memories. NISCSAT can be integrated with Soft Error Analysis Tool (SEAT) [68] which supports soft error analysis of systems rang- ing from the device to the architectural level. NISCSAT performs particle transport and energy deposition calculations in a given system model. It was written in C++ and takes advantage of object-oriented programming. NISCSAT inherits many classes from Geant4 as well as introduces new classes for speciﬁc purposes like soft error probability calculation. A simpliﬁed block diagram of the NISCSAT is shown in Fig. 4.2. NISC- SAT does not performs any device or architecture level calculations, but only particle transportation and energy deposition calculations for a given system model. However, NISCSAT can be easily integrated with any external program or code to calculate required parameters for the problem.

4.2.1 Geometry

The basic structure in the NISCSAT is a ”node” which represents the fundamental structure of a semiconductor memory where soft errors can occur and change the stored data. A node can have multiple homogenized ”layers” to represent the actual system in which insulation and metal layers exist to form the node. In order to make the soft error calculations there has to be at least one layer in the node that must be defined as the sensitive volume of the node. There can be more than one sensitive volume in a single node. Di- mension and location of the sensitive volumes can also be calculated or imported from external programs into the NISCSAT. A node definition can be put in a 3D array and form a ”memory”. A memory also can be put in an array and form a matrix of memories. There can be more than one memory definition that represents different memory architectures. Finally, memories are placed in a ”world” that represents the entire system and there can be only one world definition. Particle transport is performed only 39

Figure 4.2. NISCSAT block diagram in the world and any particle leaving the world is rejected. Energy depositions of the particles are recorded if only the deposition takes place in a sensitive volume, otherwise the particles continues their history with modified path and energy information. Figure 4.3 illustrates the geometry components of the NISCSAT. Layers, node, memory, and world are defined as ”unit” in the NISCSAT. Each node can have layers, each memory can have an array of nodes, and the world can have an array of multiple memory units. There is one more unit definition which is called a ”moderator” and represents auxiliary region definitions in the world. There can be multiple moderator units in the world and they are intended to be used in moderation of the source particles. Each individual unit can be transformed by translations or rotations respect to their mother units.

4.2.2 Material, Temperature, and Electromagnetic Field

Materials are deﬁned as single elemental data or as compounds in the NISCSAT. Some of the built in material names are as follows; Vacuum, Air, Silicon, NatBoron, Enr- Boron, NatBPSG, EnrBPSG, NatB2O3, EnrB2O3, NatB6O, EnrB6O, NatBN, EnrBN, 40

Figure 4.3. NISCSAT geometry structure

P2O5, SiO2, Si3N4, TiN, Aluminum, Tungsten, Titanium, Iron, Lead, Polyethylene, Plexiglass, Pyrex, Graphite, Concrete, Water, WaterVapor, EarthCrust. ”Nat” and ”Enr” represents enrichment of 10B in boron and stands for ”Natural” and ”Enriched”. Each unit in the NISCSAT must have a default material that can be filled with other daughter unit definitions and the remaining volume in the unit, if any, will be filled with the default material. Material definition in the world unit must also have a temperature value which must be provided after the material name. This temperature will be applied for the entire system materials. If the world material is chosen as air, specific humidity also must be given after the temperature value. World, memory, and node units can have their own electric, magnetic, or electromagnetic field values. Temperature, humidity, and electromagnetic field properties will enable simulation of different environment conditions for the NISC model.

4.2.3 Physics

NISCSAT uses predeﬁned physics lists that are available in the Geant4 to ensure con- sistency and reliability. Each physics list has its own pre-deﬁned values or models for hadronic, electromagnetic, decay, transportation, and optical processes. NISCSAT uses ”QGSP BIC HP”1 as the default physics list in which the quark gluon string model is applied for high-energy interactions of protons, neutrons, pions, kaons and nuclei. The high-energy interaction creates an exited nucleus, which is passed to the pre-compound model for the nuclear de-excitation calculations. Binary cascade is used for primary protons and neutrons with energies below 10 GeV, thus production of secondary parti-

1this is one of the standard physics list in the Geant4 41 cles produced in interactions of protons and neutrons with nuclei are described better. It also uses the binary light ion cascade to model inelastic interaction of ions up to few GeV/nucleon with matter and the data driven high precision neutron package to transport neutrons below 20 MeV down to thermal energies.

4.2.4 Source

NISCSAT supports single and multiple source particle definitions and particle definitions are inherited from the Geant4. Source particles can be ordinary particles, such as neutrons, electrons, protons, and gammas or nuclei, such as deuterons, alphas, and heavy ions. The particle energy and probability distributions can be read from a file or sampled via built in distributions like Gaussian, Maxwellian, or Watt spectrum in the NISCSAT. In addition, NISCSAT also supports external libraries such as Cosmic- Ray Shower Library (CRY) [69] for sampling cosmic rays and Fission Library [70] for sampling neutrons and gammas from the spontaneous fission sources.

4.2.5 Soft Error Probability

NISCSAT records the history of the particles that deposit ionizing energy only in the deﬁned sensitive volumes in given nodes of the model. If a particle deposits ionizing energy in a sensitive volume exceeding the required critical energy of that node, then this event is recorded as a soft error for that node and relevant memory that contains the node. Critical charge, size, and location of the sensitive volumes are properties of the memory architectures and must be calculated via device simulations. However, device simulations are not coupled with the NISCSAT simulations and reasonable values have been assigned for the critical charge value, dimensions, and location of the sensitive volumes in the simulations. If any layer of the node is set as sensitive volume, then the entire layer volume is treated as the sensitive volume. Generally, critical charge is proportional to the gate and interconnect capacitances, operating voltage, and sensitive volume thickness. The relation can be correlated with the sensitive volume thickness as [71–73]; 2 2 QCRIT = 0.0023 (pC/µm ) L (4.1) where QCRIT is the critical charge and L is the sensitive volume thickness. 42

After an event that causes a soft error, NISCSAT saves following data into soft error history:

• origin, type, and energy of the incoming source particle,

• origin, type, and energy of the secondary particle, if any, which deposited ionizing energy and cause the soft error,

• energy deposition time and range of the particle in the sensitive volume,

• layer, node, and memory in which the soft error occurred, and

• 2D soft error map of each layer for each memory indicating the nodes having soft errors.

4.3 NISC Node Model and NISCSAT Veriﬁcation

Problem implementation in Geant4 is not straightforward and it is easy to make mis- takes while defining the physical processes and tallying. In order to test the important model parameters of soft error probability calculations, particle ranges are calculated via NISCSAT and compared with reference values in common memory materials. In addition to range calculations, trajectories of the α and 7Li particles from the 10B (n, α) 7Li reaction and angular distributions of these particles are also calculated for verification of the NISCSAT. The range of the α and 7Li particles in common layer materials used in the semiconductor memory device nodes were calculated and results are tabulated in Table 4.1 and Table 4.2 with NISCSAT and Nuclenonica [48]. Nucleonica is a new nuclear science web portal from the European Commissions Joint Research Centre. The portal provides a customizable, integrated environment and collaboration platform. Nucleon- ica provides software as a service on the web rather than through installed software and consists of application modules including radioactive decay, dosimetry & shielding, fission yields, range & stopping power, reactor irradiation and nuclide depletion, transport and packaging, library creation for spectroscopy, nuclide mixtures, etc. Stopping and Range of Ions in Matter (SRIM) [49], a commonly used Monte Carlo program for calculating stopping power and range of the ions in the materials by J. F. Ziegler, is used for the range calculations for Nucleonica. 43

The range results were in good agreement with each other and verified the reliability of the NISCSAT model for the range calculations of the α and 7Li particles in the device materials. Energy deposition is the primary concern of the NISC design and correct calculation of the particle ranges for the device materials is one of the most important parameters for calculation of the energy depositions. As demonstrated in Table 4.1 and Table 4.2, the 7Li particles travel approximately half the distance of the α particles for the same materials because of their lower energy and heavier mass. This information shows that the layer thicknesses of Ti, and W between the BPSG layer and silicon layer should be minimized or avoided if possible. More importantly, if the distance between the node sensitive volume (SV) in the silicon and BPSG layer is larger than 2.5 µm, no lithium particles will reach the SV and hence the detection efficiency of the NISC will be reduced 50%. The α and 7Li particle trajectories were calculated with NISCSAT and are shown in Fig. 4.4 and Fig. 4.5. As seen from the figures, the particles travel exactly in the opposite directions from each other and direction of the particles are independent from the incoming neutron direction, as expected.

Table 4.1. Calculated α ranges in the semiconductor device materials via NISCSAT NISCSAT Energy Particle Range (µm ) (MeV) Si B SiO2 Si3N4 Ti TiN Al W 1.472 5.43 3.83 4.36 3.34 3.46 2.53 5.00 2.72 α 1.776 6.64 4.72 5.30 4.09 4.31 3.07 6.04 3.16 0.840 2.55 1.85 2.23 1.64 1.99 1.35 2.47 1.76 7Li 1.014 2.91 2.08 2.51 1.86 2.22 1.50 2.81 1.92

Table 4.2. Calculated α ranges in the other semiconductor device materials via Nucleonica Nucleonica Energy Particle Range (µm ) (MeV) Si B SiO2 Si3N4 Ti TiN Al W 1.470 5.64 3.61 4.43 3.38 3.25 2.47 4.74 1.98 α 1.776 6.46 4.45 5.38 4.13 4.19 3.00 5.81 2.39 0.840 2.51 1.88 2.34 1.76 1.71 1.29 2.32 1.01 7Li 1.014 2.87 2.11 2.64 1.99 1.93 1.45 2.66 1.17 44

Figure 4.4. Calculated α and 7Li trajectories in x-y plane via NISCSAT

Figure 4.5. Calculated α and 7Li directions in x-y plane via NISCSAT Chapter 5

Soft Error Analysis of the NISC

Soft error probabilities of various NISC models were investigated via NISCSAT simulations and findings are presented in this chapter. Soft error dependency of the NISC to incoming neutron energy spectrum was examined for different neutron sources. Mod- eration of the neutron source to enhance the soft errors was also studied with different moderators that were placed in front of the NISC. Responses of the NISC to electric, magnetic, and electromagnetic fields were also examined by applying the external field to NISC models. Environmental conditions such as temperature and specific humidity of the air were simulated to investigate the effects on the NISC soft error probabilities. The directional neutron detection capability of the NISC was explored with mono-directional and point neutron sources. Soft error probabilities of the NISC with complex node structures, including additional node layers between the silicon substrate and BPSG layer were also studied with different models. Finally, uncertainties associated with the NISCSAT simulations were examined.

5.1 NISC Simulation Model and Investigation of the Model Parameters

The semiconductor device node for the NISC design was chosen to be as simple as possible in order to focus on the effects of 10B (n, α) 7Li reaction. A cross-sectional view of the memory node model is illustrated in Fig. 5.1. All the other layers and materials that can be found in an actual device structure were ignored in the model. The 46 memory node represents the basic data storage unit in the semiconductor memory, a MOSFET/CMOS transistor. Commercially available semiconductor memories, SRAM, DRAM, and Flash memory, have different node architectures and different number of nodes to store a single bit of data. SRAMs can have six transistors while DRAM and Flash memory requires a single transistor to store or control the storage of the data. However, all of the architectures have a generic structure that can be simpliﬁed as a lumped silicon region and BPSG region that is placed on top of the silicon region. The coupling of the nuclear simulations with device simulations were represented by only supplying the required critical charge of the device and sensitive volume information, such as dimensions and location, to nuclear simulations. The NISC node model was used to construct a 2D-array model that represents a memory structure, shown in Fig. 5.2. Stacking of the memory (3D-array of nodes) was modeled by adding multiple layers of the 2D-arrays. Even if the NISCSAT is capable of simulating virtually any dimension of the arrays, depending on the physical memory of the computer, the 3D-array structure is limited to maximum conﬁguration of 1000

× 10 × 1000 (number of the nodes in each axis, Nx × Ny × Nz, where the y-axis represents the number of memory layers or depth of the model) in this study due to available computational resources.

Figure 5.1. NISC node model

The detection efﬁciency of the NISC is directly related to the soft errors in the memory since each error can be counted as a single neutron strike. Therefore, soft error probability represents the detection efﬁciency of the NISC. Soft error probability calculations were performed via the NISCSAT with single and array of the nodes to investigate the 47

Figure 5.2. NISC array model device scaling by using different node dimensions in the model. Mono-energetic, mono- directional, thermal and fast neutrons are used as the incident radiation sources. The soft error contribution due to the BPSG layer was also investigated with different 10B contents and the simulation results are presented in this chapter. The soft error modeling of the NISC and the simulation results for the device scaling and 10B content are also published in the literature by the author [74]. In this chapter, SER nuclear simulations with simple semiconductor memory device node model for different device sizes and 10B contents in the BPSG layers are presented.

