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.

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.

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.

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.