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This book provides a comprehensive introduction to spintronics-based computing for the next generation of ultra-low power/highly reliable logic. It will cover aspects from device to system-level, including magnetic memory cells, device modeling, hybrid circuit structure, design methodology, CAD tools, and technological integration methods. This book is accessible to a variety of readers and little or no background in magnetism and spin electronics are required to understand its content. The multidisciplinary team of expert authors from circuits, devices, computer architecture, CAD and system design reveal to readers the potential of spintronics nanodevices to reduce power consumption, improve reliability and enable new functionality.
Book Synopsis Spintronics-based Computing by : Weisheng Zhao
Download or read book Spintronics-based Computing written by Weisheng Zhao and published by Springer. This book was released on 2015-05-11 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive introduction to spintronics-based computing for the next generation of ultra-low power/highly reliable logic. It will cover aspects from device to system-level, including magnetic memory cells, device modeling, hybrid circuit structure, design methodology, CAD tools, and technological integration methods. This book is accessible to a variety of readers and little or no background in magnetism and spin electronics are required to understand its content. The multidisciplinary team of expert authors from circuits, devices, computer architecture, CAD and system design reveal to readers the potential of spintronics nanodevices to reduce power consumption, improve reliability and enable new functionality.
Exa-scale computing needs to re-examine the existing hardware platform that can support intensive data-oriented computing. Since the main bottleneck is from memory, we aim to develop an energy-efficient in-memory computing platform in this book. First, the models of spin-transfer torque magnetic tunnel junction and racetrack memory are presented. Next, we show that the spintronics could be a candidate for future data-oriented computing for storage, logic, and interconnect. As a result, by utilizing spintronics, in-memory-based computing has been applied for data encryption and machine learning. The implementations of in-memory AES, Simon cipher, as well as interconnect are explained in details. In addition, in-memory-based machine learning and face recognition are also illustrated in this book.
Book Synopsis Non-Volatile In-Memory Computing by Spintronics by : Hao Yu
Download or read book Non-Volatile In-Memory Computing by Spintronics written by Hao Yu and published by Springer Nature. This book was released on 2022-05-31 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exa-scale computing needs to re-examine the existing hardware platform that can support intensive data-oriented computing. Since the main bottleneck is from memory, we aim to develop an energy-efficient in-memory computing platform in this book. First, the models of spin-transfer torque magnetic tunnel junction and racetrack memory are presented. Next, we show that the spintronics could be a candidate for future data-oriented computing for storage, logic, and interconnect. As a result, by utilizing spintronics, in-memory-based computing has been applied for data encryption and machine learning. The implementations of in-memory AES, Simon cipher, as well as interconnect are explained in details. In addition, in-memory-based machine learning and face recognition are also illustrated in this book.
Nanomagnetic and spintronic computing devices are strong contenders for future replacements of CMOS. This is an important and rapidly evolving area with the semiconductor industry investing significantly in the study of nanomagnetic phenomena and in developing strategies to pinpoint and regulate nanomagnetic reliably with a high degree of energy efficiency. This timely book explores the recent and on-going research into nanomagnetic-based technology. Key features: Detailed background material and comprehensive descriptions of the current state-of-the-art research on each topic. Focuses on direct applications to devices that have potential to replace CMOS devices for computing applications such as memory, logic and higher order information processing. Discusses spin-based devices where the spin degree of freedom of charge carriers are exploited for device operation and ultimately information processing. Describes magnet switching methodologies to minimize energy dissipation. Comprehensive bibliographies included for each chapter enabling readers to conduct further research in this field. Written by internationally recognized experts, this book provides an overview of a rapidly burgeoning field for electronic device engineers, field-based applied physicists, material scientists and nanotechnologists. Furthermore, its clear and concise form equips readers with the basic understanding required to comprehend the present stage of development and to be able to contribute to future development. Nanomagnetic and Spintronic Devices for Energy-Efficient Memory and Computing is also an indispensable resource for students and researchers interested in computer hardware, device physics and circuits design.