5.1.1 Boron Content in the BPSG Layer

The NISC achieves neutron detection by introducing 10B-enriched BPSG layer to available semiconductor production technology. Introducing 10B enables neutron interactions that produce α and 7Li particles and hence the soft errors in the memory. Soft error probabilities depending on the deposited 10B amount are investigated in this section. Candi- date materials for 10B deposition at the BPSG layer are listed in Table 5.1. Simulations were performed with a single node model whose dimensions are chosen as 5 µm × ( 2 µm + 1 µm ) × 5 µm. In order to accelerate convergence and precision of the Monte Carlo calculations a mono-energetic, mono-directional neutron source was deﬁned on the surface of the memory node. The entire silicon region was assumed as the sensitive volume of the node. A thermal, mono-energetic neutron source, En = 0.0253 eV, was speciﬁcally used to observe the effects of 10B content. All the simulation parameters were exactly the same for each simulation except the BPSG material. 48

Table 5.1. Selected materials for the BPSG layer in the NISC model Density Composition 10B Content Material (g/cm3) (%) (%) 10B (20) BNAT 20 11B (80) 2.35 10B (90) BENR 90 11B (10) NAT B2 O3 (15) NAT BPSG P2O5 (10) 0.9 SiO2 (75) 2.36 ENR B2 O3 (15) ENR BPSG P2O5 (10) 4.0 SiO2 (75) BNNAT BNATN (100) 9 3.49 BNENR BENRN (100) 39 NAT NAT B2O3 B2 O3 (100) 6 ENR 2.55 ENR B2O3 B2 O3 (100) 27

Simulation results demonstrated that the SER is proportional to 10B content of the BPSG region since the reaction probability increases with 10B content as shown in Fig. 5.3 and Table 5.2. The SER is given per incoming neutron ﬂux on the node surface in order to let the results be scaled for any other neutron ﬂux values. Figure 5.3 also includes the SER of 10B-enriched pure boron, illustrated with a solid black line, besides the selected materials so that 10B enrichment in the selected materials and the SER can be related according to reference enrichments of pure boron. The selected materials show similar trend to enriched boron results with minor differences according to their 10B enrichments. These differences come from the fact that the range and energy deposition of the charged particles and neutron cross-sections are dependent on the material compositions. In Table 5.2, the last column represents the soft error probabilities that are normalized to the results of BPSG and 10B content of the materials. As other elements are added to boron to form a compound the probability of soft error inducing reaction products, other than α and 7Li, increases. In addition, as 10B increases in the material, neutron absorption probability increases, hence the self-shielding of the neutrons also increases. 49

Figure 5.3. Soft error variation with 10B content in the BPSG layer

Table 5.2. Soft error probability calculation results for 10B content in the BPSG layer. Simula- tions were performed for 5 µm × ( 2 µm + 1 µm ) × 5 µm node with QCRIT= 2.3 fC and thermal neutron source Soft Errors Probability BPSG 10B Content Source Material (%) Particles Actual (× 109) Normalizeda (%) per ﬂux (n/cm2s)

BPSGNAT 1.0 α (56), 7Li (44) 0.14 1.00 BPSGENR 4.0 α (61), 7Li (39) 0.47 0.84 NAT 7 B2O3 6.0 α (60), Li (40) 0.79 0.94 ENR 7 B2O3 27.0 α (61), Li (39) 3.30 0.87 BNNAT 9.0 α (63), 7Li (36), p (1) 1.09 0.87 BNENR 39.0 α (65), 7Li (34), p (1) 4.83 0.88 BNAT 20.0 α (65), 7Li (35) 2.10 0.75 BENR 90.0 α (65), 7Li (35) 8.43 0.67 a Normalized with respect to BPSGNAT per unit 10B content 50

5.1.2 Device Scaling

In order to ﬁnd a design space for the BPSG layer thickness and silicon substrate region for the NISC, a single node was modeled with varying BPSG and silicon layer thicknesses. The change in the BPSG thickness, tB, is associated with the charged particle production and change in the silicon thickness, tS, is associated with the critical charge of the node. NISCSAT simulations were performed with node dimensions of 5 µm x ( ENR tB+ tS ) x 5 µm and B was chosen as the BPSG material. Mono-directional neutrons were sampled uniformly over the node surface at thermal energy. The entire silicon layer was assumed to be the sensitive volume. During the simulations, one of the parameters (tB or tS) was kept constant to examine the effect of the other on the soft error probability.

Figure 5.4. Device scaling effects on the NISC node

Figure 5.4 shows the simulation results for device scaling. As the BPSG thickness increases, soft errors also increase since the neutron interaction probability increases with 10B content of the BPSG. This trend continues up to a point where the BPSG thickness exceeds the range of both α and 7Li particles in the BPSG. Further increase in the BPSG thickness beyond this point causes neutron self-shielding in the BPSG layer 51 to dominate and decreases the soft error probability. Dominant reaction products, shown in Eq. 1.9, have ranges of 3.83 µm and 1.85 µm in the boron for α and 7Li as given in the Table 4.1. If the thickness of the BPSG layer exceeds the 1.85 µm, effective charge depositions from the 7Li ions reach a saturation point and will not have any contribution to the total SER. This saturation point also exists for the α particles, but after 3.83 µm. The second and more important factor is the neutron mean free path which is 19 µm for boron (90% enriched with 10B) as mentioned in Chapter2. Any further increase in ENR tB more than 19 µm for the model with B , does not enhance the SER even if the 10B (n, α) 7Li reactions can be assumed to reach the saturation level. The reason is, after the ﬁrst 4.72 µm of the BPSG layer, the reaction products are not able to reach the silicon. On the contrary, the rest of the region will react as a neutron shield and absorb the incoming neutrons. This situation can be represented by following equation;

−tB /Σt IN = I0 e (5.1) where IN is the number of the neutrons at the depth of tB in the boron region, I0 is the incoming number of neutrons at the surface of the model, and Σt is the macroscopic neutron total cross-section. The BPSG thickness therefore should not be decreased as the node dimension decreases in order to get higher efﬁciency. The sensitive volume of the node is assigned as the entire silicon layer in the simulations; hence as the silicon thickness increases (L as in Eq. 4.1), the soft error probability decreases due to the fact that the required critical charge increases quadratically as given in Eq. 4.1. Since the entire silicon region is assumed to be the sensitive volume, only thickness is related with the critical charge whereas the surface area of the sensitive volume was kept constant for only demonstration purposes and does not reﬂect reality. Additional node simulation results are given in AppendixB, Table B.1. 52

5.1.3 Critical Charge

The concept of critical charge is problematic for two reasons. The first reason is, like charge transport, the physical response of any circuit is time dependent to some degree. The second reason is, circuits are composed of multiple transistors and hence there are many circuit nodes, which may or may not be sensitive to charge collection depending on the state of the circuit at the time of the event. In this part of the simulations, effects of the semiconductor device node’s critical charge on the SER were investigated. Re- quired critical charge depends on the device architecture of the semiconductor memory. Solid-state device simulations are needed to calculate the critical charge of the designed models. However, in this stage these simulations were not coupled with the NISCSAT simulations and critical charge value was calculated from Eq. 4.1 in the simulations. Figure 5.5 shows charged particle energy distributions in the sensitive volume from the simulation results of a 5 µm x ( 2 µm + 1 µm) x 5 µm node with BENR as the BPSG material for thermal neutrons. The mean charge generations of both α and 7Li particles are around 15 fC that is much more than the required critical charge of 2.3 fC for the node configuration. This indicates that the node model with lumped silicon region effectively represents the actual node structure since the critical charge is generated only in a path length 0.2 µm in the sensitive volume. However, surface area of the sensitive volume would be much smaller in an actual memory node and the probability of the charged particles to pass through the sensitive volume would be smaller and result in lower detection efficiencies. Design space of the NISC can be represented as shown in Fig. 5.6 in which actual charged particle energy deposition (EDEP) history and required critical charge are compared. Both α and 7Li particles deposit much of their energies at the beginning of the history and generate more than enough charge than the critical charge. Figure 5.6 also shows that NISC design is not limited with the thickness of the sensitive volume, but by the surface area. For the assumed critical charge relation and linear energy transfer, it would require a sensitive volume thickness of 5.0 µm to require more critical charge than the maximum charge that can be generated in the sensitive volume. Moreover, the dimensions are already limited with the ranges of the particles that are already smaller than 5.0 µm. Since 7Li has a lower range in the materials and effectively generates charges if the BPSG layer located less than 2.0 µm apart from the sensitive volume of the node, 7Li is the key particle for the NISC design. 53

Figure 5.5. Normalized distributions of α, 7Li, and total charge depositions in the NISC model

Figure 5.6. α and 7Li Energy depositions in the NISC model 54

5.1.4 Neutron Energy

The SER dependency on the incoming neutron energy is also proven to be a major component since the reaction cross-section drastically decreases for the fast neutrons. In addition, the contributions of reactions that can induce soft errors other than 10B (n, α) 7Li reaction generally have a threshold energy requirement on the incoming neutrons around 4 MeV. Therefore, for the fast neutrons below 4 MeV the SER probability is almost zero, and for the neutrons above 4 MeV the SER can be ignored compared to the thermal neutron results. Soft error probabilities for a single node with BPSGNAT and BENR are given in Table 5.3 for different neutron energies. A mono-energetic, mono- directional neutron source was sampled uniformly over the node surface area in the simulations. Simulations showed that the soft error probability strongly depends on the energy of the incoming neutrons since 10B (n, α) 7Li reaction cross-section is higher in the thermal energies, as expected.

Table 5.3. Simulation results for neutron energy dependency of the NISC. Simulations were performed with 5 µm × ( 2 µm + 1 µm ) × 5 µm node, QCRIT= 2.3 fC Soft Errors Neutron BPSG Source Probability (× 109) Energy Material Particles (%) per ﬂux (n/cm2s)

BENR α (65), 7Li (35) 8.43 0.0253 eV BPSGNAT α (56), 7Li (44) 0.14 BENR d (100) 0.003 2.0 MeV BPSGNAT - - BENR 10B (50), 25Mg (50) 0.005 14.0 MeV BPSGNAT - - BENR α (34), 28Si (33), 3He (33) 0.008 50.0 MeV BPSGNAT 28Si (100) 0.003

A mono-energetic neutron source approximation is not realistic for neutron detection since most of the real neutron sources have an energy distribution rather than a single energy. In addition, neutrons will scatter and slow down in the surrounding materials untill they reach the detection surface and have an energy spectrum. Therefore, energy spectrum dependent simulations were also performed to investigate the effect of neutron 55 energy spectra on the soft error probability of the NISC. Three different energy spectra were used to model the neutron sources: Watt spectrum, Maxwell distribution, and Gaussian distribution.

• The energy spectra of the inherent spontaneous ﬁssion sources is represented by the Watt spectrum as;

0 √ W (a, b, E0) = Ce−aE sinh( bE0) (5.2)

where C is the normalization constant, a and b are the spectrum parameters, and E0 is the secondary neutron energy. Average number of neutrons per ﬁssion, Watt parameters, and average neutron energy for the selected spontaneous ﬁssion sources are given in Table 1.2.

• The Maxwell energy distribution is represented as;

0 √ M(a, E0) = Ce−E /a E0 (5.3)

where C is the normalization constant, a is the spectrum parameter, and E0 is the secondary neutron energy.

• The Gaussian energy distribution is represented as;

0 2 G(a, b, E0) = Ce−((E −b)/a) (5.4)

where C is the normalization constant, E0 is the secondary neutron energy, b is the mean energy, and a is the width of the spectrum.

The shape of the each distribution having the same mean energy of 1 MeV is illustrated in Fig. 5.7. Simulations were performed with mono-directional, uniformly distributed neutron sources for 100x1x100 array of 5 µm × ( 2 µm + 1 µm ) × 5 µm nodes with BENR and results are given in Table 5.4. All the simulations were performed with a spectrum width of the mean energy in order to span the entire energy range to account for the slowing down, except for the spontaneous ﬁssion sources whose distributions were already known. Using energy spectrum instead of mono-energy for the neutron energy 56

Figure 5.7. Comparison of energy distribution models used in the simulations. Mean energy for each distribution is kept constant at E¯ = 1.0 MeV increases the soft error probabilities since they all have lower energies in the tail region. The fact that the 10B neutron absorption cross-section changes with 1/v in the thermal region means that the soft error probability increases almost twice as much by using energy distributions instead of single energy. The fast neutron soft error probabilities are still much lower than the thermal neutron sources, and using an energy spectrum model instead of mono-energetic neutrons increases the soft error probability. 57

Table 5.4. Soft error simulation results with various neutron source models and BENR.Simulations were performed with 100 × 1 × 100 array of 5 µm × ( 2 µm + 1 µm ) × 5 µm nodes Spectrum Node Soft Errora Source E¯ Parameters Probability (× 109) Name Name (a,b) per ﬂux (n/cm2s)

Thermal-C 0.0253 eV Constant N/A 9.490 Thermal-M1 0.0253 eV Maxwell 0.016865,- 18.68 Thermal-M2 0.0500 eV Maxwell 0.033331,- 16.16 Thermal-M3 0.2530 eV Maxwell 0.16865,- 10.53 Thermal-M4 0.5000 eV Maxwell 0.33331,- 8.475 Thermal-M5 2.5300 eV Maxwell 1.68650,- 5.035 Thermal-M6 5.0000 eV Maxwell 3.33310,- 3.877 Thermal-G1 0.0253 eV Gaussian 0.01012,0.0253 10.44 Thermal-G2 0.0500 eV Gaussian 0.0200,0.0500 7.950 Thermal-G3 0.2530 eV Gaussian 0.10120,0.253 3.783 Thermal-G4 0.5000 eV Gaussian 0.200,0.500 2.850 Thermal-G3 2.5300 eV Gaussian 1.0120,2.53 1.477 Thermal-G4 5.0000 eV Gaussian 2.00,5.00 1.265 Fast-C 2.000 MeV Constant N/A 0.010 SF-235U 1.890 MeV Watt 1.29 , 4.85 0.017 SF-238U 1.688 MeV Watt 1.54 , 6.81 0.022 SF-239Pu 2.073 MeV Watt 1.13 , 3.80 0.012 SF-240Pu 1.933 MeV Watt 1.26 , 4.69 0.015 SF-241Pu 2.000 MeV Watt 1.19 , 4.15 0.010 SF-252Cf 2.130 MeV Watt 0.85 , 1.03 0.017 Fast-M1 1.000 MeV Maxwell 0.66,- 0.015 Fast-M2 2.000 MeV Maxwell 1.33,- 0.017 Fast-M3 5.000 MeV Maxwell 3.33,- 0.022 Fast-M4 14.00 MeV Maxwell 9.33,- 0.015 Fast-G1 1.000 MeV Maxwell 0.40,1.0 0.380 Fast-G2 2.000 MeV Maxwell 0.80,2.0 0.400 Fast-G3 5.000 MeV Maxwell 2.00,5.0 0.343 Fast-G4 14.00 MeV Maxwell 5.60,14.0 0.398 a Total probability divided by the total node number 58

