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Memristor Platforms for Pattern Recognition Memristor Theory, Systems and Applications Politecnico di Torino Porto Institutional Repository [Doctoral thesis] Memristor Platforms for Pattern Recognition Memristor Theory, Systems and Applications Original Citation: Secco, Jacopo (2017). Memristor Platforms for Pattern Recognition Memristor Theory, Systems and Applications. PhD thesis Availability: This version is available at : http://porto.polito.it/2680573/ since: September 2017 Published version: DOI:10.6092/polito/porto/2680573 Terms of use: This article is made available under terms and conditions applicable to Open Access Policy Article ("Public - All rights reserved") , as described at http://porto.polito.it/terms_and_conditions. html Porto, the institutional repository of the Politecnico di Torino, is provided by the University Library and the IT-Services. The aim is to enable open access to all the world. Please share with us how this access benefits you. Your story matters. (Article begins on next page) Doctoral Dissertation Doctoral Program in Electronic Engineering (29thcycle) Memristor Platforms for Pattern Recognition Memristor Theory, Systems and Applications By Jacopo Secco ****** Supervisor(s): Prof. Fernando Corinto, Supervisor Doctoral Examination Committee: Prof. Julius Georgiou, Referee, University of Cyprus Prof. Mauro Forti, Referee, Universitá degli Studi di Siena Prof. Salvatore Iannotta, IMEM-CNR Prof. Georgis Sirakoulis, Democritus University of Thrace Politecnico di Torino 2017 Declaration I hereby declare that, the contents and organization of this dissertation constitute my own original work and does not compromise in any way the rights of third parties, including those relating to the security of personal data. Jacopo Secco 2017 * This dissertation is presented in partial fulfillment of the requirements for Ph.D. degree in the Graduate School of Politecnico di Torino (ScuDo). A mia Mamma ed a mio Papá...ed a tutti quelli che hanno creduto come loro... Acknowledgements At the end of this journey, many are to thank for their precious work and help. At first I would like to acknowledge my Ph.D. supervisor Professor Fernando Corinto for his mentorship and his continuous technical, professional and personal support throughout the whole project. I would like to acknowledge also Professor Marco Gilli, Professor Emilio Paolucci, DET and the whole Institution for the support and the large number of growth possibilities given to my Ph.D. and research projects. I would also like to thank Professor Ronald Tetzlaff, Professor Danilo Demarchi, Professor Alon Ascoli, Professor Laryssa Baraban, Professor Kyeong-Sik Min, Professor Sandro Carrara, Professor Marius Orlowski, Doctor Matteo Fasano, Doctor Jens Muller, Doctor Julian Shutt, Doctor Juliane Simchen, and Doctor Abu Sebastian for their cooperation on the research projects brought on together. I would like to thank my team at Omnidermal: Marco Farina, Alberto Uberti, Piergiorgio Perotto, Paride Letizia, Vito Musci and Ubaldo Uberti for the work done and for their commitment to the goals that have been reached and the ones that still have to come. Same I would like to thank Doctor Elia Ricci for his clinical and scientific help. At last I would like to thank all my (now extended) family, especially my parents and my girlfriend Chiara for their unconditional love and affection. Thank you all. Abstract In the last decade a large scientific community has focused on the study ofthe memristor. The memristor is thought to be by many the best alternative to CMOS technology, which is gradually showing its flaws. Transistor technology has de- veloped fast both under a research and an industrial point of view, reducing the size of its elements to the nano-scale. It has been possible to generate more and more complex machinery and to communicate with that same machinery thanks to the development of programming languages based on combinations of boolean operands. Alas as shown by Moore’s law, the steep curve of implementation and of development of CMOS is gradually reaching a plateau. It is clear the need of studying new elements that can combine the efficiency of transistors and at the same time increase the complexity of the operations. Memristors can be described as non-linear resistors capable of maintaining memory of the resistance state that they reached. From their first theoretical treatment by Professor Leon O. Chua in 1971, different research groups have devoted their expertise in studying the both the fabrication and the implementation of this new promising technology. In the following thesis a complete study on memristors and memristive elements is presented. The road map that characterizes this study departs from a deep understanding of the physics that govern memristors, focusing on the HP model by Dr. Stanley Williams. Other devices such as phase change memories (PCMs) and memristive biosensors made with Si nano-wires have been studied, developing emulators and equivalent circuitry, in order to describe their complex dynamics. This part sets the first milestone of a pathway that passes