Book Synopsis Nanomagnetic and Spintronic Devices for Energy-Efficient Memory and Computing by : Jayasimha Atulasimha
Download or read book Nanomagnetic and Spintronic Devices for Energy-Efficient Memory and Computing written by Jayasimha Atulasimha and published by John Wiley & Sons. This book was released on 2016-03-07 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nanomagnetic and spintronic computing devices are strong contenders for future replacements of CMOS. This is an important and rapidly evolving area with the semiconductor industry investing significantly in the study of nanomagnetic phenomena and in developing strategies to pinpoint and regulate nanomagnetic reliably with a high degree of energy efficiency. This timely book explores the recent and on-going research into nanomagnetic-based technology. Key features: Detailed background material and comprehensive descriptions of the current state-of-the-art research on each topic. Focuses on direct applications to devices that have potential to replace CMOS devices for computing applications such as memory, logic and higher order information processing. Discusses spin-based devices where the spin degree of freedom of charge carriers are exploited for device operation and ultimately information processing. Describes magnet switching methodologies to minimize energy dissipation. Comprehensive bibliographies included for each chapter enabling readers to conduct further research in this field. Written by internationally recognized experts, this book provides an overview of a rapidly burgeoning field for electronic device engineers, field-based applied physicists, material scientists and nanotechnologists. Furthermore, its clear and concise form equips readers with the basic understanding required to comprehend the present stage of development and to be able to contribute to future development. Nanomagnetic and Spintronic Devices for Energy-Efficient Memory and Computing is also an indispensable resource for students and researchers interested in computer hardware, device physics and circuits design.
Using spin to replace or augment the role of charge in signal processing devices, computing systems and circuits may improve speed, power consumption, and device density in some cases—making the study of spinone of the fastest-growing areas in micro- and nanoelectronics. With most of the literature on the subject still highly advanced and heavily theoretical, the demand for a practical introduction to the concepts relating to spin has only now been filled. Explains effects such as giant magnetoresistance, the subject of the 2007 Nobel Prize in physics Introduction to Spintronics is an accessible, organized, and progressive presentation of the quantum mechanical concept of spin. The authors build a foundation of principles and equations underlying the physics, transport, and dynamics of spin in solid state systems. They explain the use of spin for encoding qubits in quantum logic processors; clarify how spin-orbit interaction forms the basis for certain spin-based devices such as spintronic field effect transistors; and discuss the effects of magnetic fields on spin-based device performance. Covers active hybrid spintronic devices, monolithic spintronic devices, passive spintronic devices, and devices based on the giant magnetoresistance effect The final chapters introduce the burgeoning field of spin-based reversible logic gates, spintronic embodiments of quantum computers, and other topics in quantum mechanics that have applications in spintronics. An Introduction to Spintronics provides the knowledge and understanding of the field needed to conduct independent research in spintronics.
Book Synopsis Introduction to Spintronics by : Supriyo Bandyopadhyay
Download or read book Introduction to Spintronics written by Supriyo Bandyopadhyay and published by CRC Press. This book was released on 2008-03-20 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using spin to replace or augment the role of charge in signal processing devices, computing systems and circuits may improve speed, power consumption, and device density in some cases—making the study of spinone of the fastest-growing areas in micro- and nanoelectronics. With most of the literature on the subject still highly advanced and heavily theoretical, the demand for a practical introduction to the concepts relating to spin has only now been filled. Explains effects such as giant magnetoresistance, the subject of the 2007 Nobel Prize in physics Introduction to Spintronics is an accessible, organized, and progressive presentation of the quantum mechanical concept of spin. The authors build a foundation of principles and equations underlying the physics, transport, and dynamics of spin in solid state systems. They explain the use of spin for encoding qubits in quantum logic processors; clarify how spin-orbit interaction forms the basis for certain spin-based devices such as spintronic field effect transistors; and discuss the effects of magnetic fields on spin-based device performance. Covers active hybrid spintronic devices, monolithic spintronic devices, passive spintronic devices, and devices based on the giant magnetoresistance effect The final chapters introduce the burgeoning field of spin-based reversible logic gates, spintronic embodiments of quantum computers, and other topics in quantum mechanics that have applications in spintronics. An Introduction to Spintronics provides the knowledge and understanding of the field needed to conduct independent research in spintronics.