5.1.5 Array Structure

In addition to single node simulations, device scaling effects were also studied with node arrays that represent actual memory structures more realistically. Soft error probability calculations in the single node only depend on the charged particle generations and energy depositions in that particular node. On the other hand, in the case of the array structure, soft error probability also depends on the charged particles that are generated in another node and deposit energy not in the same node, but rather in the sensitive volume of the neighbor nodes. This phenomenon is called multiple bit upset (MBU). Soft error probabilities for 2D and 3D arrays for a node with dimensions of 5 µm × ( 2 µm + 1 µm ) × 5 µm are given in Fig. 5.8 and Table 5.5. Simulations were performed with BENR for uniformly sampled, mono-directional thermal neutrons. In Fig. 5.8, soft error probabilities are normalized to soft error probability of the single node. As it can be concluded from the Fig. 5.8, the greatest increase in the node soft error probability was achieved with the 2 × 2 × 2 array configuration, and the increase in the node soft error probability gradually decreased with addition of more rows, columns, and layers. The main reason for this trend is the range of α and 7Li particles and the selected node dimensions which are comparable with each other. Both particles do not contribute to the soft error probability beyond the second node that they can reach in any direction. In the node calculations, MBUs were ignored since the produced particles that leave the node were rejected and did not contribute the SER in another node. However, in the 2D-array of the nodes, which is the actual case in the physical memory, MBUs were observed and the node SER probability increased almost 30% mainly from the alpha particles since they have higher range in the silicon compared to 7Li. The SER contribution from the alpha particles starts to dominate with increasing number of layers as given in Table 5.5. In the single node case, escape probability of one of the particles is high since they have opposite directions and this probability reduces when there is an array of the nodes instead of a single node. In case of the 3D-array of the nodes, the SER probability continues to increase due to MBUs and contribution of both particles, while the major increase comes from the increase in the neutron reaction rates due to increase in the 10B content at discrete layers. Instead of using one large BPSG layer, which will cause self-shielding, using multi-layer memory or multi-memory configuration increases the SER probability in the overall system. NISC intrinsic efficiency is directly related to total SER of the system and can be defined as the ratio of the total 59

Figure 5.8. Soft error simulation results for 3D-array NISC models soft errors in the NISC to total number of incoming neutrons entering to the NISC. As- suming that the every soft error will be counted in the NISC, the intrinsic efficiency of the NISC increases linearly as more memory layers added to the NISC. The last column in Table 5.5 shows the intrinsic efficiency enhancement for the simulation model. Addi- tion of each layer increases the intrinsic efficiency about 5%. On the other hand, adding more layer also increases the neutron self-shielding which results in a saturation in the intrinsic efficiency enhancement as shown in Fig. 5.9. Another set of simulations was also performed to investigate the MBUs in the NISC. In these simulations, while dimensions of the node had been changed, the total surface area or the volume of the memory was kept constant. The objective of these simulations was to investigate the node and memory soft error probabilities for a given space. Simulation results with a constant memory surface area of 500 × 500 µm2 are given in AppendixB, Table 5.6 and extended simulation results are given in Table B.2. As seen from Table 5.6, by decreasing node surface area, more nodes are replaced for the same memory area and the node soft error probability decreases due to decrease in the reaction probability for a small area. However, total soft error probability, or memory soft error probability increases with the number of the nodes in the memory. Decrease 60

Table 5.5. 3D-array NISC simulation results with BENR and thermal neutrons for 5 µm × ( 2 µm + 1 µm ) × 5 µm node, QCRIT= 2.3 fC Soft Errors Array Probability (× 109) Intrinsic Source Config. per flux (n/cm2s) Efficiency (%) Particles (%) Total Nodea 100x1x100 α (66), 7Li (34) 1.17x105 11.73 4.69 100x2x100 α (69), 7Li (31) 2.89x105 14.47 11.57 100x5x100 α (72), 7Li (28) 7.04x105 14.07 28.14 100x10x100 α (73), 7Li (27) 1.17x106 11.75 47.00 1Kx1x1K α (66), 7Li (34) 1.17x107 11.72 4.68 1Kx2x1K α (70), 7Li (30) 2.90x107 14.47 11.57 1Kx5x1K α (72), 7Li (28) 7.18x107 14.36 28.72 1Kx10x1K α (72), 7Li (28) 1.19x108 11.87 47.48 a Total probability divided by the total node number

Figure 5.9. NISC intrinsic efﬁciency curve for 3D-array NISC models in the silicon layer thickness results in a higher soft error probability due to lowered critical charge requirement in the node. However, with further decrease in the silicon layer 61 thickness, soft error probability starts to decrease since α and 7Li do not have enough paths in the sensitive volume to deposit energy. A similar trend was observed in the simulations with a constant memory volume of 500 ×15 × 500 µm3. By introducing additional memory layers, the node soft error probability decreases due to self-shielding of the neutrons. However, even if the node SER probability decreases with memory layers, total SER increases in the system that the NISC detection efﬁciency increases. Simulation results are given in Table 5.7 and extended simulation results are given in AppendixB, Table B.3.

Table 5.6. NISC array simulation results for a given fixed memory surface area of 500 × 500 µm2 with BENR Soft Error Node Dim. Q Array Probability (× 109) Wx(t +t )xW CRIT B S (fC) Config. per flux (n/cm2s) (µm) Total Nodea 1 × ( 2 + 1 ) × 1 2.300 500 × 1 × 500 1.20x105 0.480 2 × ( 2 + 1 ) × 2 2.300 250 × 1 × 250 1.17x105 1.872 5 × ( 2 + 1 ) × 5 2.300 100 × 1 × 100 1.11x105 11.12 1 × ( 2 + 0.60 ) × 1 0.828 500 × 1 × 500 1.23x105 0.494 2 × ( 2 + 0.60 ) × 2 0.828 250 × 1 × 250 1.15x105 1.835 5 × ( 2 + 0.60 ) × 5 0.828 100 × 1 × 100 1.14x105 11.45 1 × ( 2 + 0.35 ) × 1 0.282 500 × 1 × 500 1.27x105 0.507 2 × ( 2 + 0.35 ) × 2 0.282 250 × 1 × 250 1.17x105 1.866 5 × ( 2 + 0.35 ) × 5 0.282 100 × 1 × 100 1.14x105 11.42 1 × ( 2 + 0.060 ) × 1 0.0083 500 × 1 × 500 1.30x105 0.520 2 × ( 2 + 0.060 ) × 2 0.0083 250 × 1 × 250 1.21x105 1.932 5 × ( 2 + 0.060 ) × 5 0.0083 100 × 1 × 100 1.16x105 11.63 1 × ( 2 + 0.035 ) × 1 0.0028 500 × 1 × 500 1.27x105 0.508 2 × ( 2 + 0.035 ) × 2 0.0028 250 × 1 × 250 1.22x105 1.948 5 × ( 2 + 0.035 ) × 5 0.0028 100 × 1 × 100 1.16x105 11.61 a Total probability divided by the total node number 62

Table 5.7. NISC array simulation results for a given fixed memory volume of 500 × 15 × 500 µm3 with BENR Soft Error Node Dim. Q Array Probability (× 109) Wx(t +t )xW CRIT B S (fC) Config. per flux (n/cm2s) (µm) Total Nodea 1 × ( 2 + 1 ) × 1 2.300 500 × 5 × 500 7.11 × 105 0.568 2 × ( 2 + 1 ) × 2 2.300 250 × 5 × 250 6.90 × 105 2.207 5 × ( 2 + 1 ) × 5 2.300 100 × 5 × 100 6.74 × 105 13.47 1 × ( 1.90 + 0.60 ) × 1 0.828 500 × 6 × 500 8.14 × 105 0.542 2 × ( 1.90 + 0.60 ) × 2 0.828 250 × 6 × 250 7.96 × 105 2.123 5 × ( 1.90 + 0.60 ) × 5 0.828 100 × 6 × 100 7.80 × 105 13.00 1 × ( 2.15 + 0.35 ) × 1 0.282 500 × 6 × 500 8.55 × 105 0.570 2 × ( 2.15 + 0.35 ) × 2 0.282 250 × 6 × 250 8.30 × 105 2.215 5 × ( 2.15 + 0.35 ) × 5 0.282 100 × 6 × 100 8.12 × 105 13.53 1 × ( 1.440 + 0.060 ) × 1 0.0083 500 × 10 × 500 1.08 × 106 0.433 2 × ( 1.440 + 0.060 ) × 2 0.0083 250 × 10 × 250 1.07 × 106 1.707 5 × ( 1.440 + 0.060 ) × 5 0.0083 100 × 10 × 100 1.05 × 106 10.51 1 × ( 1.465 + 0.035 ) × 1 0.0028 500 × 10 × 500 1.08 × 106 0.432 2 × ( 1.465 + 0.035 ) × 2 0.0028 250 × 10 × 250 1.06 × 106 1.704 5 × ( 1.465 + 0.035 ) × 5 0.0028 100 × 10 × 100 1.05 × 106 10.52 a Total probability divided by the total node number 63

5.1.6 Sensitive Volume

Soft error probability strongly depends on the sensitive volume dimensions and its location in the node structure. In order to investigate the sensitive volume dependency of the soft errors, a new model, shown in Fig. 5.10, was constructed. Simulation results are given in Table 5.8. Since the charged particles already have enough energy to generate excess charges more than the critical charge, the soft error probability simply proportional to the sensitive volume dimensions. As the dimensions increase, the charged particles generate more electron-hole pairs in their paths and the soft error probability increases.

Figure 5.10. NISC node model for the sensitive volume calculations

Table 5.8. Sensitive volume dependent NISC simulation results with BENR. Simulations were performed with 100 × 1 × 100 array of 5 × ( 2 + tSV + 1 ) × 5 µm nodes and thermal neutrons Node Soft Errors t SV Source Probability (× 109) (µm) Particles (%) per ﬂux (n/cm2s)

0.1 α (67), 7Li (33) 0.015 0.2 α (65), 7Li (35) 0.043 0.4 α (78), 7Li (22) 0.135 0.6 α (69), 7Li (31) 0.318 1.0 α (72), 7Li (28) 0.795 64

5.1.7 Intermediate Node Layers

In a real memory node, there are insulation and metal layers to form the contact points and create word line and bit lines in the memory. NISC aims to introduce 10B-enriched BPSG layers as close as possible to node depletion region, or so called sensitive volume. A simple model, shown in Fig. 5.11, was constructed to investigate effects of additional layers between the BPSG layer and sensitive volume of the node. Intermediate layer material was chosen as SiO2 to represent a lumped region that contains all the insulation and metal layers. Thickness of the intermediate layer, tL was changed in the simulations to investigate how far the BPSG layer can be located from the sensitive volume of the node.

Figure 5.11. NISC node model with an intermediate layer

Simulations were performed with mono-energetic, mono-directional thermal neutron source and 2D/3D-array of 5 µm × ( 2 µm + 1 µm ) × 5 µm nodes with BENR. Table 5.9 shows 2D-array simulation results. As the thickness of the intermediate layer increases, the soft error probability of the NISC decreases. This behavior was expected since the majority of the generated particles in the BPSG layer cannot reach the sensitive volume as the thickness of the intermediate layer increases. After 2 µm, 7Li particles, whose range in SiO2 is ≈ 2.4 µm, do not contribute to soft errors while some of the alpha particles still can reach the sensitive volume. After 4 µm, α particles, whose range in

SiO2 is ≈ 4.4 µm, also cannot reach the sensitive volume and soft error probability goes to zero. On the other hand, 3D-array simulation results show that if there are multiple layers of 2D-array, the soft error probability never goes to zero but approaches to a limit value as listed in Table 5.10. The reason for this difference is that there was not any material deﬁned between the memory layers in the simulation model and hence both α and 7Li particles were causing MBUs when they were passing through multiple memory layers. 65

Table 5.9. NISC simulation results for 100 × 1 × 100 array with an intermediate layer between the BPSG and silicon layers. Simulations were performed with 5 µm × ( 2 µm + tL + 1 µm ) × 5 µm node and thermal neutrons Node Soft Errors t L Source Probability (× 109) (µm) Particles (%) per ﬂux (n/cm2s)

0.0 α (67), 7Li (33) 9.77 0.1 α (67), 7Li (33) 9.32 0.2 α (69), 7Li (31) 9.30 0.5 α (70), 7Li (30) 8.03 1.0 α (78), 7Li (22) 5.74 1.5 α (90), 7Li (10) 3.52 2.0 α (99), 7Li (1) 1.99 3.0 α (100) 0.45 4.0 α (100) 0.02 5.0 - -

Table 5.10. NISC simulation results for 100 × 5 × 100 array with an intermediate layer between the BPSG and silicon layers. Simulations were performed with 5 µm × ( 2 µm + tL + 1 µm ) × 5 µm node and thermal neutrons Node Soft Errors t L Source Probability (× 109) (µm) Particles (%) per ﬂux (n/cm2s)

0.0 α (73), 7Li (27) 11.65 0.1 α (74), 7Li (26) 11.46 0.2 α (76), 7Li (24) 11.33 0.5 α (75), 7Li (25) 10.60 1.0 α (77), 7Li (23) 9.27 1.5 α (76), 7Li (24) 8.32 2.0 α (74), 7Li (26) 7.50 3.0 α (68), 7Li (32) 6.52 4.0 α (67), 7Li (33) 6.22 5.0 α (67), 7Li (33) 6.15 10.0 α (66), 7Li (34) 6.17 15.0 α (66), 7Li (34) 6.22 66

In order to show the intermediate layer effects a CMOS transistor model was also constructed from studies of Reed [46, 47] in which soft error susceptibility of CMOS transistors to high energy ions were investigated. Same CMOS model, shown in Fig. 5.12, was constructed via NISCSAT to investigate complex models. The model consists of 14 µm × 8.89 µm × 14 µm node and 2 µm × 2 µm × 2 µm sensitive volume that was located at the center of the silicon substrate. Soft error simulations were performed with the CMOS model for the thermal neutrons and then the CMOS model was modified by adding BPSG layer with BENR. Modified CMOS models are shown in Fig. 5.13 and NISCSAT simulation results are given in Table 5.11. As expected, unmodified model shows no soft errors while BPSG-layered models have soft errors if the BPSG layer is located within the range of the alpha particles.

