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Resistive Switching: Oxide Materials, Mechanisms, Devices and Operations
This book provides a broad examination of redox-based resistive switching memories (ReRAM), a promising technology for novel types of nanoelectronic devices, according to the International Technology Roadmap for Semiconductors, and the materials and physical processes used in these ionic transport-based switching devices. It covers defect kinetic models for switching, ReRAM deposition/fabrication methods, tuning thin film microstructures, and material/device characterization and modeling. A slate of world-renowned authors address the influence of type of ionic carriers, their mobility, the role of the local and chemical composition and environment, and facilitate readers’ understanding of ...
Applications of Emerging Memory Technology
The book intends to bring under one roof research work of leading groups from across the globe working on advanced applications of emerging memory technology nanodevices. The applications dealt in the text will be beyond conventional storage application of semiconductor memory devices. The text will deal with material and device physical principles that give rise to interesting characteristics and phenomena in the emerging memory device that can be exploited for a wide variety of applications. Applications covered will include system-centric cases such as – caches, NVSRAM, NVTCAM, Hybrid CMOS-RRAM circuits for: Machine Learning, In-Memory Computing, Hardware Security - RNG/PUF, Biosensing and other misc beyond storage applications. The book is envisioned for multi-purpose use as a textbook in advanced UG/PG courses and a research text for scientists working in the domain.
Memristors for Neuromorphic Circuits and Artificial Intelligence Applications
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 ap...
Springer Handbook of Semiconductor Devices
This Springer Handbook comprehensively covers the topic of semiconductor devices, embracing all aspects from theoretical background to fabrication, modeling, and applications. Nearly 100 leading scientists from industry and academia were selected to write the handbook's chapters, which were conceived for professionals and practitioners, material scientists, physicists and electrical engineers working at universities, industrial R&D, and manufacturers. Starting from the description of the relevant technological aspects and fabrication steps, the handbook proceeds with a section fully devoted to the main conventional semiconductor devices like, e.g., bipolar transistors and MOS capacitors and ...
Synaptic Plasticity for Neuromorphic Systems
One of the most striking properties of biological systems is their ability to learn and adapt to ever changing environmental conditions, tasks and stimuli. It emerges from a number of different forms of plasticity, that change the properties of the computing substrate, mainly acting on the modification of the strength of synaptic connections that gate the flow of information across neurons. Plasticity is an essential ingredient for building artificial autonomous cognitive agents that can learn to reliably and meaningfully interact with the real world. For this reason, the neuromorphic community at large has put substantial effort in the design of different forms of plasticity and in putting them to practical use. These plasticity forms comprise, among others, Short Term Depression and Facilitation, Homeostasis, Spike Frequency Adaptation and diverse forms of Hebbian learning (e.g. Spike Timing Dependent Plasticity). This special research topic collects the most advanced developments in the design of the diverse forms of plasticity, from the single circuit to the system level, as well as their exploitation in the implementation of cognitive systems.
Phase Change Memory
This book describes the physics of phase change memory devices, starting from basic operation to reliability issues. The book gives a comprehensive overlook of PCM with particular attention to the electrical transport and the phase transition physics between the two states. The book also contains design engineering details on PCM cell architecture, PCM cell arrays (including electrical circuit management), as well as the full spectrum of possible future applications.
Machine Learning and Non-volatile Memories
This book presents the basics of both NAND flash storage and machine learning, detailing the storage problems the latter can help to solve. At a first sight, machine learning and non-volatile memories seem very far away from each other. Machine learning implies mathematics, algorithms and a lot of computation; non-volatile memories are solid-state devices used to store information, having the amazing capability of retaining the information even without power supply. This book will help the reader understand how these two worlds can work together, bringing a lot of value to each other. In particular, the book covers two main fields of application: analog neural networks (NNs) and solid-state ...
Enabling Technologies for Very Large-Scale Synaptic Electronics
An important part of the colossal effort associated with the understanding of the brain involves using electronics hardware technology in order to reproduce biological behavior in ‘silico’. The idea revolves around leveraging decades of experience in the electronics industry as well as new biological findings that are employed towards reproducing key behaviors of fundamental elements of the brain (notably neurons and synapses) at far greater speed-scale products than any software-only implementation can achieve for the given level of modelling detail. So far, the field of neuromorphic engineering has proven itself as a major source of innovation towards the ‘silicon brain’ goal, with...
