Introducing 14-Nm Finfet Technology in Microwind Etienne Sicard
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Analog/Mixed-Signal Design in Finfet Technologies A.L.S
Analog/Mixed-Signal Design in FinFET Technologies A.L.S. Loke, E. Terzioglu, A.A. Kumar, T.T. Wee, K. Rim, D. Yang, B. Yu, L. Ge, L. Sun, J.L. Holland, C. Lee, S. Yang, J. Zhu, J. Choi, H. Lakdawala, Z. Chen, W.J. Chen, S. Dundigal, S.R. Knol, C.-G. Tan, S.S.C. Song, H. Dang, P.G. Drennan, J. Yuan, P.R. Chidambaram, R. Jalilizeinali, S.J. Dillen, X. Kong, and B.M. Leary [email protected] Qualcomm Technologies, Inc. September 4, 2017 CERN ESE Seminar (Based on AACD 2017) Mobile SoC Migration to FinFET Mobile SoC is now main driver for CMOS scaling • Power, Performance, Area, Cost (PPAC) considerations, cost = f(volume) • Snapdragon™ 820 - Qualcomm Technologies’ first 14nm product • Snapdragon™ 835 – World’s first 10nm product Not drawn to scale Plenty of analog/mixed-signal content Display • PLLs & DLLs Camera • Wireline I/Os Memory • Data converters BT & WLAN • Bandgap references DSP GPU • Thermal sensors • Regulators • ESD protection CPU Audio Modem SoC technology driven by logic & SRAM scaling needs due to cost Terzioglu, Qualcomm [1] Loke et al., Analog/Mixed-Signal Design in FinFET Technologies Slide 1 Outline • Fully-Depleted FinFET Basics • Technology Considerations • Design Considerations • Conclusion Loke et al., Analog/Mixed-Signal Design in FinFET Technologies Slide 2 Towards Stronger Gate Control log (I ) gate D VGS IDsat Cox I VDS S Dlin drain I source ϕs T I CB CD off lower supply lower power DIBL body VBS VGS VTsat VTlin VDD • Capacitor divider dictates source-barrier ϕs & ID • Fully-depleted finFET weakens CB, CD steeper -
Design of Finfet Based 1-Bit Full Adder
ISSN (Print) : 2320 – 3765 ISSN (Online): 2278 – 8875 International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (A High Impact Factor, Monthly, Peer Reviewed Journal) Website: www.ijareeie.com Vol. 8, Issue 6, June 2019 Design of Finfet Based 1-Bit Full Adder Priti Sahu1, Ravi Tiwari2 M.Tech Scholar, Department of Electronics and Tele-Communication Engineering, Shri Shankaracharya Group of Institutions Engineering (SSGI), CSVTU, Bhilai, Chattisgarh, India Department of Electronics and Tele-Communication Engineering, Shri Shankaracharya Group of Institutions Engineering (SSGI), CSVTU, Bhilai, Chattisgarh, India ABSTRACT: This paper proposes a 1-bit Full adder using Fin type Field Effect Transistor (FinFETs) at 250nm CMOS technology. The paper is intended to reduce leakage current and leakage power, chip area, and to increase the switching speed of 1-bit Full Adder while maintaining the competitive performance with few transistors are used. In this paper, we are designed a double-gate (DG) FinFETs and extracting their transfer characteristics by using Synopsys TANNER- EDA simulation tool. We investigate the use of Double Gate FinFET technology which provides low leakage and high- performance operation by utilizing high speed and low threshold voltage transistors for logic cells. Which show that it is particularly effective in sub threshold circuits and can eliminate performance variations with Low power. A 22ns access time and frequency 0.045GHz provide 250nm CMOS process technology with 5V power supply is employed to carry out 1-bit Full Adder of speed, power and reliability compared to Metal Oxide Semiconductor Field Effect Transistor (MOSFET) based full adder designs. Hence FinFET is a promising candidate and is a better replacement for MOSFET. -
Memristive-Finfet Circuits for FFT & Vedic Multiplication