Figure 5.12. Simpliﬁed CMOS node model (CMOS-1) for NISCSAT simulations 67

Modiﬁed CMOS model 2 (CMOS-2) Modiﬁed CMOS model 3 (CMOS-3)

Modiﬁed CMOS model 4 (CMOS-4) Modiﬁed CMOS model 5 (CMOS-5)

Figure 5.13. Modiﬁed CMOS node models 68

Table 5.11. Simulation results for modiﬁed CMOS models with BENR and thermal neutrons Node Soft Errors Model Model Source Probability (× 109) Name Parameter(s) Particles (%) per ﬂux (n/cm2s)

CMOS-1 1 × 1 × 1 - - CMOS-2 1 × 1 × 1 - - CMOS-3 1 × 1 × 1 α (91), 7Li (9) 1.06 CMOS-4 1 × 1 × 1 α (79), 7Li (21) 0.568 CMOS-5 1 × 1 × 1 α (100) 0.157

5.2 Directional Neutron Detection Capability of the NISC

The direction information is difﬁcult to obtain for the neutrons since they interact with all the surrounding materials and scatter to different directions. When the neutrons reach the detection surface, the energy and the direction of the original neutrons are changed due to the interactions. However, for intense neutron sources it may be possible to detect some portion of the un-collided neutrons. The directional source detection capability of the NISC is discussed in this section with uniformly sampled, mono-directional plane neutron source and isotropic point neutron source. Point sources can also be treated as mono-directional sources if the distance between the source and the memory is far enough that all the incoming neutrons on the memory surface would have almost identical angles since the memory dimensions would be much smaller than the distance from the source. For example, if a point source were located 100 cm away from a memory with 10 cm x 10 cm surface area then the maximum deviation in the incoming neutron angle would be less than 1 degree. An isotropic point source model was also utilized to represent a more realistic detection scenario and simulations were performed for the same conﬁgurations.

5.2.1 Mono-directional Plane Source Calculations

The directional source detection of the NISC was investigated with two memory configurations. The first configuration consisted of two identical single-layer memories that 69

Figure 5.14. Simulation setup for the directional dependency of the NISC

were placed 1 cm apart (DM) from each other as illustrated in Fig. 5.14. Each memory had 2D-array of 5 µm × ( 2 µm + 1 µm ) × 5 µm nodes with BENR in the BPSG layer. Surface area of the each memory was chosen as 500x500 µm2 and contained 100x1x100 nodes. A mono-directional, mono-energetic, thermal neutron source was located, Dy, 100 cm in front of the memories and uniformly sampled over a plane surface. Sampling area of the source plane was chosen as 50 × 50 µm2 which was smaller than the memory surface area to ensure clear observation of the soft error occurrences in the memories. Location of the source plane was shifted horizontally, Dz, in one direction during the simulations while adjusting the angle of the source particles to make sure that they hit the same area on the memory surface. A matrix was formed for each memory from the locations of the memory nodes that had soft errors during the simulations and results were represented as the SER map for each memory. A new operator ” ” was defined as; only show the nodes in the second memory if the number of the soft errors for that node in the second memory was greater than the number of the soft errors for the node in the second memory. Applying the operator to Memory 2 and Memory 1, Memory 2 Memory 1, an additional SER map was also constructed from the differences between the SER maps of the second and first memory. Simulation results are given in Fig. 5.15 through Fig. 5.18. In the second configuration, instead of using two 70 memories, only one memory with multiple layers was placed in front of the same neutron source and results are given in Fig. 5.19 through Fig. 5.22. Additional results are given in AppendixB, Fig. B.1 through Fig. B.3.

Memory 1 Memory 2

Memory 2 Memory 1

Figure 5.15. Memory soft error maps of the multi-memory system for mono-directional plane source and source location shifted by Dz = 0 cm

Using memory address for determining the node locations in the memory and con- structing a SER map enables the directional detection of the neutrons for the NISC. Multiple memory conﬁgurations show clear change in the memory SER map with small change in the source location due to the fact that the distance between the memories are considerably larger than the dimensions of the memory. However, angle range is limited to calculate the incoming neutron direction. There were no soft errors in the second 71

Memory 1 Memory 2

Memory 2 Memory 1

Figure 5.16. Memory soft error maps of the multi-memory system for mono-directional plane source and source location shifted by Dz = 1 cm

memory after 2.5 cm shift in the source location which corresponds to 1.43 degree between the memory surface normal and the incoming neutron direction for the given simulation parameters. On the other hand, multi-layer memory conﬁguration shows much less dependency on the source location since the memory layers are much closer to each other and virtually all the directions are detectable though the resolution in the direction is much smaller. Combining both single-layer, multi-memory system with multi-layer, single-memory system gives a more efﬁcient system for directional dependency. 72

Memory 1 Memory 2

Memory 2 Memory 1

Figure 5.17. Memory soft error maps of the multi-memory system for mono-directional plane source and source location shifted by Dz = 2 cm 73

Memory 1 Memory 2

Memory 2 Memory 1

Figure 5.18. Memory soft error maps of the multi-memory system for mono-directional plane source and source location shifted by Dz = 3 cm 74

Layer 1 Layer 2

Layer 3

Layer 2 Layer 1 Layer 3 Layer 1

Figure 5.19. Memory soft error maps of the multi-layer memory system for mono-directional plane source and source location shifted by Dz = 50 cm 75

Layer 1 Layer 2

Layer 3

Layer 2 Layer 1 Layer 3 Layer 1

Figure 5.20. Memory soft error maps of the multi-layer memory system for mono-directional plane source and source location shifted by Dz = 500 cm 76

Layer 1 Layer 2

Layer 3

Layer 2 Layer 1 Layer 3 Layer 1

Figure 5.21. Memory soft error maps of the multi-layer memory system for mono-directional plane source and source location shifted by Dz = 1000 cm 77

Layer 1 Layer 2

Layer 3

Layer 2 Layer 1 Layer 3 Layer 1 Figure 5.22. Memory soft error maps of the multi-layer memory system for mono-directional plane source and source location shifted by Dz = 2000 cm 78

5.2.2 Isotropic Point Source Calculations

The directional detection capability of the NISC was also examined with an isotropic point source. In order to increase the neutron sampling on the memory surface, source was located 0.1 cm in front of the memories and it was relocated to different places in one direction similar to the plane source simulations. Distance between the memories was also changed to 0.1 cm. Simulation results with single-layer, multiple-memory system are shown in Figure 5.23 through Figure 5.26. Even if there was no particular pattern, as in the case of the plane source, it was still observed that the change in the SER maps also indicated the direction of the source.

Memory 1 Memory 2

Memory 2 Memory 1

Figure 5.23. Memory soft error maps of the multi-memory system for isotropic point source and source location shifted by Dz = 0 cm 79

Memory 1 Memory 2

Memory 2 Memory 1

Figure 5.24. Memory soft error maps of the multi-memory system for isotropic point source and source location shifted by Dz = 0.08 cm 80

Memory 1 Memory 2

Memory 2 Memory 1

Figure 5.25. Memory soft error maps of the multi-memory system for isotropic point source and source location shifted by Dz = 0.1 cm 81

Memory 1 Memory 2

Memory 2 Memory 1

Figure 5.26. Memory soft error maps of the multi-memory system for isotropic point source and source location shifted by Dz = 0.2 cm 82

Layer 1 Layer 2

Layer 3

Layer 2 Layer 1 Layer 3 Layer 1

Figure 5.27. Memory soft error maps of the multi-layer memory system for isotropic point source and source location shifted by Dz = 0.08 cm 83

Layer 1 Layer 2

Layer 3

Layer 2 Layer 1 Layer 3 Layer 1

Figure 5.28. Memory soft error maps of the multi-layer memory system for isotropic point source and source location shifted by Dz = 0.1 cm 84

5.3 Statistics in the NISC Simulations

Statistics in the NISCSAT is based on events that cause the soft errors in the model. Each event represents the history of a particle and ends with a ”true” or ”false” decision if they cause a soft error. At the end of each simulation, accumulated soft error numbers are used to calculate the soft error probability in the simulation. Relative error in a √ single simulation is represented as NSER/NSER, where NSER is the total number of the soft errors in the memory. In order to improve the statistics in the simulation results, each batch of simulations were performed with different random number sequences for the same simulation parameters. Resulting soft error numbers for each run are used to calculate the overall statistics in the simulations. In this section, uncertainties associated with the results were examined using a constant memory surface area of 500 × 500 µm with 5 µm × ( 2 µm + 1 µm ) × 5 µm node and different node array conﬁgurations. A mono-energetic, mono-directional thermal neutron source was uniformly sampled over the memory surface. Run conﬁguration for each simulation is represented as N = N B × N H, where N is total number of histories, NB is number of batches that the calculations repeated, and NH is number of histories for each batch. As in all the Monte Carlo methods, Table 5.12 shows that the √ standard deviation in the results improves with 1/ N. Simulations with 105 histories generated good results with small errors and it was used for all the simulations in this study. Extended simulation results are given in AppendixB, Table B.4. 85

Table 5.12. NISCSAT statistics in the simulation results for 2D-array models with 5 µm × ( 2 µm + 1 µm ) × 5 µm node and BENR

Node Soft Errors Array N Source Probability (× 109) Conﬁg. Particles (%) [±σ] per ﬂux (n/cm2s) [±σ]

1 × 1 × 1 5 × 103 α (69.14), 7Li (30.86) [10.4] 7.45 [1.81] 1 × 1 × 1 5 × 104 α (66.81), 7Li (33.19) [2.24] 7.74 [0.23] 1 × 1 × 1 5 × 105 α (64.83), 7Li (35.17) [0.73] 7.50 [0.12] 100 × 1 × 100 5 × 103 α (74.37), 7Li (25.63) [10.0] 9.80 [0.57] 100 × 1 × 100 5 × 104 α (67.20), 7Li (32.80) [2.03] 9.46 [0.28] 100 × 1 × 100 5 × 105 α (67.38), 7Li (32.62) [0.41] 9.60 [0.17] 1000 × 1 × 1000 5 × 103 α (68.00), 7Li (32.00) [8.00] 10.3 [1.93] 1000 × 1 × 1000 5 × 104 α (66.68), 7Li (33.32) [2.40] 9.64 [0.50] 1000 × 1 × 1000 5 × 105 α (67.28), 7Li (32.72) [0.72] 9.62 [0.20] Chapter 6

Cosmic Ray Background and Environmental Effects on the NISC

In this chapter, cosmic ray induced soft errors in the NISC design were investigated via NISC Soft Error Analysis Tool (NISCSAT). The NISC is sensitive to the thermal neutrons due to high neutron cross-section of 10B at thermal energies. In order to investigate the cosmic background effects on the NISC, cosmic rays were modeled by using Cos- mic Ray Shower Library (CRY) [69, 75], which was developed at Lawrence Livermore National Laboratory (LLNL). NISC single node and arrays were modeled at three different altitudes (sea level, 2100 m, and 11300 m) for different 10B content in the NISC model.

6.1 Cosmic Rays

The cosmic rays primarily consist of the galactic particles and the solar particles, and continuously penetrate through the atmosphere. The solar particles originate from the sun with energies up to 1 GeV and particle ﬂuxes depend on the 11-year period solar cycle. However, due to the interactions with the atmosphere, almost all of these particles are absorbed in the atmosphere and none of them can create particles. On the other hand, the galactic particles, mainly protons, have enormous energies (up to 108 GeV) and they create cascades of particles that can also create secondary cascades of particles and so on [16]. Origins of the cosmic terrestrial neutrons come from the galactic particles and 87 these neutrons contribute almost 97% of the cosmic rays at the sea level [17, 76].

Figure 6.1. Sea level cosmic ray ﬂuxes, generated by Cosmic Ray Shower Library (CRY)

6.1.1 Cosmic Rays Modeling

The semiconductor device node for the NISC design was chosen to be as simple as possible in order to focus on the 10B (n, α) 7Li reaction that is the main idea of the NISC. Cosmic rays were modeled with Cosmic Ray Shower Library (CRY) [69] from Lawrence Livermore National Laboratory (LLNL). The CRY is based on precomputed input tables derived from full MCNPX simulations of primary cosmic rays (1 GeV to 100 TeV primary particles) on full atmosphere model and enables a fast simulation of the cosmic rays without any computational time requirement besides the SER simulation in the NISCSAT. The CRY was called from the NISCSAT at three different altitudes sea level, 2100 m, and 11300 m with coordinates set to New York City. However, only downward cosmic particles are included in the CRY and the cosmic thermal neutron energy spectrum differs from the measured data (obtained from Joint Electron Device Engineering Council (JEDEC) [19]) due to neutron scattering and moderation at the sea level. The fact that the NISC is sensitive to the thermal neutrons, a modified cosmic thermal neutron energy spectrum obtained from the JEDEC standard was also used for 88 the sea level simulations. Cosmic ray particle fluxes at different altitudes are given in Fig. 6.1 and Figs. 6.3 to 6.5. Modified sea level cosmic neutron spectrum is shown in Fig. 6.2. Comparisons of calculated cosmic ray fluxes with the literature [76–82] are given in AppendixB, Fig. B.4 through Fig. B.10.

