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This book provides a comprehensive study of the research outcomes on memristor emulator circuits and includes various analog applications as examples. The authors describe in detail how to design different types of memristor emulators, using active and passive components for different applications. Most of the emulator circuits presented in this book are new and are the outcomes of the authors’ recent research. Coverage also includes the latest technological advances in memristor and memristor emulators. Readers will benefit from an understanding of the fundamental concepts and potential applications related to memristors, since these emulator circuits can be built in the laboratory using inexpensive, off-the-shelf circuit components. Introduces readers to memristor emulator circuit design, using regular off-the-shelf circuit components; Describes analog applications of memristors that can be verified by the proposed emulator circuits; Includes a brief overview of the updated mathematical models of the memristor device, with different material implementations; Equips readers to understand the three fingerprints of memristors, which make them unique, compared to the three known, passive elements (resistor, inductor and capacitor).
This chapter introduces a design guide of memristor emulator circuits, from conceptual idea until experimental tests. Three topologies of memristor emulator circuits in their incremental and decremental versions are analysed and designed at low and high frequency. The behavioural model of each topology is derived and programmed at SIMULINK under the MATLAB environment. An offset compensation technique is also described in order to achieve the frequency-dependent pinched hysteresis loop that is on the origin and when the memristor emulator circuit is operating at high frequency. Furthermore, from these topologies, a technique to transform normal non-linear resistors to inverse non-linear resistors is also addressed. HSPICE numerical simulations for each topology are also shown. Finally, three real analogue applications based on memristors are analysed and explained at the behavioural level of abstraction.
This book covers a range of models, circuits and systems built with memristor devices and networks in applications to neural networks. It is divided into three parts: (1) Devices, (2) Models and (3) Applications. The resistive switching property is an important aspect of the memristors, and there are several designs of this discussed in this book, such as in metal oxide/organic semiconductor nonvolatile memories, nanoscale switching and degradation of resistive random access memory and graphene oxide-based memristor. The modelling of the memristors is required to ensure that the devices can be put to use and improve emerging application. In this book, various memristor models are discussed, from a mathematical framework to implementations in SPICE and verilog, that will be useful for the practitioners and researchers to get a grounding on the topic. The applications of the memristor models in various neuromorphic networks are discussed covering various neural network models, implementations in A/D converter and hierarchical temporal memories.
Emerging memristor technology is drawing widespread attention during recent time due to its potential diverse applications in nanoelectronic memories, logic and neu romorphic computer architectures, digital and analog modulations system, and oscillator circuits. There has been a surge of interest to study memristor. However, as of now there is no single device available in the market that can truly exhibit the memristive be havior for a certain frequency range. Due to the absence of a real fabricated memristor, researchers are still relying on the memristor emulators to investigate the behavior and applications of memristor. Memristor emulator circuits are intended to understand and mimic the perceived behavior and properties of memristor. In addition, memristor has recently been recognized as a new and the fourth passive element that has the potential for many applications in digital, analog, and mixed signal domains. In the absence of a physical memristor, these emulator circuits would be of great importance to understand the fundamental concepts and potential applications related to memristor,because these emulator circuits can be built in the laboratory using in expensive off-the-shelf circuit components. One of the most widely used ideal memristor models developed by the HP Lab does not fit the anticipated nonlinear behavior of a real memristor. Therefore, this dissertation has proposed generic and practical emulator circuits for a current-controlled and voltage-controlled memristor, which can be used to mimic the behavior of the well-known memristor models like-Simmons Tunneling Barrier Model (STBM), ThrEshold Adaptive Memristor (TEAM) Model, and Voltage ThrEshold Adaptive Memristor (VTEAM) in addition to the simple Hewlett Packard (HP) model. Prior emulators can only emulate the linear electrical behavior. Moreover, the proposed emulator circuit development techniques can be configured for both floating and grounded models. In addition to the mathematical modeling and analysis of the proposed emulator, we provide SPICE simulation and experimental results. The analytical observations and the experimental results show that the proposed circuits can mimic the nonlinear behavior of real memristors for certain frequency range. Furthermore, the proposed emulator has been used to verify some applications like Wien bridge oscillator. Both series and parallel connectivity of the proposed emulator circuits have been studied experimentally. Finally, a brief comparison with the previously published emulators is presented to highlight the advantages of the proposed design.