Special Issue - 2016 International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 NCETET - 2016 Conference Proceedings Threshold Logic Computing: Memristive-FinFET Circuits for FFT & Vedic Multiplication Nasria A Divya R PG Scholar Assistant Professor Applied Electronics and instrumentation Electronics and Communication Younus College of Engineering and Technology Younus College of Engineering and Technology Kollam, India Kollam, India Abstract—Threshold logic computing is the simplest kind of expensive because of their area, power, and delay overhead. computing units used to build artificial neural networks. Brain In the beginning stage, threshold gates have been mimic circuits can be developed electronically in different ways. implemented using CMOS devices, Single Electron But using Memristive Threshold Logic (MTL) cells have various Transistors and Monostable-Bistable Logic Elements and advantages than Logic gates. MTL cell is used for designing FFT resonant tunnelling diodes. Capacitor-based Threshold Logic computing useful for signal processing applications and Vedic addition and multiplications for efficient Arithmetic Logic unit gates (CTL) are implemented using capacitors as weights design. MTL cell is the basic cell which consist of two parts: along with CMOS-based comparator circuits. In CMOS CTL Memristor based input voltage averaging circuits and an output gates, sum of weighted input voltage is compared with a threshold circuit. The threshold circuit is the combination of fixed reference voltage, and based on this comparison, a Op-amp and FinFET circuit. FinFET circuits indicate much logical output is produced. CTL gates are used for 2D image lower power consumption, lower chip area and operates at filtering in. These CTL gates have many advantages such as lower voltages. -
Advanced MOSFET Structures and Processes for Sub-7 Nm CMOS Technologies
Advanced MOSFET Structures and Processes for Sub-7 nm CMOS Technologies By Peng Zheng A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Engineering - Electrical Engineering and Computer Sciences in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Tsu-Jae King Liu, Chair Professor Laura Waller Professor Costas J. Spanos Professor Junqiao Wu Spring 2016 © Copyright 2016 Peng Zheng All rights reserved Abstract Advanced MOSFET Structures and Processes for Sub-7 nm CMOS Technologies by Peng Zheng Doctor of Philosophy in Engineering - Electrical Engineering and Computer Sciences University of California, Berkeley Professor Tsu-Jae King Liu, Chair The remarkable proliferation of information and communication technology (ICT) – which has had dramatic economic and social impact in our society – has been enabled by the steady advancement of integrated circuit (IC) technology following Moore’s Law, which states that the number of components (transistors) on an IC “chip” doubles every two years. Increasing the number of transistors on a chip provides for lower manufacturing cost per component and improved system performance. The virtuous cycle of IC technology advancement (higher transistor density lower cost / better performance semiconductor market growth technology advancement higher transistor density etc.) has been sustained for 50 years. Semiconductor industry experts predict that the pace of increasing transistor density will slow down dramatically in the sub-20 nm (minimum half-pitch) regime. Innovations in transistor design and fabrication processes are needed to address this issue. The FinFET structure has been widely adopted at the 14/16 nm generation of CMOS technology. -
Characterization of the Cmos Finfet Structure On
CHARACTERIZATION OF THE CMOS FINFET STRUCTURE ON SINGLE-EVENT EFFECTS { BASIC CHARGE COLLECTION MECHANISMS AND SOFT ERROR MODES By Patrick Nsengiyumva Dissertation Submitted to the Faculty of the Graduate School of Vanderbilt University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY in Electrical Engineering May 11, 2018 Nashville, Tennessee Approved: Lloyd W. Massengill, Ph.D. Michael L. Alles, Ph.D. Bharat B. Bhuva, Ph.D. W. Timothy Holman, Ph.D. Alexander M. Powell, Ph.D. © Copyright by Patrick Nsengiyumva 2018 All Rights Reserved DEDICATION In loving memory of my parents (Boniface Bimuwiha and Anne-Marie Mwavita), my uncle (Dr. Faustin Nubaha), and my grandmother (Verediana Bikamenshi). iii ACKNOWLEDGEMENTS This dissertation work would not have been possible without the support and help of many people. First of all, I would like to express my deepest appreciation and thanks to my advisor Dr. Lloyd Massengill for his continual support, wisdom, and mentoring throughout my graduate program at Vanderbilt University. He has pushed me to look critically at my work and become a better research scholar. I would also like to thank Dr. Michael Alles and Dr. Bharat Bhuva, who have helped me identify new paths in my research and have been a constant source of ideas. I am also very grateful to Dr. W. T. Holman and Dr. Alexander Powell for serving on my committee and for their constructive comments. Special thanks go to Dr. Jeff Kauppila, Jeff Maharrey, Rachel Harrington, and Tim Haeffner for their support with test IC designs and experiments. I would also like to thank Dennis Ball (Scooter) for his tremendous help with TCAD models. -