Figure 6.2. Sea level cosmic neutron ﬂux modiﬁcation to CRY using JEDEC standards

6.1.2 Simulation Results

The cosmic ray induced soft errors in the NISC is greater at sea level due to cosmic thermal neutrons compared to higher altitudes, as expected. As given in Table 6.1 and Table 6.2, the SER probabilities from the cosmic rays are negligible for the sea level thermal neutrons when compared to mono-energetic thermal neutron induced SERs in the NISC model. In addition, the calculated probabilities are normalized to cosmic ray flux and the SER probabilities would be much lower if the actual cosmic ray fluxes were used. Depending on the location on the world, the cosmic ray flux is about 10- 60 n/cm2 hr for sea level cosmic neutrons. The cosmic neutron flux is approximately 0.01 n/cm2s which is equivalent to 0.01 µg 252Cf point source at a distance nearly 4 m away. Even if the cosmic ray induced SER results can be disregarded, an accurate sea level cosmic thermal neutron modeling remains important since the scattered and 89

Figure 6.3. Comparison of cosmic neutron ﬂuxes at sea level slowed down neutron spectra can have 10 times higher SER probability. In addition to the cosmic thermal neutron induced 10B (n,α) 7Li reactions, other cosmic particles, mainly muons, also contribute the SER probability and there is no way to prevent those soft errors. Fortunately, any electronic device that will operate with the NISC can be calibrated to account for the cosmic ray induced soft errors during the operation. 90

Figure 6.4. Cosmic ray ﬂuxes generated by CRY at 2100 m

Figure 6.5. Cosmic ray ﬂuxes generated by CRY at 11300 m 91

Table 6.1. Cosmic rays induced soft errors in the NISC node model with different BPSG materials. Simulations were performed for 5 µm × ( 2 µm + 1 µm ) × 5 µm node Soft Errors Altitude BPSG Source Probability (× 109) (km) Material Particle (%) per ﬂux (particles/cm2s)

BENR µ- (100) 0.003 0 BPSGNAT µ+ (63), µ- (12), e- (25) 0.028 α (48), 7Li (24), µ- (14) BENR 0.074 e- (9), e+ (5) 0a 7Li (20), µ+ (40), µ- (20), BPSGNAT 0.018 e- (20) α (42), 7Li (17), µ- (17), BENR 0.043 2.1 e- (8), e+ (16) BPSGNAT µ- (100) 0.003 α (38), 7Li (13), µ- (12), BENR 0.029 11.3 e- (25), e+ (12) BPSGNAT e+ (100) 0.003 a Neutron spectrum modiﬁed with JEDEC 92

Table 6.2. Cosmic rays induced soft errors in the 3D-array NISC model. Simulations were performed for 5x(2+1)x5 µm node

Soft Errors

Altitude Array Probability (× 109) Source (km) Config. per flux (particles/cm2s) Particle (%) Total Nodea α (40), 7Li (30), µ- (7), 1K × 1 × K 2.38 × 105 0.238 µ+ (10), e- (6), e+ (3), p (4) α (56), 7Li (28), µ- (9), 0b 1K × 2 × K 5.26 × 105 0.263 µ+ (4), e- (2), e+ (1) α (57), 7Li (21), µ- (6), 1K × 5 × K 1.26 × 106 0.252 µ+ (8), e- (3), e+ (3), p (2) α (32), 7Li (25), µ- (11), 1K × 1 × K 9.94 × 104 0.099 µ+ (18), e- (7), e+ (7) α (54), 7Li (23), µ- (6), 2.1 1K × 2 × K 2.48 × 105 0.124 µ+ (7), e- (3), e+ (3), p (4) α (59), 7Li (20), µ- (5), 1K × 5 × K 5.99 × 105 0.120 µ+ (5), e- (5), e+ (2), p (6) α (54), 7Li (19), e- (8), 1K × 1 × K 9.28 × 104 0.093 e+ (8), p(11) α (56), 7Li (20), µ- (2), 11.3 1K × 2 × K 2.18 × 105 0.109 e- (8), e+ (3), p (11) α (55), 7Li (22), µ- (1), µ+ (2), 1K × 5 × K 5.28 × 105 0.106 e- (8), e+ (5), p (6), 10B (1) a Total probability divided by the total node number b Neutron spectrum modified with JEDEC 93

6.2 Environmental Effects

In this section, NISC model was utilized with different operating conditions of the memory such as speciﬁc humidity of the air, moderation of the neutron source, temperature, electric, and magnetic ﬁelds.

6.2.1 Temperature Effects in the NISC Model

The neutron cross-sections are temperature dependent and detection efﬁciency of the NISC is depend on the 10B (n, α) 7Li reaction. Therefore, the temperature effects on the NISC soft error probability was investigated by simulations with different temperatures. Figure 6.6 shows the simulation results at different temperatures (see AppendixB, Table B.5 for additional results). Simulation results indicate a minor dependency of the soft error probability on the temperature. This is expected since (n, α) cross-section of 10B has a poor dependency on the temperature (g-factor, represents the temperature dependency of a neutron cross-section, for 10B is 1.00027).

Figure 6.6. Node soft error probability variations with the temperature. Probabilities are normalized to value at 300 K 94

6.2.2 Humidity Effects in the NISC Model

One of the environmental conditions that may affect the NISC efficiency is the humidity of the air. In order to investigate the humidity effects on the soft error probability of the NISC, mono-directional thermal neutrons were sampled from 100 cm away from the memory and the specific humidity (defined as the ratio of the water vapor mass to the total air mass) of the air was changed during the simulations. Simulation results, given in Fig. 6.7, show that the increase in the humidity level scatters the incoming neutrons away and yields lower soft errors in the NISC. One would expect to have more soft errors due to moderation in the air, however, during the neutrons scatter and slow down, they are deflected from the memory surface area and cause less soft errors. Statistics in the fast neutron simulation results were too bad due to low soft error probabilities, therefore the results can be treated as if there were no difference in the soft error probabilities due to humidity effects for the fast neutrons. More detailed results are given in AppendixB, Table B.6 and Table B.7.

Figure 6.7. Node soft error probability variations with the humidity for the thermal neutron source. Probabilities are normalized to value at 10% humidity 95

6.2.3 Moderator Effects in the NISC Model

The NISC is sensitive to the thermal neutrons because of the 10B neutron cross-section. Neutron sources such as spontaneous fission sources emit fast neutrons, ≈ 2.0 MeV, and soft error probability in the NISC for the fast neutrons is too low. A possible solution would be usage of moderators with the NISC to slow down the incoming neutrons. NISC was modeled with selected moderators, Polyethylene, Water, Graphite, and Plex- iglass in order to observe the change in the soft error probabilities of the NISC in this section. Simulations were performed with mono-energetic, uniformly distributed neutron sources which were located 100 cm away from the memory surface. NISC model was chosen as 100 × 1 × 100 array of 5 µm × ( 2 µm + 1 µm ) × 5 µm node with BENR and results are shown in Fig. 6.8 (see AppendixB, Table B.8 for detailed results). Statistics in the fast neutron simulation results were too bad due to low soft error probabilities, therefore the results can be treated as if there were no difference in the soft error probabilities due to moderators for the fast neutrons. The results show that moderation of the incoming neutron source with an attached moderator to the NISC reduces the detection efficiency of the NISC. Scattering in the moderator causes neutrons to slow down, however, the original direction of the neutrons also changes and they are deflected away before reaching the memory surface. Therefore, attaching a moderator to the NISC decreases the detection efficiency of the NISC. 96

Figure 6.8. Node soft error probability variations with the moderators for the thermal neutron source. Probabilities are normalized to value with no moderator case

6.2.4 Electric and Magnetic Field Effects in the NISC Model

The effects of strong electromagnetic fields were investigated for the NISC model in this section. Magnetic field value for all the calculations up to this point were set to Earth’s magnetic field for New York City at the seal level. Field value was calculated by using ”International Geomagnetic Reference Field” (IGRF11) [83] model and horizontal intensity was set to 20 µT while vertical intensity was set to 49 µT. Strong electric, magnetic, and electromagnetic fields were introduced in the NISC model in order to investigate their effects on the NISC detection efficiency. Both electric and magnetic fields were set to enhance the soft error occurrences by accelerating the charged particles from the BPSG layer towards to the silicon layer in the nodes as illustrated in Fig. 6.9. Simulation results are given in Fig. 6.10 and Fig. 6.11 (see AppendixB, Table B.9 for detailed simulation results). The magnetic field almost has no effect on the soft error probability in the NISC since both α and 7Li are heavy particles and it is impractical to have such a strong magnetic field to bend their path in the memory. The electric field also has no major effects on the NISC efficiency; even if it is practical to create a strong 97

Figure 6.9. Electromagnetic field model for the NISC electric field across the node since the node dimensions are small. As a result, EM fields are not a solution to enhance the NISC efficiency. Since the dimensions are so small, it is really hard to deploy such strong fields in the memories by low power in a small place without affecting the system operations. Another reason for this is, since the nodes are in close proximity to each other, soft errors in the system have already been maximized via MBUs and further addition of the kinetic energy to the particles basically has no effect in total energy deposition of the particles in the system.

Figure 6.10. Node soft error probability variations with the electric ﬁeld. Probabilities are normalized to value with no ﬁeld 98

Figure 6.11. Node soft error probability variations with the magnetic ﬁeld. Probabilities are normalized to value with no ﬁeld Chapter 7

Conclusions and Future Work

Soft errors in the semiconductor memories are gaining more and more importance by continuous scaling of the transistor feature size. Neutron Intercepting Silicon Chip (NISC) addresses a new approach to neutron detection systems by introducing 10B- enriched BPSG layers to the semiconductor memory architectures to convert semiconductor memories to thermal neutron detectors. This research addresses the development of a simulation tool, NISC Soft Error Analysis Tool (NISCSAT), for soft error modeling and analysis in the semiconductor memories to provide basic design considerations for the NISC.

7.1 Conclusions

The proof of the principle was shown by the soft error rate (SER) measurement experiments in Penn State Breazeale Reactor (PSBR). Experiment results indicate that SER linearly increases with the incoming neutron ﬂux due to the fact that interaction probability increases in the memory. Decrease in the memory supply voltage results in lower critical charge requirement in the memory and increases soft error probability. NISCSAT has been successfully utilized to simulate a simple model of the NISC and was proven to be capable of simulating nuclear interactions and energy depositions in the memory models. NISC design considerations involved with the effects of device scaling, 10B content in the BPSG layer, incoming neutron energy, and critical charge of the node. NISCSAT simulations were performed with various memory node models to investigate these effects. Device scaling simulations show that 10B content of the 100

BPSG layer in the NISC node should be increased as much as possible in order to achieve higher soft error rates, hence higher neutron detection efficiencies. The reaction probability of the neutrons with 10B increases by adding more 10B into the BPSG layer. The thermal neutron mean free path in the 10B is about 19 µm which means that the reaction probability can be increased by increasing the thickness of the BPSG layer. On the other hand, device scaling simulations show that any further increase in the thickness of the BPSG layer by more than 2 µm causes self-shielding of the incoming neutrons in the BPSG layer and results in lower detection efficiencies due to absorption of the neutrons in the BPSG layer. Moreover, if the BPSG layer is located 7 µm apart from the depletion region in the node, there were no observed soft errors induced by 10B (n,α) 7Li reaction. This is due to the fact that both of the reaction products have lower ranges than in silicon or any possible node layers. In order to increase the detection efficiency of the NISC while avoiding self-shielding and particle range limitations, multiple memories with single-layer nodes or multiple memories with multi-layer nodes can be used, each having a BPSG layer thickness of 2 µm. Memory layers can be stacked up to 10 layers to make sure that the 10B (n,α) 7Li reaction takes place in the system. Simulations with memory models show that NISC efficiency can be increased almost linearly by adding extra memory layers or using multiple memories up to the levels at which the neutron- self-shielding becomes dominant for the additional memory layers. Simulations with a BPSG layer thickness of 2 µm also show that the intrinsic efficiency of the NISC can be increased by 5% via adding more memory layers until the neutron self-shielding starts to dominate and so the intrinsic efficiency saturates. When the intrinsic efficiency of the NISC saturates, the total BPSG layer thickness adds up to 80 µm ( 40 memory layers), and the intrinsic efficiency increases up to 66%. Soft error probability is directly affected by the critical charge, which depends on location and dimension of depletion region in the silicon substrate, memory architecture, and its operation conditions. The required critical charges for the NISC models were calculated from the thickness of the depletion region, treated as sensitive volume, or assigned a constant value in the NISCSAT simulations. Calculation results regarding the critical charge show that the soft error probability decreases with increasing critical charge requirement and there were no observed soft errors induced by 10B (n,α) 7Li reaction above 80 fC. The reason is that the combined energy of the reaction products do not have enough energy to generate charge in excess of 0.125 pC in the silicon substrate. 101