The past few decades of research and development in solid-state semicon ductor physics and electronics have witnessed a rapid growth in the drive to exploit quantum mechanics in the design and function of semiconductor devices. This has been fueled for instance by the remarkable advances in our ability to fabricate nanostructures such as quantum wells, quantum wires and quantum dots. Despite this contemporary focus on semiconductor "quantum devices," a principal quantum mechanical aspect of the electron - its spin has it accounts for an added quan largely been ignored (except in as much as tum mechanical degeneracy). In recent years, however, a new paradigm of electronics based on the spin degree of freedom of the electron has begun to emerge. This field of semiconductor "spintronics" (spin transport electron ics or spin-based electronics) places electron spin rather than charge at the very center of interest. The underlying basis for this new electronics is the intimate connection between the charge and spin degrees of freedom of the electron via the Pauli principle. A crucial implication of this relationship is that spin effects can often be accessed through the orbital properties of the electron in the solid state. Examples for this are optical measurements of the spin state based on the Faraday effect and spin-dependent transport measure ments such as giant magneto-resistance (GMR). In this manner, information can be encoded in not only the electron's charge but also in its spin state, i. e.
Book Synopsis Semiconductor Spintronics and Quantum Computation by : D.D. Awschalom
Download or read book Semiconductor Spintronics and Quantum Computation written by D.D. Awschalom and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past few decades of research and development in solid-state semicon ductor physics and electronics have witnessed a rapid growth in the drive to exploit quantum mechanics in the design and function of semiconductor devices. This has been fueled for instance by the remarkable advances in our ability to fabricate nanostructures such as quantum wells, quantum wires and quantum dots. Despite this contemporary focus on semiconductor "quantum devices," a principal quantum mechanical aspect of the electron - its spin has it accounts for an added quan largely been ignored (except in as much as tum mechanical degeneracy). In recent years, however, a new paradigm of electronics based on the spin degree of freedom of the electron has begun to emerge. This field of semiconductor "spintronics" (spin transport electron ics or spin-based electronics) places electron spin rather than charge at the very center of interest. The underlying basis for this new electronics is the intimate connection between the charge and spin degrees of freedom of the electron via the Pauli principle. A crucial implication of this relationship is that spin effects can often be accessed through the orbital properties of the electron in the solid state. Examples for this are optical measurements of the spin state based on the Faraday effect and spin-dependent transport measure ments such as giant magneto-resistance (GMR). In this manner, information can be encoded in not only the electron's charge but also in its spin state, i. e.
In addition to the electron's charge, spintronics deals with the electron's spin and magnetic moment for computation or data storage. Certainly, an extremely promising application of spintronic devices is datastorage; the remanence makes the memory non-volatile and instant-on. Moreover, these devices are thermally stable making them suitable for extreme-temperature operations. In this monograph, we leverage spintronic devices for information processing and do not cover data-storage. We explore three non- Boolean computational framework: (1) Energy minimization based optimizer, which we recently published in Nature Nanotechnology [23], (2) Coupled Oscillatory framework [47] and (3) Neuromorphic learning framework. In Energy minimization framework, we harness the innate physical properties of nanomagnets to directly solve a class of energy minimization problems. Due to the fact that the Hamiltonian of a system of coupled nanomagnets is quadratic, a wide class of quadratic energy minimization can be solved much more quickly by the relaxation of a grid of nanomagnets than by a conventional Boolean processor. Another property that researchers have harnessed is achieving radio-frequency ferromagnetic resonance, which can be harnessed in a system of nano-oscillators to provide solution to dynamical systems. This property is also utilized in neuromorphic frameworks.
Book Synopsis Non-Boolean Computing with Spintronic Devices by : Kawsher A. Roxy
Download or read book Non-Boolean Computing with Spintronic Devices written by Kawsher A. Roxy and published by . This book was released on 2018 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: In addition to the electron's charge, spintronics deals with the electron's spin and magnetic moment for computation or data storage. Certainly, an extremely promising application of spintronic devices is datastorage; the remanence makes the memory non-volatile and instant-on. Moreover, these devices are thermally stable making them suitable for extreme-temperature operations. In this monograph, we leverage spintronic devices for information processing and do not cover data-storage. We explore three non- Boolean computational framework: (1) Energy minimization based optimizer, which we recently published in Nature Nanotechnology [23], (2) Coupled Oscillatory framework [47] and (3) Neuromorphic learning framework. In Energy minimization framework, we harness the innate physical properties of nanomagnets to directly solve a class of energy minimization problems. Due to the fact that the Hamiltonian of a system of coupled nanomagnets is quadratic, a wide class of quadratic energy minimization can be solved much more quickly by the relaxation of a grid of nanomagnets than by a conventional Boolean processor. Another property that researchers have harnessed is achieving radio-frequency ferromagnetic resonance, which can be harnessed in a system of nano-oscillators to provide solution to dynamical systems. This property is also utilized in neuromorphic frameworks.