The End of Moore's Law and Faster General Purpose Computing, and A
The End of Moore’s Law & Faster General Purpose Computing, and a Road Forward John Hennessy Stanford University March 2019 The End of an Era • 40 years of stunning progress in microprocessor design • 1.4x annual performance improvement for 40+ years ≈ 106 x faster (throughput)! • Three architectural innovations: • Width: 8->16->64 bit (~4x) • Instruction level parallelism: • 4-10 cycles per instruction to 4+ instructions per cycle (~10-20x) • Multicore: one processor to 32 cores (~32x) • Clock rate: 3 MHz to 4 GHz (through technology & architecture) • Made possible by IC technology: • Moore’s Law: growth in transistor count • Dennard Scaling: power/transistor shrinks as speed & density increase • Power = frequency x CV2 • Energy expended per computation was reducing Future processors 1 THREE CHANGES CONVERGE • Technology • End of Dennard scaling: power becomes the key constraint • Slowdown in Moore’s Law: transistors cost (even unused) • Architectural • Limitation and inefficiencies in exploiting instruction level parallelism end the uniprocessor era. • Amdahl’s Law and its implications end the “easy” multicore era • Application focus shifts • From desktop to individual, mobile devices and ultrascale cloud computing, IoT: new constraints. Future processors 2 UNIPROCESSOR PERFORMANCE (SINGLE CORE) Performance = highest SPECInt by year; from Hennessy & Patterson [2018]. Future processors 3 MOORE’S LAW IN DRAMS 4 THE TECHNOLOGY SHIFTS MOORE’S LAW SLOWDOWN IN INTEL PROCESSORS 10X Cost/transisto r slowing down faster, due to fab costs. Future processors 5 TECHNOLOGY, POWER, AND DENNARD SCALING 200 4.5 180 4 Technology (nm) 160 3.5 Energy/nm^2 140 3 120 2.5 100 2 80 1.5 60 Namometers 1 40 20 0.5 nm^2 per Power Relative 0 0 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 Power consumption Energy scaling for fixed task is better, since more & faster xistors. -
A 14Nm Finfet Transistor-Level 3D Partitioning Design to Enable High
A 14nm Finfet Transistor-Level 3D Partitioning Design to Enable High- Performance and Low-Cost Monolithic 3D IC Jiajun Shi1,3, Deepak Nayak1, Srinivasa Banna1, Robert Fox2, Srikanth Samavedam2, Sandeep Samal4 and Sung Kyu Lim4 1Technology Research, GLOBALFOUNDRIES, Santa Clara, CA, USA 2Technology Development, GLOBALFOUNDRIES, Malta, NY, USA 3Department of ECE, University of Massachusetts, Amherst, MA, USA 4School of ECE, Georgia Institute of Technology, Atlanta, GA, USA [email protected], [email protected] Abstract In the paper, we investigate the optimum dimensions of Monolithic 3D IC (M3D) shows degradation in MIV, taking into account 3D cell footprint restrictions, ease performance compared to 2D IC due to the restricted thermal of manufacturability, and electrostatic coupling between top- budget during fabrication of sequential device layers. A and bot-tier. The 3D cells are designed following our 14nm transistor-level (TR-L) partitioning design is used in M3D to Finfet design rules and the MIV dimensions. The 3D cell RC mitigate this degradation. Silicon validated 14nm FinFET is extracted precisely by using CalibrexACT and Sentaurus data and models are used in a device-to-system evaluation to Interconnect. We performed cell performance evaluation in compare the TR-L partitioned M3D’s (TR-L M3D) various LT cases and quantified intra-cell capacitance performance against the conventional gate-level (G-L) reduction in 3D cells. We then extensively benchmarked partitioned M3D’s performance as well as standard 2D IC. system-level circuits to investigate both WB and LT Extensive cell-level and system-level evaluation, including process’s impacts on TR-L M3D’s system timing and the various device and interconnect process options, shows that timing-associated impact on power. -