However, since the reaction products are emitted in oppositing directions, the maximum charge deposition is limited to 80 fC, which is equivalent to the full energy deposition of 1.776 MeV-α particles. Results also show that mean charge deposition of the reaction products in the sensitive volume of the node is about 15 fC, which implies that the NISC design should have a memory architecture with a critical charge of 15 fC or less in order to obtain higher detection efficiencies. The sensitive volume of a memory node is defined as the region where the memory structure is sensitive to any excess charge above the critical charge. Generally, depletion regions in a transistor, where the p-n junctions exist, are the sensitive volumes of the node. Location and dimension of the sensitive volume define the critical charge of the node with operation parameters and architecture of the memory. Simulation results show that surface area and hence the dimensions of the sensitive volume should be maximized as much as possible while keeping the critical charge value under 15fC. In addition, the sensitive volume should be placed in close proximity to the BPSG layers so that its location would be within the range of the α and 7Li particles. Results indicate that the distance between the BPSG layer and the sensitive volume should be less than 2 µm to increase the detection efficiency of the NISC. The incoming neutron energy is probably the most important design parameter for the NISC. Detection efficiency depends strongly on the neutron energy since the neutron cross-section of the (n,α) reaction is energy-dependent, and soft errors are considerably high only for the thermal neutron energies. Detection efficiency ratio of thermal (0.0253 eV) to fast (2 MeV) neutrons approximately equal to the ratio of 10B (n,α) 7Li neutron cross-sections for these energies which is 8000:1. Directional neutron detection capability of the NISC is also tested with different memory configurations via NISCSAT. Simulation results show that source direction information can be supplied by using multiple memory or multi-layer memory systems. Results also indicate that multiple memory configuration gives quick responses with a limited range of directions due to change in the incoming neutron direction while the multi-layer memory configuration shows less direction dependency but with a wide range of directional dependent detection. In order to achieve more accurate directional detection, combination of both configurations, multi-layer multiple memory, should be used in a detection system having NISC. Environmental conditions and their effects on the NISC performance were also in- 102 vestigated in this research. Because of the fact that the NISC is sensitive to thermal neutrons, cosmic thermal neutrons which are the most abundant particle type of the cosmic rays at sea level, are a concern for the thermal neutron detection by the NISC. Cosmic rays were modeled and simulated via NISCSAT in order to investigate the detection reliability of the NISC. If the cosmic rays and the thermal neutron source are assumed to have the same particle flux values, simulation results show that cosmic rays account for less than 2% of the soft errors for the thermal neutron detection. The actual cosmic thermal neutron flux value at sea level is quite low, approximately 0.01 n/cm2s, and can be ignored if the neutron source is strong. On the other hand, the cosmic ray flux depends on the altitude and varies exponentially, thus potentially causing higher background detection rates for the NISC. The cosmic ray flux at altitudes of about 2 km is 100 times higher than sea level flux and 10,000 times higher at altitudes of about 10 km. In addition, fast neutron detection by the NISC, which already has a poor efficiency, becomes almost impossible, particularly for higher altitudes where the cosmic ray fluxes and energies are higher. Temperature, one of the selected environmental parameters, was also investigated via NISCSAT simulations and results show that it has no major effects on the NISC detection efficiency since the neutron cross-section of 10B is almost temperature independent. Air humidity is another important parameter and one would expect to have higher detection efficiency with higher air humidity values, since the neutrons would be moderated more in the humid air and reaction probability would increase. Contrary to expectations, simulation results show that with increasing humidity values, even if the neutrons slow down in the air, they scatter away before reaching the memory surface, which results in lower efficiencies. The same trend is also observed by using a moderator to slow down the incoming neutrons, that is, using a moderator also decreases the detection efficiency by scattering the incoming neutrons. Electromagnetic fields were the last investigated environmental parameter and results show that there were no major effects on the NISC efficiency for even impractically high field values. As a result, NISCSAT is proven to be capable of calculating soft error probabilities in a given semiconductor memory model and simulation results proved that NISC is feasible for thermal neutron detections. It is concluded that NISC memory should be designed in such a way that it must operate at minimum allowable operating voltage to decrease the critical charge of the memory node and the depletion region must be 103 extended as much as possible in the node while keeping it close to the BPSG layer. Multiple memory or multi-layer memory configurations are required to increase NISC detection efficiency and the capability of directional neutron detection.

7.2 Recommendations and Future Work

The NISCSAT only performs particle transport and energy deposition calculations in a given memory and node models. Circuit simulations are needed to calculate critical charge, sensitive volume location and its dimensions. In addition, all the simulations were based on the steady state energy depositions, so that any excess charge deposition in the sensitive volume was treated as a soft error occurrence. This approximation does not reflect the real memory response in which the architecture and operation conditions of the memory plays an important role in deciding which event would cause a soft error in the memory and therefore circuit simulations must be coupled with NISCSAT simulations. The NISCSAT can be integrated with Soft Error Analysis Tool (SEAT) [68], which supports device level, circuit level, logic level, and architecture level soft error analysis to overcome this problem. Sentaurus DeviceTM from Synopsys [84] can be used to simulate solid-state device characteristics and can be coupled with nuclear simulations in order to calculate the SER. Memory node model in the NISCSAT is represented as homogenized regions of metal and insulation layers, but homogenization does not represent the real node structure. Therefore, detailed node and memory models must be constructed for more realistic results. The NISC models were chosen to be as simple as possible to observe the important design parameters in general. However, optimization calculations are needed for all the design parameters of the NISC in order to increase the detection efficiency. Even if the SER measurements are successfully done with the memory devices, an advanced system is required to investigate the transistor-based detection of the SER events in the whole memory arrays. The preliminary results obtained at the PSBR were simply performed by dumping a data pattern into the memory and then reading and comparing the written pattern with the original one while the device was under irradiation. Experiments involved errors including communication errors, bus errors, and system errors during the irradiation processes. These errors also come from the external radiations and determination of the SER events among these errors is very difficult. Therefore, an 104 advanced testing system is required for the verification of both experimental and simulation results. The most important drawback of the NISC simulations is that they are not verified or validated with experimental data from an actual NISC memory since no prototype was available during the progress of this dissertation. Once a NISC memory is produced, simulation results with actual experimental data must be compared so that further improvements in the NISC design can be accomplished. After successful completion of this research, a new neutron detection system can be put in the production line and integrated with currently available support systems for detecting and tracking thermal neutron sources. Appendix A

Critical Dimensions for Commercially Available Memories

Following sections presents some of the commercially available semi conductor memory critical dimensions. All the data taken from Chipworks Incorporated [45].

A.1 Microsoft X02170-001 eDRAM

Table A.1. Microsoft X02170-001 eDRAM package, die, and bond pad sizes Feature Size Package 35.0 m x 35.0 mm x 1.8 mm Die size (die seal) 7.87 mm x 8.30 mm Die thickness 650 µm Die area (die seal/whole seal) 65.3 mm2/66.0 mm2 Bond pad width 50 µm NAND gate 0.74 µm x 2.5 µm

Table A.2. Microsoft X02170-001 eDRAM well depths Layer Thickness (µm) Peripheral P-well ∼ 1 Peripheral N-well ∼ 1.5 Array N-well ∼ 1 106

Table A.3. Microsoft X02170-001 eDRAM transistor horizontal dimensions Structure Size (nm) Minimum NMOS gate length ∼ 60 Minimum PMOS gate length ∼ 60 PMOS eDRAM access transistor gate length 150 Minimum contact to gate spacing (eDRAM array) ∼ 75 Sidewall spacer thickness ∼ 60 Minimum contacted gate pitch (periphery) 350 Minimum pitch poly (eDRAM) 250

Table A.4. Microsoft X02170-001 eDRAM peripheral transistor and polycide vertical dimensions Structure Size (nm) Polycide gate thickness (NMOS) 140 Polycide gate thickness (PMOS) 130 Gate oxide thickness (access transistor) 3.0 N+ source/drain depth ∼ 0.08 P+ source/drain depth ∼ 0.07 (estimate)

Table A.5. Microsoft X02170-001 eDRAM metallization dimensions Layer Width (µm) Pitch (µm) thickness (µm) Metal 8 4.5 - 1.5 Metal 7 0.49 1.04 0.83 Metal 6 0.19 0.29 0.29 Metal 5 0.19 0.28 0.29 Metal 4 0.19 0.28 0.28 Metal 3 0.19 0.29 0.33 Metal 2 0.20 0.29 0.32 Metal 1 0.17 0.32 0.24 eDRAM array Isolation under poly - - 0.39 107

Table A.6. Microsoft X02170-001 eDRAM dielectric thicknesses Composition Layer Thickness (µm) Layer (Layers Top to Bottom) (Layers Top to Bottom) Passivation Nitride/oxide 0.78 (0.43/0.35) ILD 7 Oxide/nitride/oxide/nitride 0.83 (0.27/0.08/0.40/0.08) ILD 6 Oxide/oxide/oxide/SiOCN 1.55 (0.70/0.05/0.74/0.06) ILD 5 SiOC/oxide/SiOCN 0.57 (0.50/0.05/0.04) ILD 4 SiOC/oxide/SiOCN 0.61 (0.52/0.04/0.05) ILD 3 SiOC/oxide/SiOCN 0.57 (0.48/0.04/0.05) ILD 2 SiOC/oxide/SiOCN 0.64 (0.55/0.04/0.05) ILD 1 SiOC/oxide/SiOCN 0.61 (0.52/0.04/0.05) SiOC/oxide/oxide/SiOC/ 1.3 (0.18/0.04/0.34/0.31/ PMD oxide/PSG/oxide 0.03/0.37/0.02) Isolation Oxide STI 0.39

Table A.7. Microsoft X02170-001 eDRAM cell dimensions Feature Width (µm) Length (µm) eDRAM cell 0.31 0.74 Capacitor 0.24 0.45 Wordline pitch 0.37 - Bitline pitch 0.31 - Metal 2 wordline strap pitch 0.37 -

Table A.8. Microsoft X02170-001 eDRAM capacitor layer thicknesses Feature Thickness (nm) eDRAM capacitor height ∼ 380 TiN common plate ∼ 20 Ti bottom plate ∼ 19 ZrO capacitor dielectric ∼ 8 - 10 Access transistor gate oxide 3.0 108

A.2 Nanya elixir N2TU51280AF-37B 512 Mbit DDR2 SDRAM

Table A.9. Nanya elixir N2TU51280AF-37B 512 Mbit DDR2 SDRAM horizontal dimensions, minimum pitch metals Minimum Width Minimum Space Minimum Pitch Layer (µm) ± 5 % (µm) ± 5 % (µm) ± 5 % Metal 3 0.62 0.34 0.96 Metal 2 0.23 0.22 0.45 Metal 1 0.10 0.12 0.22

Table A.10. Nanya elixir N2TU51280AF-37B 512 Mbit DDR2 SDRAM horizontal dimensions, contacts and vias Minimum Diameter Minimum Space Minimum Pitch Layer (µm) ± 5 % (µm) ± 5 % (µm) ± 5 % Via 2 0.30 0.30 0.60 Via 1 0.15 0.28 0.43 Contacts to diffusion 0.16 0.29 0.45 Contact to polycide 0.14 0.31 0.45 109

Table A.11. Nanya elixir N2TU51280AF-37B 512 Mbit DDR2 SDRAM horizontal dimensions, die, transistors, poly and isolation Layer Size (± 5 %) Die Size (edge seal) 9.22 mm x 9.39 mm Die Area (inside die seal/whole die) 86.6 mm2/87.9 mm2 Bond pad 72 µm x 72 µm DRAM cell size 0.225 µm x 0.45 µm (0.10 µm2) Minimum Peripheral NMOS Gate Length 0.15 µm Minimum Peripheral PMOS Gate Length 0.16 µm DRAM Access Transistor Gate Length 0.11 µm Access Transistor Gate Width ∼ 0.1 µm Minimum Pitch Polycide 0.22 µm Sidewall Spacer Thickness ∼0.04 µm Minimum Contact to Gate Spacing 0.08 µm Minimum Isolation Width 0.29 µm

Table A.12. Nanya elixir N2TU51280AF-37B 512 Mbit DDR2 SDRAM vertical dimensions Layer Composition Thickness (µm) ± 5 % Passivation Nitride/Oxide 0.44 (0.33/0.11) Metal 3 TiN/Al/TiAl 1.2 ( 0.05/1.1/ 0.06) Metal 2 TiN/Al/TiAl/Ti 0.46 ( 0.05/0.37/ 0.02/ 0.02) Metal 1 W/TiN 0.17 (0.15/ 0.02) IMD 2 Oxide/Oxide 0.40 IMD 1 Oxide/Oxide 0.42 ( 0.38/0.04) PMD Oxide/BPSG/Nitride 0.48 (0.19/0.28/ 0.01) Isolation under Poly Oxide ﬁlled STI 0.31 Polycide WSi 0.16 ( 0.07/ 0.09) DRAM Access Transistor Oxide 5.0 nm Gate Oxide Capacitor Dielectric N/O 3.5/1.5 nm N+ S/D Diffusion 0.08 P+ S/D Diffusion 0.09 (estimate) Shallow N-well 1.5 P-well 1.0 Deep N-well 2.2 Array N-well 8.0 Die Thickness 265 110

A.3 PSC A3R12E3GEF G6E 635BLC4M 512 Mbit DDR2 SDRAM

Table A.13. PSC A3R12E3GEF G6E 635BLC4M 512 Mbit DDR2 SDRAM: metals horizontal dimensions Min Width Min Space Min Pitch Layer (µm) (µm) (µm) Metal 3 0.40 0.40 0.80 Metal 2 0.25 0.20 0.45 Metal 1 0.055 0.14 0.20