Explore the cutting-edge of neuromorphic technologies with applications in Artificial Intelligence In Neuromorphic Devices for Brain-Inspired Computing: Artificial Intelligence, Perception, and Robotics, a team of expert engineers delivers a comprehensive discussion of all aspects of neuromorphic electronics designed to assist researchers and professionals to understand and apply all manner of brain-inspired computing and perception technologies. The book covers both memristic and neuromorphic devices, including spintronic, multi-terminal, and neuromorphic perceptual applications. Summarizing recent progress made in five distinct configurations of brain-inspired computing, the authors explore this promising technology’s potential applications in two specific areas: neuromorphic computing systems and neuromorphic perceptual systems. The book also includes: A thorough introduction to two-terminal neuromorphic memristors, including memristive devices and resistive switching mechanisms Comprehensive explorations of spintronic neuromorphic devices and multi-terminal neuromorphic devices with cognitive behaviors Practical discussions of neuromorphic devices based on chalcogenide and organic materials In-depth examinations of neuromorphic computing and perceptual systems with emerging devices Perfect for materials scientists, biochemists, and electronics engineers, Neuromorphic Devices for Brain-Inspired Computing: Artificial Intelligence, Perception, and Robotics will also earn a place in the libraries of neurochemists, neurobiologists, and neurophysiologists.
Book Synopsis Neuromorphic Devices for Brain-inspired Computing by : Qing Wan
Download or read book Neuromorphic Devices for Brain-inspired Computing written by Qing Wan and published by John Wiley & Sons. This book was released on 2022-05-16 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the cutting-edge of neuromorphic technologies with applications in Artificial Intelligence In Neuromorphic Devices for Brain-Inspired Computing: Artificial Intelligence, Perception, and Robotics, a team of expert engineers delivers a comprehensive discussion of all aspects of neuromorphic electronics designed to assist researchers and professionals to understand and apply all manner of brain-inspired computing and perception technologies. The book covers both memristic and neuromorphic devices, including spintronic, multi-terminal, and neuromorphic perceptual applications. Summarizing recent progress made in five distinct configurations of brain-inspired computing, the authors explore this promising technology’s potential applications in two specific areas: neuromorphic computing systems and neuromorphic perceptual systems. The book also includes: A thorough introduction to two-terminal neuromorphic memristors, including memristive devices and resistive switching mechanisms Comprehensive explorations of spintronic neuromorphic devices and multi-terminal neuromorphic devices with cognitive behaviors Practical discussions of neuromorphic devices based on chalcogenide and organic materials In-depth examinations of neuromorphic computing and perceptual systems with emerging devices Perfect for materials scientists, biochemists, and electronics engineers, Neuromorphic Devices for Brain-Inspired Computing: Artificial Intelligence, Perception, and Robotics will also earn a place in the libraries of neurochemists, neurobiologists, and neurophysiologists.
The book discusses almost all aspects of spintronics-based neuromorphic computing, starting from a very basic level, and will be of interest to both spintronics and neuromorphic computing communities. The chapters also cover most simulation and experimental studies reported recently by researchers worldwide on this topic. The book includes an introductory chapter on nanomagnetism and spin physics and another on neural network algorithms (covering both the machine-learning and neuroscience aspects of these algorithms). These introductory chapters will help the readers build their background and truly appreciate the recent research results on spintronics-based neuromorphic computing, presented in the later chapters of the book. Various numerical simulation exercises are also provided in the book.
