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Semiconductor Memory Devices and Circuits
This book covers semiconductor memory technologies from device bit-cell structures to memory array design with an emphasis on recent industry scaling trends and cutting-edge technologies. The first part of the book discusses the mainstream semiconductor memory technologies. The second part of the book discusses the emerging memory candidates that may have the potential to change the memory hierarchy, and surveys new applications of memory technologies for machine/deep learning applications. This book is intended for graduate students in electrical and computer engineering programs and researchers or industry professionals in semiconductors and microelectronics. Explains the design of basic m...
Low Substrate Temperature Modeling Outlook of Scaled n-MOSFET
Low substrate/lattice temperature (
Design Automation and Applications for Emerging Reconfigurable Nanotechnologies
This book is a single-source solution for anyone who is interested in exploring emerging reconfigurable nanotechnology at the circuit level. It lays down a solid foundation for circuits based on this technology having considered both manual as well as automated design flows. The authors discuss the entire design flow, consisting of both logic and physical synthesis for reconfigurable nanotechnology-based circuits. The authors describe how transistor reconfigurable properties can be exploited at the logic level to have a more efficient circuit design flow, as compared to conventional design flows suited for CMOS. Further, the book provides insights into hardware security features that can be intrinsically developed using the runtime reconfigurable features of this nanotechnology.
ETCMOS 2016 Vol.2: Devices, Circuits and Systems Track
Presentation slides from the Devices, Circuits and Systems track at the ETCMOS 2016 conference in Montreal, May 25-27, 2016
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.
Functional Metal Oxide Nanostructures
Metal oxides and particularly their nanostructures have emerged as animportant class of materials with a rich spectrum of properties and greatpotential for device applications. In this book, contributions from leadingexperts emphasize basic physical properties, synthesis and processing, and thelatest applications in such areas as energy, catalysis and data storage. Functional Metal Oxide Nanostructuresis an essential reference for any materials scientist or engineer with aninterest in metal oxides, and particularly in recent progress in defectphysics, strain effects, solution-based synthesis, ionic conduction, and theirapplications.
Non-Volatile In-Memory Computing by Spintronics
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.