Table A.14. PSC A3R12E3GEF G6E 635BLC4M 512 Mbit DDR2 SDRAM: vias and contacts horizontal dimensions Min Width Min Space Min Pitch Layer (µm) (µm) (µm) Via 2 0.40 0.40 0.80 Via 1 0.36 0.13 0.49 Tungsten contact 0.18 0.20 0.38 Poly contact 0.04 0.17 0.21 Poly extension 0.105 0.150 n/a Poly extension to n/a 0.02 n/a bitline space 111

Table A.15. PSC A3R12E3GEF G6E 635BLC4M 512 Mbit DDR2 SDRAM: transistors, poly, and isolation Layer Size (µm) Die size (edge seal) 8.04 mm x 8.57 mm Bond pad size 80 x 72 Bond pad space 5 Cell array region gate length 0.090 Periphery region gate length 0.130 Periphery region SWS thickness 0.070 Periphery contact to gate space 0.150 Minimum width isolation 0.090

Table A.16. PSC A3R12E3GEF G6E 635BLC4M 512 Mbit DDR2 SDRAM: vertical dimensions Layer Composition Thickness (µm) Passivation Oxynitride/Oxide 0.52 (0.46/0.06) Metal 3 TiN/Al/TiN 0.06/0.64/0.06 Metal 2 TiN/Al/TiN 0.06/0.27/0.06 Metal 1 Tungsten 0.07 IMD 2 Oxide/Oxide 1.28 (0.86/0.42) IMD 1 (cell region, above Oxide/Oxide/Oxide 0.48 (0.12/0.25/0.11) common plate) IMD 1 (cell region, below Oxide/PSG/Nitride/ 3.05 (1.83/0.92/0.05/ common plate) Oxide/Oxide 0.07/0.18) Oxide/Oxide/Oxide/ 3.75 (0.12/0.60/1.83/ IMD 1 (periphery) PSG/Nitride/Oxide/ 0.92/0.05//0.07/0.18) Oxide Oxide/Oxide/PSG/ 0.62 (0.14/0.06/0.41/ PMD Nitride) 0.01) Stacked capacitor dielectric Tantalum Oxide 0.05 Gate dielectric (cell region) Oxide 0.0075 Gate dielectric (periphery) Oxide 0.0045 Isolation under poly STI 0.290 N-well depth (cell region) - 1.5 P-well depth (cell region) embedded 0.6 N-well depth (periphery) - 1.1 P-well depth (periphery) - 1.1 P-type Epi layer thickness - 5.5 Die thickness - 240 Appendix B

Additional Tables and Figures for Chapter 5 and Chapter6

Additional or extended results for Chapter5 and6 are given in this chapter. Please refer to the text for explanation on the tables and ﬁgures.

B.1 Additional Tables and Figures for Chapter5

Additional or extended results for Chapter5 are presented in this section. 113

Table B.1. Additional results for device scaling simulations with 5 µm × ( tB + tS ) × 5 µm node. Simulations were performed with BENR as the BPSG material and thermal neutrons Soft Errors t +t Q Probability B S CRIT Source (µm) (fC) (× 109) per flux Particles (%) (n/cm2s) 1.0 + 0.035 0.003 α (55), 7Li (44), e- & γ (≤ 1) 7.36 1.0 + 0.065 0.009 α (54), 7Li (45), e- & γ (≤ 1) 7.73 1.0 + 0.090 0.019 α (56), 7Li (43), e- & γ (≤ 1) 7.47 4.5 + 0.5 0.575 α (66), 7Li (34) 8.39 4.0 + 1.0 2.300 α (66), 7Li (34) 8.27 3.0 + 2.0 9.200 α (68), 7Li (32) 7.69 2.0 + 3.0 20.70 α (75), 7Li (25) 5.11 1.0 + 4.0 36.80 α (97), 7Li (3) 2.01 0.5 + 4.5 46.58 α (100) 1.03 1.0 + 1.0 α (55), 7Li (45) 6.97 2.0 + 1.0 α (65), 7Li (35) 8.43 3.0 + 1.0 α (67), 7Li (33) 8.86 2.300 9.0 + 1.0 α (67), 7Li (33) 6.56 19.0 + 1.0 α (64), 7Li (36) 4.10 49.0 + 1.0 α (65), 7Li (35) 0.91 114 5 5 5 5 5 5 5 5 ) 10 10 10 10 10 10 10 10 9 s) × × × × × × × × 2 10 Node 0.480 1.872 11.12 0.490 1.902 11.15 × 1.11 1.11 1.15 1.10 1.15 1.18 1.15 1.15 5 5 5 5 5 5 5 5 5 5 5 5 5 5 10 10 10 10 10 10 10 10 10 10 10 10 10 10 × × × × × × × × × × × × × × per flux (n/cm Total Probability ( 1.11 1.11 1.15 1.10 1.15 1.18 1.15 1.15 1.20 1.17 1.11 1.22 1.19 1.12 3) 4) 5) 3) ≤ ≤ ≤ ≤ ( ( Soft Errors γ γ , & p ( , & p ( γ γ Li (31) Li (33) Li (32) Li (33) Li (34) Li (33) Li (33) Li (34) Li (35) Li (33) 7 7 7 7 7 7 7 7 7 7 Source (68), (67), (68), (67), (66), (67), (67), (66), (65), (67), Li (31), e-& Li (31), e-& 7 7 Particles (%) Li (30), e-, α α α α α α α α α α Li (30), e-& 7 7 (64), (67), (66), α α (67), α α and constant memory sur- ENR 1 1 1 1 1 1 1 1 500 250 100 500 250 100 × × × × × × × × × × × × × × 1 1 1 1 1 1 1 1 1 1 1 1 1 1 × × × × × × × × × × × × × × Array Config. 1 1 1 1 1 1 1 1 500 250 100 500 250 100 CRIT (fC) 2.300 1.863 0.828 0.282 0.075 2.300 2.300 2.300 1.863 1.863 1.863 Q 0.0186 0.0083 0.0028 . Thermal neutron source was used. 500 1 2 5 500 500 500 500 500 500 2 500 1 2 5 × m × × × × × × × × × W µ × × × × × ) S 500 m) +t µ × B ( (t ( 2 + 1 ) ( 2 + 1 ) ( 2 + 1 ) ( 2 + 1 ) × ( 2 + 0.90 ) ( 2 + 0.90 ) ( 2 + 0.90 ) ( 2 + 0.90 ) ( 2 + 0.60 ) ( 2 + 0.35 ) ( 2 + 0.18 ) ( 2 + 0.09 ) ( 2 + 0.06 ) Node Dim. ( 2 + 0.035 ) × × × × W × × × × × × × × × 1 2 5 × 1 2 5 500 500 500 500 500 500 500 500 Continued on Next Page. . . Table B.2: Extended 2D-array simulation resultsface area with of B 500 115 ) 9 s) 2 10 Node 0.494 1.835 11.45 0.507 1.866 11.42 0.510 2.004 12.05 0.508 1.948 12.18 0.520 1.932 11.63 × 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 × × × × × × × × × × × × × × × per flux (n/cm Total Probability ( 1.23 1.15 1.14 1.27 1.17 1.14 1.28 1.25 1.20 1.27 1.22 1.22 1.30 1.21 1.16 3) 4) 4) 3) 3) 4) 2) 3) 2) ≤ ≤ ≤ ≤ ≤ ≤ ≤ ≤ ≤ ( ( ( ( ( ( ( ( ( Soft Errors γ γ γ γ γ γ γ γ γ Li (34) Li (33) Li (33) Li (35) Li (37) Li (34) 7 7 7 7 7 7 Source (66), (67), (67), (65), (67), (66), Li (34), e-& Li (32), e-& Li (32), e-& Li (33), e-& Li (32), e-& Li (31), e-& Li (33), e-& Li (33), e-& Li (32), e-& 7 7 7 7 7 7 7 7 7 Particles (%) α α α α α α (63), (64), (64), (64), (65), (65), (65), (64), (66), α α α α α α α α α 500 250 100 500 250 100 500 250 100 500 250 100 500 250 100 Table B.2 – Continued × × × × × × × × × × × × × × × 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 × × × × × × × × × × × × × × × Array Config. 500 250 100 500 250 100 500 250 100 500 250 100 500 250 100 CRIT (fC) 0.828 0.828 0.828 0.282 0.282 0.282 0.075 0.075 0.075 Q 0.0186 0.0186 0.0186 0.0083 0.0083 0.0083 1 2 5 1 2 5 1 2 5 1 2 5 1 2 5 × × × × × × × × × × × × × × × )xW S m) +t µ B ( ( 2 + 0.60 ) ( 2 + 0.60 ) ( 2 + 0.60 ) ( 2 + 0.35 ) ( 2 + 0.35 ) ( 2 + 0.35 ) ( 2 + 0.18 ) ( 2 + 0.18 ) ( 2 + 0.18 ) Node Dim. ( 2 + 0.090 ) ( 2 + 0.090 ) ( 2 + 0.090 ) ( 2 + 0.060 ) ( 2 + 0.060 ) ( 2 + 0.060 ) Wx(t × × × × × × × × × × × × × × × 1 2 5 1 2 5 1 2 5 1 2 5 1 2 5 Continued on Next Page. . . 116 ) 9 s) 2 10 Node 0.508 1.948 11.61 × 5 5 5 10 10 10 × × × per flux (n/cm Total Probability ( 1.27 1.22 1.16 1) 1) 2) ≤ ≤ ≤ ( ( ( Soft Errors γ γ γ Source Li (34), e-& Li (33), e-& Li (33), e-& 7 7 7 Particles (%) (65), (66), (65), α α α 500 250 100 Table B.2 – Continued × × × 1 1 1 × × × Array Config. 500 250 100 CRIT (fC) Q 0.0028 0.0028 0.0028 1 2 5 × × × )xW S m) +t µ B ( Node Dim. ( 2 + 0.035 ) ( 2 + 0.035 ) ( 2 + 0.035 ) Wx(t × × × 1 2 5 117 5 5 5 5 5 6 6 6 ) 9 10 10 10 10 10 10 10 10 s) × × × × × × × × 2 10 Node 0.568 2.207 13.47 0.575 2.260 13.88 × 1.33 1.35 1.28 1.33 1.44 1.04 1.05 1.04 5 5 5 5 5 6 6 6 5 5 5 5 5 5 10 10 10 10 10 10 10 10 10 10 10 10 10 10 × × × × × × × × × × × × × × per flux (n/cm Total Probability ( 6.64 6.75 7.67 7.97 8.64 1.04 1.05 1.04 7.11 6.90 6.74 7.19 7.06 6.94 7) 4) 3) 2) ≤ ≤ ≤ ≤ ( ( ( ( Soft Errors γ γ γ γ Li (27) Li (27) Li (28) Li (29) Li (27) Li (27) Li (26) Li (28) Li (27) Li (27) 7 7 7 7 7 7 7 7 7 7 Source (73), (73), (72), (71), (73), (73), (74), (72), (73), (73), Li (28), e-& Li (32), e-& Li (33), e-& Li (33), e-& 7 7 7 7 Particles (%) α α α α α α α α α α (65), (64), (64), (65), α α α α and constant memory vol- 1 1 1 1 1 1 1 1 500 250 100 500 250 100 × × × × × × × × × × × × × × ENR 5 5 6 6 6 5 5 5 5 5 5 10 10 10 × × × × × × × × × × × Array × × × Config. 1 1 1 1 1 1 1 1 500 250 100 500 250 100 CRIT (fC) 2.300 1.863 0.828 0.282 0.075 2.300 2.300 2.300 1.863 1.863 1.863 Q 0.0186 0.0083 0.0028 500 500 500 500 500 500 500 1 2 5 × . Thermal neutron source was used. × × × × × × × × × 3 1 2 5 500 W m × × × × × µ ) S m) 500 + t µ ( B × ( 2 + 1 ) ( 2 + 1 ) ( 2 + 1 ) ( 2 + 1 ) ( t Node Dim. 15 × × × × × ( 2.10 + 0.90 ) ( 1.90 + 0.60 ) ( 2.15 + 0.35 ) ( 2.32 + 0.18 ) ( 1.41 + 0.09 ) ( 2.10 + 0.90 ) ( 2.10 + 0.90 ) ( 5.10 + 0.90 ) ( 1.44 + 0.060 ) × ( 1.465 + 0.035 ) 1 2 5 W × × × × × × × × × 500 × 1 2 2 500 500 500 500 500 500 500 Continued on Next Page. . . Table B.3: Extended 3D-array simulation resultsume with of 500 B 118 ) 9 s) 2 10 Node 0.542 2.123 13.00 0.570 2.215 13.53 0.616 2.414 14.64 0.436 1.707 10.56 0.433 1.707 10.51 × 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 × × × × × × × × × × × × × × × per flux (n/cm Total Probability ( 8.14 7.96 7.80 8.55 8.30 8.12 9.24 9.05 8.78 1.09 1.07 1.05 1.08 1.07 1.05 5) 7) 7) 3) 4) 4) 2) 2) 3) ≤ ≤ ≤ ≤ ≤ ≤ ≤ ≤ ≤ ( ( ( ( ( ( ( ( ( Soft Errors γ γ γ γ γ γ γ γ γ Li (29) Li (28) Li (28) Li (30) Li (30) Li (30) 7 7 7 7 7 7 Source Li (29),e-& Li (28),e-& Li (28),e-& Li (30),e-& Li (30),e-& Li (31),e-& Li (31),e-& Li (32),e-& Li (32),e-& (71), (72), (72), (70), (70), (70), 7 7 7 7 7 7 7 7 7 Particles (%) α α α α α α (66), (65), (65), (67), (66), (65), (67), (66), (65), α α α α α α α α α 500 250 100 500 250 100 500 250 100 500 250 100 500 250 100 × × × × × × × × × × × × × × × 6 6 6 6 6 6 6 6 6 10 10 10 10 10 10 Table B.3 – Continued × × × × × × × × × Array × × × × × × Config. 500 250 100 500 250 100 500 250 100 500 250 100 500 250 100 CRIT (fC) 0.828 0.828 0.828 0.282 0.282 0.282 0.075 0.075 0.075 Q 0.0186 0.0186 0.0186 0.0083 0.0083 0.0083 1 2 5 1 2 5 1 2 5 1 2 5 1 2 5 × × × × × × W × × × × × × × × × × ) S m) + t µ ( B ( t Node Dim. ( 1.90 + 0.60 ) ( 1.90 + 0.60 ) ( 1.90 + 0.60 ) ( 2.15 + 0.35 ) ( 2.15 + 0.35 ) ( 2.15 + 0.35 ) ( 2.32 + 0.18 ) ( 2.32 + 0.18 ) ( 2.32 + 0.18 ) × ( 1.410 + 0.090 ) ( 1.410 + 0.090 ) ( 1.410 + 0.090 ) ( 1.440 + 0.060 ) ( 1.440 + 0.060 ) ( 1.440 + 0.060 ) × × × × × × × × × W × × × × × × 1 2 5 1 2 5 1 2 5 1 2 5 1 2 5 Continued on Next Page. . . 119 ) 9 s) 2 10 Node 0.432 1.704 10.52 × 6 6 6 10 10 10 × × × per flux (n/cm Total Probability ( 1.08 1.06 1.05 2) 2) 2) ≤ ≤ ≤ ( ( ( Soft Errors γ γ γ Source Li (31),e-& Li (32),e-& Li (32),e-& 7 7 7 Particles (%) (67), (66), (66), α α α 500 250 100 × × × 10 10 10 Table B.3 – Continued Array × × × Config. 500 250 100 CRIT (fC) Q 0.0028 0.0028 0.0028 1 2 5 × × × W × ) S m) + t µ ( B ( t Node Dim. × ( 1.465 + 0.035 ) ( 1.465 + 0.035 ) ( 1.465 + 0.035 ) W × × × 1 2 5 120