AI Chips: What They Are and Why They Matter
APRIL 2020 AI Chips: What They Are and Why They Matter An AI Chips Reference AUTHORS Saif M. Khan Alexander Mann Table of Contents Introduction and Summary 3 The Laws of Chip Innovation 7 Transistor Shrinkage: Moore’s Law 7 Efficiency and Speed Improvements 8 Increasing Transistor Density Unlocks Improved Designs for Efficiency and Speed 9 Transistor Design is Reaching Fundamental Size Limits 10 The Slowing of Moore’s Law and the Decline of General-Purpose Chips 10 The Economies of Scale of General-Purpose Chips 10 Costs are Increasing Faster than the Semiconductor Market 11 The Semiconductor Industry’s Growth Rate is Unlikely to Increase 14 Chip Improvements as Moore’s Law Slows 15 Transistor Improvements Continue, but are Slowing 16 Improved Transistor Density Enables Specialization 18 The AI Chip Zoo 19 AI Chip Types 20 AI Chip Benchmarks 22 The Value of State-of-the-Art AI Chips 23 The Efficiency of State-of-the-Art AI Chips Translates into Cost-Effectiveness 23 Compute-Intensive AI Algorithms are Bottlenecked by Chip Costs and Speed 26 U.S. and Chinese AI Chips and Implications for National Competitiveness 27 Appendix A: Basics of Semiconductors and Chips 31 Appendix B: How AI Chips Work 33 Parallel Computing 33 Low-Precision Computing 34 Memory Optimization 35 Domain-Specific Languages 36 Appendix C: AI Chip Benchmarking Studies 37 Appendix D: Chip Economics Model 39 Chip Transistor Density, Design Costs, and Energy Costs 40 Foundry, Assembly, Test and Packaging Costs 41 Acknowledgments 44 Center for Security and Emerging Technology | 2 Introduction and Summary Artificial intelligence will play an important role in national and international security in the years to come. -
Design and Implementation of 6T Sram Using Finfet with Low Power Application
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 07 | July -2017 www.irjet.net p-ISSN: 2395-0072 DESIGN AND IMPLEMENTATION OF 6T SRAM USING FINFET WITH LOW POWER APPLICATION Jigyasa panchal1, Dr.Vishal Ramola2 1M.Tech Scholar, Dept. Of VLSI Design, F.O.T. Uttrakhand Technical University, Dehradun, India 2Asst.Prof. (H.O.D.), Dept. Of VLSI Design, F.O.T. Uttrakhand Technical University, Dehradun, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract- CMOS devices are facing many problems because time, memory storage system like SRAM suffers due to high the gate starts losing control over the channel. These problems occupancy of cache memory in the chip area as well as it also includes increase in leakage currents, increase of on current, suffers from maximum energy consumption of the chip increase in manufacturing cost, large variations in power [6]. parameters, less reliability and yield, short channel effects etc Since conventional CMOS is used to design SRAM, but it is also 1.1 CMOS BASED SRAM facing the problem of high power dissipation and increase in leakage current which affects its performance badly. Memories An SRAM cell is the key component storing a single bit of are required to have short access time, less power dissipation binary information. Memory circuits, mainly caches, are and low leakage current thus FINFET based SRAM cells are predicted to occupy more than 90% of the chip silicon area recommended over CMOS based SRAM cells. Reducing the in the foreseeable future. This makes its design and test to be leakage aspects of the SRAM cells has been very essential to robust without any room for errors. -
4K-Memristor Analog-Grade Passive Crossbar Circuit
H. Kim et al., Analog-Grade Passive Memristive Circuit, June 2019 4K-Memristor Analog-Grade Passive Crossbar Circuit H. Kim*,ϯ, H. Niliϯ, M. R. Mahmoodi, and D. B. Strukov*# Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106, USA The superior density of passive analog-grade memristive crossbars may enable storing large synaptic weight matrices directly on specialized neuromorphic chips, thus avoiding costly off- chip communication. To ensure efficient use of such crossbars in neuromorphic computing circuits, variations of current-voltage characteristics of crosspoint devices must be substantially lower than those of memory cells with select transistors. Apparently, this requirement explains why there were