trough more complex implementations such as neuromorphic systems and neural networks based on memristors proving their computing efficiency. Finally it will be presented a memristror-based technology, covered by patent, demonstrating its efficacy for clinical applications. The presented system has been designed for detecting and assessing automatically chronic wounds, a syndrome that affects roughly 2% of vi the world population, through a Cellular Automaton which analyzes and processes digital images of ulcers. Thanks to its precision in measuring the lesions the proposed solution promises not only to increase healing rates, but also to prevent the worsening of the wounds that usually lead to amputation and death. Contents 1 Introduction1 1.1 Description of the Work . .9 1.1.1 Contributions to the Work . .9 1.1.2 Author’s Contribution to Papers . 10 2 Memristor Theory: Device Modeling and Simulations 12 2.1 Original Description . 13 2.2 Flux-Charge Anlysis Method and Memristor Classification . 19 2.2.1 LM Circuit Class Introduction and Analysis . 22 2.2.2 Insight on Kirchoff’s Incremental Laws . 24 2.2.3 Memristor Classification Based on j and q ......... 25 2.3 Conductive Filament Formation and Rupturing in the HP Device . 29 2.3.1 General Overview of Memristor Models and HP Model Introduction . 29 2.3.2 Chua’s Constitutive Memristor Relations for William’s De- vice: Set Model . 33 2.3.3 Chua’s Constitutive Memristor Relations for William’s De- vice: Reset Model . 38 2.3.4 Rupturing Model Simulation Results . 44 2.3.5 Discussion and Conclusions . 50 2.4 j − q Modeling of Phase Change Memories . 53 viii Contents 2.4.1 Introduction to PCMs and Physical Description . 54 2.4.2 Memristor Model for PCM devices . 57 2.4.3 Derived Conclusions on PCMs as Memristors . 62 2.5 Memristive-RC Switching Dynamics of Si Nano-Wire Biosensors . 63 2.5.1 Electronic Approach Model . 66 2.5.2 SiNW Memristor Classification . 69 2.6 Memristor Emulator Circuit Based on Static Nonlinear Two-Ports and Dynamic Bipole . 71 2.6.1 Circuit Design and Analysis . 72 2.6.2 Spice Modeling and Simulation Results . 76 3 Memristors-Based Systems for Machine Learning 79 3.1 General Introduction to Perceptrons and SBPI . 80 3.1.1 Stochastic Belief Propagation Inspired Algoritm . 82 3.2 Memristor-Based SBPI Perceptron Design and Implementation . 85 3.2.1 Generalized Boundary Condition Memristor Model . 86 3.2.2 Memristor-Based Perceptron Circuit Design . 87 3.3 Functional and Learning Efficiency Essays on the Memristive Per- ceptron . 91 3.3.1 "Read" and "Write" Impulse Characterization . 93 3.3.2 Binary Synapse Threshold Characterization . 94 3.3.3 Perceptron Learning Efficiency Essays . 95 3.3.4 Statistical Analysis on the Learning Efficiency of the Memristor- Based SBPI Perceptron . 98 4 Memristive Systems for Clinical Assessment of Chronic Wounds 104 4.1 Clinical Overview . 105 4.2 Cellular Automaton and Discrete Time Cellular Neural Networks . 106 Contents ix 4.2.1 Cellular Automata and Stochastic Belief Propagation In- spired Perceptron Equivalence . 109 4.2.2 Element Detection in an RGB Image Trough Memristive SBPI-CA . 112 4.3 System Based on SBPI-CA for Chronic Wound Detection, Follow- Up and Assessment . 115 4.3.1 System Design . 117 4.3.2 Pre-Clinical Trials and Results . 123 5 Conclusion 129 References 131 List of Figures 142 List of Tables 151 Chapter 1 Introduction The memristor as a circuit element was first theoretically treated by professor Leon O. Chua in 1971, where he defined it as the fourth and missing circuit element [1]. Memristors where defined this way since they represent the missing link between the integral of the current named charge (q), and the integral of the voltage named flux (j) as shown in Figure 1.1, which also introduces its circuit symbol. Although Professor Chua introduced and deeply described the basis of this new element he also stated that, at that time, the discovery of the physical device was yet to be done. In 2008, at the Hp Laboratories, Dr. Stanley Williams was able to produce a first prototype of memristor [2] opening a new path for scientific development and discovery around the element, from device modeling and building, to implementation and application in complex systems. One example of scientific community that was created from these discoveries is the EU COST Anction IC1401, which has the scope to provide means of easy communication to researchers that are geographically dispersed around the world in order to reach ambitious and multidisciplinary goals. Recently the COST Action IC1401 has presented and interesting work of review, summarizing the most recent and significant advances in memristor theory and applications [3]. The applications for which memristors are intended are various, mostly focused on non-volatile memory, neuromorphic engineering and mixed-signal processing due to their properties. However, mostly in the field of non-volatile memories given that research has been predominantly driven towards that kind of industry, the majority of memristive devices explored exhibit non-volatile resistance states that can be 2 Introduction Fig.
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