Book Synopsis Spintronics-based Neuromorphic Computing by : Debanjan Bhowmik
Download or read book Spintronics-based Neuromorphic Computing written by Debanjan Bhowmik and published by Springer. This book was released on 2024-09-02 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book discusses almost all aspects of spintronics-based neuromorphic computing, starting from a very basic level, and will be of interest to both spintronics and neuromorphic computing communities. The chapters also cover most simulation and experimental studies reported recently by researchers worldwide on this topic. The book includes an introductory chapter on nanomagnetism and spin physics and another on neural network algorithms (covering both the machine-learning and neuroscience aspects of these algorithms). These introductory chapters will help the readers build their background and truly appreciate the recent research results on spintronics-based neuromorphic computing, presented in the later chapters of the book. Various numerical simulation exercises are also provided in the book.
In a new branch of physics and technology, called spin-electronics or spintronics, the flow of electrical charge (usual current) as well as the flow of electron spin, the so-called "spin current", are manipulated and controlled together. This book is intended to provide an introduction and guide to the new physics and applications of spin current.
Book Synopsis Spin Current by : Sadamichi Maekawa
Download or read book Spin Current written by Sadamichi Maekawa and published by Oxford University Press. This book was released on 2017 with total page 541 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a new branch of physics and technology, called spin-electronics or spintronics, the flow of electrical charge (usual current) as well as the flow of electron spin, the so-called "spin current", are manipulated and controlled together. This book is intended to provide an introduction and guide to the new physics and applications of spin current.
This book is the first comprehensive book about reservoir computing (RC). RC is a powerful and broadly applicable computational framework based on recurrent neural networks. Its advantages lie in small training data set requirements, fast training, inherent memory and high flexibility for various hardware implementations. It originated from computational neuroscience and machine learning but has, in recent years, spread dramatically, and has been introduced into a wide variety of fields, including complex systems science, physics, material science, biological science, quantum machine learning, optical communication systems, and robotics. Reviewing the current state of the art and providing a concise guide to the field, this book introduces readers to its basic concepts, theory, techniques, physical implementations and applications. The book is sub-structured into two major parts: theory and physical implementations. Both parts consist of a compilation of chapters, authored by leading experts in their respective fields. The first part is devoted to theoretical developments of RC, extending the framework from the conventional recurrent neural network context to a more general dynamical systems context. With this broadened perspective, RC is not restricted to the area of machine learning but is being connected to a much wider class of systems. The second part of the book focuses on the utilization of physical dynamical systems as reservoirs, a framework referred to as physical reservoir computing. A variety of physical systems and substrates have already been suggested and used for the implementation of reservoir computing. Among these physical systems which cover a wide range of spatial and temporal scales, are mechanical and optical systems, nanomaterials, spintronics, and quantum many body systems. This book offers a valuable resource for researchers (Ph.D. students and experts alike) and practitioners working in the field of machine learning, artificial intelligence, robotics, neuromorphic computing, complex systems, and physics.
Book Synopsis Reservoir Computing by : Kohei Nakajima
Download or read book Reservoir Computing written by Kohei Nakajima and published by Springer Nature. This book was released on 2021-08-05 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first comprehensive book about reservoir computing (RC). RC is a powerful and broadly applicable computational framework based on recurrent neural networks. Its advantages lie in small training data set requirements, fast training, inherent memory and high flexibility for various hardware implementations. It originated from computational neuroscience and machine learning but has, in recent years, spread dramatically, and has been introduced into a wide variety of fields, including complex systems science, physics, material science, biological science, quantum machine learning, optical communication systems, and robotics. Reviewing the current state of the art and providing a concise guide to the field, this book introduces readers to its basic concepts, theory, techniques, physical implementations and applications. The book is sub-structured into two major parts: theory and physical implementations. Both parts consist of a compilation of chapters, authored by leading experts in their respective fields. The first part is devoted to theoretical developments of RC, extending the framework from the conventional recurrent neural network context to a more general dynamical systems context. With this broadened perspective, RC is not restricted to the area of machine learning but is being connected to a much wider class of systems. The second part of the book focuses on the utilization of physical dynamical systems as reservoirs, a framework referred to as physical reservoir computing. A variety of physical systems and substrates have already been suggested and used for the implementation of reservoir computing. Among these physical systems which cover a wide range of spatial and temporal scales, are mechanical and optical systems, nanomaterials, spintronics, and quantum many body systems. This book offers a valuable resource for researchers (Ph.D. students and experts alike) and practitioners working in the field of machine learning, artificial intelligence, robotics, neuromorphic computing, complex systems, and physics.