so few demonstrations of neuromorphic system prototypes using passive crossbars. Here we report a 64×64 passive metal-oxide memristor crossbar circuit with ~99% device yield, based on a foundry-compatible fabrication process featuring etch-down patterning and low-temperature budget, conducive to vertical integration. The achieved ~26% variations of switching voltages of our devices were sufficient for programming 4K-pixel gray-scale patterns with an average tuning error smaller than 4%. The analog properties were further verified by experimentally demonstrating MNIST pattern classification with a fidelity close to the software-modeled limit for a network of this size, with an ~1% average error of import of ex-situ-calculated synaptic weights. We believe that our work is a significant improvement over the state-of-the-art passive crossbar memories in both complexity and analog properties. Introduction Analog-grade nonvoltaile memories, such as those based on floating-gate transistor [1- 3], and phase-change [4-6], ferroelectric [7, 8], magnetic [9], solid-state electrolyte [10, 11], organic [12], and metal-oxide [13-21] materials, are enabling components for mixed-signal circuits implementing vector-by-martix multiplicaiton (VMM), which is the most common operation in any artificial neural network. -
Introducing 10-Nm Finfet Technology in Microwind Etienne Sicard
Introducing 10-nm FinFET technology in Microwind Etienne Sicard To cite this version: Etienne Sicard. Introducing 10-nm FinFET technology in Microwind. 2017. hal-01551695 HAL Id: hal-01551695 https://hal.archives-ouvertes.fr/hal-01551695 Submitted on 30 Jun 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. APPLICATION NOTE 10 nm technology Introducing 10-nm FinFET technology in Microwind Etienne SICARD Professor INSA-Dgei, 135 Av de Rangueil 31077 Toulouse – France www.microwind.org email: [email protected] This paper describes the implementation of a high performance FinFET-based 10-nm CMOS Technology in Microwind. New concepts related to the design of FinFET and design for manufacturing are also described. The performances of a ring oscillator layout and a 6-transistor RAM memory layout are also analyzed. 1. Technology Roadmap Several companies and research centers have released details on the 14-nm CMOS technology, as a major step for improved integration and performances, with the target of 7-nm process by 2020. We recall in -
Review Article Finfets: from Devices to Architectures
Hindawi Publishing Corporation Advances in Electronics Volume 2014, Article ID 365689, 21 pages http://dx.doi.org/10.1155/2014/365689 Review Article FinFETs: From Devices to Architectures Debajit Bhattacharya and Niraj K. Jha Department of Electrical Engineering, Princeton University, Princeton, NJ 08544, USA Correspondence should be addressed to Niraj K. Jha; [email protected] Received 4 June 2014; Accepted 23 July 2014; Published 7 September 2014 Academic Editor: Jaber Abu Qahouq Copyright © 2014 D. Bhattacharya and N. K. Jha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Since Moore’s law driven scaling of planar MOSFETs faces formidable challenges in the nanometer regime, FinFETs and Trigate FETs have emerged as their successors. Owing to the presence of multiple (two/three) gates, FinFETs/Trigate FETs are able to tackle short-channel effects (SCEs) better than conventional planar MOSFETs at deeply scaled technology nodes and thus enable continued transistor scaling. In this paper, we review research on FinFETs from the bottommost device level to the topmost architecture level. We survey different types of FinFETs, various possible FinFET asymmetries and their impact, and novel logic- level and architecture-level tradeoffs offered by FinFETs. We also review analysis and optimization tools that are available for characterizing FinFET devices, circuits, and architectures. 1. Introduction (), drain-induced barrier lowering (DIBL), and threshold voltage ( th) roll-off. Improvement in these metrics implies Relentless scaling of planar MOSFETs over the past four less degradation in the transistor’s th with continued scaling, decades has delivered ever-increasing transistor density and which in turn implies less degradation in off .