Layer 1 Layer 2

Layer 3

Layer 2 Layer 1 Layer 3 Layer 1

Figure B.1. Memory soft error maps of the multi-layer memory system for mono-directional plane source and source location shifted by Dz = 1 cm 121

Layer 1 Layer 2

Layer 3

Layer 2 Layer 1 Layer 3 Layer 1

Figure B.2. Memory soft error maps of the multi-layer memory system for mono-directional plane source and source location shifted by Dz = 100 cm 122

Layer 1 Layer 2

Layer 3

Layer 2 Layer 1 Layer 3 Layer 1

Figure B.3. Memory soft error maps of the multi-layer memory system for mono-directional plane source and source location shifted by Dz = 200 cm 123

B.2 Additional Tables and Figures for Chapter6

Additional or extended results for Chapter6 are presented in this section.

Figure B.4. Comparison of cosmic muon ﬂuxes at sea level 124

Figure B.5. Comparison of cosmic proton ﬂuxes at sea level

Figure B.6. Comparison of cosmic electron ﬂuxes at sea level 125

Table B.4. Extended results for the NISCSAT uncertainty calculations. Simulations were performed with BENR and 5 µm × ( 2 µm + 1 µm ) × 5 µm node. Thermal neutron source was used.

Node Soft Errors Array N Source Probability (× 109) Conﬁg. Particles (%) [±σ] per ﬂux (n/cm2s) [±σ]

1 × 1 × 1 5 × 102 α (66.29), 7Li (33.71) [36.7] 9.49 [5.34] 1 × 1 × 1 5 × 103 α (69.14), 7Li (30.86) [10.4] 7.45 [1.81] 1 × 1 × 1 5 × 104 α (66.81), 7Li (33.19) [2.24] 7.74 [0.23] 1 × 1 × 1 5 × 105 α (64.83), 7Li (35.17) [0.73] 7.50 [0.12] 1 × 5 × 1 5 × 102 α (72.22), 7Li (27.78) [7.03] 9.10 [1.27] 1 × 5 × 1 5 × 103 α (69.11), 7Li (30.89) [2.65] 9.26 [0.43] 1 × 5 × 1 5 × 104 α (69.05), 7Li (30.95) [1.24] 9.46 [0.05] 1 × 5 × 1 5 × 105 α (68.42), 7Li (31.58) [0.40] 9.52 [0.04] 100 × 1 × 100 5 × 102 α (59.29), 7Li (40.71) [27.2] 11.50 [4.07] 100 × 1 × 100 5 × 103 α (74.37), 7Li (25.63) [10.0] 9.80 [0.57] 100 × 1 × 100 5 × 104 α (67.20), 7Li (32.80) [2.03] 9.46 [0.28] 100 × 1 × 100 5 × 105 α (67.38), 7Li (32.62) [0.41] 9.60 [0.17] 100 × 2 × 100 5 × 102 α (83.25), 7Li (16.75) [13.7] 9.49 [3.40] 100 × 2 × 100 5 × 103 α (73.86), 7Li (26.17) [2.19] 11.80 [1.09] 100 × 2 × 100 5 × 104 α (71.76), 7Li (28.24) [1.29] 11.65 [0.25] 100 × 2 × 100 5 × 105 α (71.69), 7Li (28.31) [0.22] 11.71 [0.09] 100 × 5 × 100 5 × 102 α (80.22), 7Li (19.78) [6.15] 10.51 [2.83] 100 × 5 × 100 5 × 103 α (73.12), 7Li (26.88) [3.20] 11.28 [0.54] 100 × 5 × 100 5 × 104 α (73.34), 7Li (26.66) [1.11] 11.49 [0.28] 100 × 5 × 100 5 × 105 α (73.37), 7Li (26.63) [0.19] 11.61 [0.06] 1000 × 1 × 1000 5 × 102 α (88.50), 7Li (11.50) [10.2] 11.03 [5.23] 1000 × 1 × 1000 5 × 103 α (68.00), 7Li (32.00) [8.00] 10.30 [1.93] 1000 × 1 × 1000 5 × 104 α (66.68), 7Li (33.32) [2.40] 9.64 [0.50] 1000 × 1 × 1000 5 × 105 α (67.28), 7Li (32.72) [0.72] 9.62 [0.20] 1000 × 5 × 1000 5 × 102 α (77.59), 7Li (22.41) [13.0] 11.70 [1.22] 1000 × 5 × 1000 5 × 103 α (73.81), 7Li (26.19) [2.70] 11.86 [0.30] 1000 × 5 × 1000 5 × 104 α (73.45), 7Li (26.55) [0.96] 11.64 [0.14] 1000 × 5 × 1000 5 × 105 α (73.61), 7Li (26.39) [0.12] 11.65 [0.04] 126

Figure B.7. Comparison of cosmic neutron ﬂuxes at 11300 m

Figure B.8. Comparison of cosmic proton ﬂuxes at 11300 m 127

Figure B.9. Comparison of cosmic electron ﬂuxes at 11300 m

Figure B.10. Comparison of cosmic muon ﬂuxes at 11300 m 128

Table B.5. Extended simulation results for temperature effects on the NISC model with the thermal neutron source. Simulations were performed with 100 × 1 × 100 array of 5 µm × ( 2 µm + 1 µm ) × 5 µm nodes and BENR as the BPSG material. Soft Errors Temperature Probability (× 109) Source (K) per ﬂux (n/cm2s) Particles (%) Total Nodea 50 α (67), 7Li (33) 1.10 × 105 11.03 100 α (67), 7Li (33) 1.14 × 105 11.35 200 α (67), 7Li (33) 1.15 × 105 11.50 300 α (67), 7Li (33) 1.14 × 105 11.39 500 α (67), 7Li (33) 1.13 × 105 11.32 800 α (68), 7Li (32) 1.13 × 105 11.28 1000 α (68), 7Li (32) 1.11 × 105 11.13 a Total probability divided by the total node number 129

Table B.6. Extended simulation results for humidity effects on the NISC model with the thermal neutron source. Simulations were performed with 100 × 1 × 100 array of 5 µm × ( 2 µm + 1 µm ) × 5 µm nodes and BENR as the BPSG material. Soft Errors Speciﬁc Probability (× 109) Source Humidity per ﬂux (n/cm2s) Particles (%) (%) Total Nodea 0 α (68), 7Li (32) 9.99 × 104 9.99 10 α (68), 7Li (32) 9.93 × 104 9.93 20 α (68), 7Li (32) 9.54 × 104 9.54 30 α (67), 7Li (33) 9.39 × 104 9.39 40 α (67), 7Li (33) 9.19 × 104 9.19 50 α (67), 7Li (33) 8.79 × 104 8.79 60 α (67), 7Li (33) 8.48 × 104 8.48 70 α (67), 7Li (33) 8.25 × 104 8.25 75 α (67), 7Li (33) 8.22 × 104 8.22 80 α (67), 7Li (33) 8.37 × 104 8.37 85 α (67), 7Li (33) 8.07 × 104 8.07 90 α (68), 7Li (32) 8.08 × 104 8.08 100 α (69), 7Li (31) 8.20 × 104 8.20 a Total probability divided by the total node number Source is located at 100 cm away from the memory 130

Table B.7. Extended simulation results for humidity effects on the NISC model with 2MeV neutron source. Simulations were performed with 100 × 1 × 100 array of 5 µm × ( 2 µm + 1 µm ) × 5 µm nodes and BENR as the BPSG material. Soft Errors Speciﬁc Probability (× 109) Source Humidity per ﬂux (n/cm2s) Particles (%) (%) Total Nodea 0 α (33), 7Li (33), 10B (33) 75.0 0.0075 10 10B (100) 25.0 0.0025 20 10B (100) 25.0 0.0025 30 α (50), 28Si (100) 50.0 0.0050 40 α (100) 25.0 0.0025 50 7Li (50), 28Si (50) 50.0 0.0050 60 α (50), 28Si (50) 100.0 0.0100 70 α (50), 7Li (50) 50.0 0.0050 80 7Li (100) 50.0 0.0050 90 α (25), 10B (50), p (25) 100.0 0.0100 100 28Si (100) 25.0 0.0025 a Total probability divided by the total node number Source is located at 100 cm away from the memory 131

Table B.8. Extended simulation results for moderator effects on the NISC model with fast and thermal neutron source. Simulations were performed with 100 × 1 × 100 array of 5 µm × ( 2 µm + 1 µm ) × 5 µm nodes and BENR as the BPSG material. Moderator Node Soft Errora Neutron Thickness Probability (× 109) Energy Name (cm) per ﬂux (n/cm2s)

Bare - 9.4900 Polyethylene 0.1 7.4375 0.0253 eV Water 0.1 7.8425 Graphite 0.1 9.1400 Plexiglass 0.1 7.8350 Bare - 0.0100 Polyethylene 0.1 0.0025 2.000 MeV Water 0.1 0.0050 Graphite 0.1 0.0050 Plexiglass 0.1 0.0100 Polyethylene 1.0 0.0050 Water 1.0 0.0100 2.000 MeV Graphite 1.0 0.0050 Plexiglass 1.0 - Polyethylene 10.0 - Water 10.0 0.0025 2.000 MeV Graphite 10.0 0.0025 Plexiglass 10.0 0.0025 a Total probability divided by the total node number 132

Table B.9. Extended simulation results for electromagnetic ﬁeld effects on the NISC model with the thermal neutron source. Simulations were performed with 100 × 1 × 100 array of 5 µm × ( 2 µm + 1 µm ) × 5 µm nodes and BENR as the BPSG material. Value Node Soft Errors Field E B Source Probability (× 109) Name (V/cm) (T) Particles (%) per ﬂux (n/cm2s)

EM-1 0.0 0.0 α (73), 7Li (27) 11.53 EM-2 108 0.0 α (74), 7Li (26) 11.43 EM-2 106 0.0 α (74), 7Li (26) 11.59 EM-2 104 0.0 α (73), 7Li (27) 11.65 EM-2 102 0.0 α (73), 7Li (27) 11.53 EM-2 100 0.0 α (73), 7Li (27) 11.54 EM-2 10-2 0.0 α (73), 7Li (27) 11.54 EM-3 0.0 106 α (73), 7Li (27) 11.52 EM-3 0.0 104 α (73), 7Li (27) 11.56 EM-3 0.0 102 α (73), 7Li (27) 11.65 EM-3 0.0 100 α (73), 7Li (27) 11.53 EM-3 0.0 10-2 α (73), 7Li (27) 11.56 EM-4 104 102 α (73), 7Li (27) 11.54 a Total probability divided by the total node number Bibliography

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[84] Sentaurus Device - An advanced multidimensional (1D/2D/3D) device simulator. URL http://www.synopsys.com Vita Cihangir C¸elik

Cihangir Çelik was born to Mehmet and Dursun Çelik in Tarsus, Turkey. He re- ceived his B.Sc. degree in Nuclear Engineering from the department of Nuclear Energy Engineering, Hacettepe University in 2001. He started to work as a research and teach- ing assistant at the same department where he also earned his M.Sc. degree in Nuclear Engineering from Hacettepe University in 2004. He started to pursue his Ph.D. degree at the Pennsylvania State University. During his Ph.D. study, he also worked as a graduate research assistant at the Department of Mechanical and Nuclear Engineering, Penn State University. He is a member of the American Nuclear Society (ANS) and the Alpha Nu Sigma Society. He is married to Gonca Yılmaz Çelik. He and his wife have one child, Bora Mete Çelik. They currently reside in the United States.