In order to sustain Moore's Law-based device scaling, principal attention has focused on toward device architectural innovations for improved device performance as per ITRS projections for technology nodes up to 10 nm. Efficient integration of lower substrate temperatures (
This book aims at describing in detail the different layout techniques for remarkably boosting the electrical performance and the ionizing radiation tolerance of planar Metal-Oxide-Semiconductor (MOS) Field Effect Transistors (MOSFETs) without adding any costs to the current planar Complementary MOS (CMOS) integrated circuits (ICs) manufacturing processes. These innovative layout styles are based on pn junctions engineering between the drain/source and channel regions or simply MOSFET gate layout change. These interesting layout structures are capable of incorporating new effects in the MOSFET structures, such as the Longitudinal Corner Effect (LCE), the Parallel connection of MOSFETs with Different Channel Lengths Effect (PAMDLE), the Deactivation of the Parallel MOSFETs in the Bird's Beak Regions (DEPAMBBRE), and the Drain Leakage Current Reduction Effect (DLECRE), which are still seldom explored by the semiconductor and CMOS ICs industries. Several three-dimensional (3D) numerical simulations and experimental works are referenced in this book to show how these layout techniques can help the designers to reach the analog and digital CMOS ICs specifications with no additional cost. Furthermore, the electrical performance and ionizing radiation robustness of the analog and digital CMOS ICs can significantly be increased by using this gate layout approach.
RRAM technology has made significant progress in the past decade as a competitive candidate for the next generation non-volatile memory (NVM). This lecture is a comprehensive tutorial of metal oxide-based RRAM technology from device fabrication to array architecture design. State-of-the-art RRAM device performances, characterization, and modeling techniques are summarized, and the design considerations of the RRAM integration to large-scale array with peripheral circuits are discussed. Chapter 2 introduces the RRAM device fabrication techniques and methods to eliminate the forming process, and will show its scalability down to sub-10 nm regime. Then the device performances such as programming speed, variability control, and multi-level operation are presented, and finally the reliability issues such as cycling endurance and data retention are discussed. Chapter 3 discusses the RRAM physical mechanism, and the materials characterization techniques to observe the conductive filaments and the electrical characterization techniques to study the electronic conduction processes. It also presents the numerical device modeling techniques for simulating the evolution of the conductive filaments as well as the compact device modeling techniques for circuit-level design. Chapter 4 discusses the two common RRAM array architectures for large-scale integration: one-transistor-one-resistor (1T1R) and cross-point architecture with selector. The write/read schemes are presented and the peripheral circuitry design considerations are discussed. Finally, a 3D integration approach is introduced for building ultra-high density RRAM array. Chapter 5 is a brief summary and will give an outlook for RRAM’s potential novel applications beyond the NVM applications.
Silicon-on-Insulator (SOI) technology is widely used in high-performance and low-power semiconductor devices. The SOI wafers have two layers of active silicon (Si), and normally the bottom Si layer is a mere physical structure. The idea of making intelligent pixel detectors by using the bottom Si layer as sensors for X-ray, infrared light, high-energy particles, neutrons, etc. emerged from very early days of the SOI technology. However, there have been several difficult issues with fabricating such detectors and they have not become very popular until recently. This book offers a comprehensive overview of the basic concepts and research issues of SOI radiation image detectors. It introduces basic issues to implement the SOI detector and presents how to solve these issues. It also reveals fundamental techniques, improvement of radiation tolerance, applications, and examples of the detectors. Since the SOI detector has both a thick sensing region and CMOS transistors in a monolithic die, many ideas have emerged to utilize this technology. This book is a good introduction for people who want to develop or use SOI detectors.
As technologists, we are constantly exploring and pushing the limits of our own disciplines, and we accept the notion that the efficiencies of new technologies are advancing at a very rapid rate. However, we rarely have time to contemplate the broader impact of these technologies as they impact and amplify adjacent technology disciplines. This book therefore focuses on the potential impact of those technologies, but it is not intended as a technical manuscript. In this book, we consider our progress and current position %toward on arbitrary popular concepts of future scenarios rather than the typical measurements of cycles per second or milliwatts. We compare our current human cultural situation to other past historic events as we anticipate the future social impact of rapidly accelerating technologies. We also rely on measurements based on specific events highlighting the breadth of the impact of accelerating semiconductor technologies rather than the specific rate of advance of any particular semiconductor technology. These measurements certainly lack the mathematic precision and repeatability to which technologists are accustomed, but the material that we are dealing with—the social objectives and future political structures of humanity—does not permit a high degree of mathematic accuracy. Our conclusion draws from the concept of Singularity. It seems certain that at the rate at which our technologies are advancing, we will exceed the ability of our post‒Industrial Revolution structures to absorb these new challenges, and we cannot accurately anticipate what those future social structures will resemble.
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.
Plasma-based techniques are widely and successfully used across the field of materials processing, advanced nanosynthesis, and nanofabrication. The diversity of currently available processing architectures based on or enhanced by the use of plasmas is vast, and one can easily get lost in the opportunities presented by each of these configurations. This mini-book provides a concise outline of the most important concepts and architectures in plasma-assisted processing of materials, helping the reader navigate through the fundamentals of plasma system selection and optimization. Architectures discussed in this book range from the relatively simple, user-friendly types of plasmas produced using direct current, radio-frequency, microwave, and arc systems, to more sophisticated advanced systems based on incorporating and external substrate architectures, and complex control mechanisms of configured magnetic fields and distributed plasma sources.
The Bulletin of the Atomic Scientists is the premier public resource on scientific and technological developments that impact global security. Founded by Manhattan Project Scientists, the Bulletin's iconic "Doomsday Clock" stimulates solutions for a safer world.
Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size