Summary: "As memristors are not yet on the market, the development of memristor emulators and memristor based circuits is very important for real and practical engineering applications. The objectives of this book are to review the basic concepts of the memristor, describe state-of-the-art memristor based circuits and to stimulate further research and development in this area."--Preface.
This book reports on the latest advances in and applications of memristors, memristive devices and systems. It gathers 20 contributed chapters by subject experts, including pioneers in the field such as Leon Chua (UC Berkeley, USA) and R.S. Williams (HP Labs, USA), who are specialized in the various topics addressed in this book, and covers broad areas of memristors and memristive devices such as: memristor emulators, oscillators, chaotic and hyperchaotic memristive systems, control of memristive systems, memristor-based min-max circuits, canonic memristors, memristive-based neuromorphic applications, implementation of memristor-based chaotic oscillators, inverse memristors, linear memristor devices, delayed memristive systems, flux-controlled memristive emulators, etc. Throughout the book, special emphasis is given to papers offering practical solutions and design, modeling, and implementation insights to address current research problems in memristors, memristive devices and systems. As such, it offers a valuable reference book on memristors and memristive devices for graduate students and researchers with a basic knowledge of electrical and control systems engineering.
Artificial Intelligence (AI) has found many applications in the past decade due to the ever increasing computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses. Training these systems requires huge amounts of data and, after the network is trained, it can recognize unforeseen data and provide useful information. The so-called Spiking Neural Networks behave similarly to how the brain functions and are very energy efficient. Up to this moment, both spiking and conventional neural networks have been implemented in software programs running on conventional computing units. However, this approach requires high computing power, a large physical space and is energy inefficient. Thus, there is an increasing interest in developing AI tools directly implemented in hardware. The first hardware demonstrations have been based on CMOS circuits for neurons and specific communication protocols for synapses. However, to further increase training speed and energy efficiency while decreasing system size, the combination of CMOS neurons with memristor synapses is being explored. The memristor is a resistor with memory which behaves similarly to biological synapses. This book explores the state-of-the-art of neuromorphic circuits implementing neural networks with memristors for AI applications.
Nanoscale Memristor Device and Circuits Design provides theoretical frameworks, including (i) the background of memristors, (ii) physics of memristor and their modeling, (iii) menristive device applications, and (iv) circuit design for security and authentication. The book focuses on a broad aspect of realization of these applications as low cost and reliable devices. This is an important reference that will help materials scientists and engineers understand the production and applications of nanoscale memrister devices. A memristor is a two-terminal memory nanoscale device that stores information in terms of high/low resistance. It can retain information even when the power source is removed, i.e., "non-volatile." In contrast to MOS Transistors (MOST), which are the building blocks of all modern mobile and computing devices, memristors are relatively immune to radiation, as well as parasitic effects, such as capacitance, and can be much more reliable. This is extremely attractive for critical safety applications, such as nuclear and aerospace, where radiation can cause failure in MOST-based systems. Outlines the major principles of circuit design for nanoelectronic applications Explores major applications, including memristor-based memories, sensors, solar cells, or memristor-based hardware and software security applications Assesses the major challenges to manufacturing nanoscale memristor devices at an industrial scale
Using memristors one can achieve circuit functionalities that are not possible to establish with resistors, capacitors and inductors, therefore the memristor is of great pragmatic usefulness. Potential unique applications of memristors are in spintronic devices, ultra-dense information storage, neuromorphic circuits and programmable electronics. Memristor Networks focuses on the design, fabrication, modelling of and implementation of computation in spatially extended discrete media with many memristors. Top experts in computer science, mathematics, electronics, physics and computer engineering present foundations of the memristor theory and applications, demonstrate how to design neuromorphic network architectures based on memristor assembles, analyse varieties of the dynamic behaviour of memristive networks and show how to realise computing devices from memristors. All aspects of memristor networks are presented in detail, in a fully accessible style. An indispensable source of information and an inspiring reference text, Memristor Networks is an invaluable resource for future generations of computer scientists, mathematicians, physicists and engineers.
This book provides a comprehensive overview of current research on memristors, memcapacitors and, meminductors. In addition to an historical overview of the research in this area, coverage includes the theory behind memristive circuits, as well as memcapacitance, and meminductance. Details are shown for recent applications of memristors for resistive random access memories, neuromorphic systems and hybrid CMOS/memristor circuits. Methods for the simulation of memristors are demonstrated and an introduction to neuromorphic modeling is provided.