The large scale integration and planar scaling of individual system chips is reaching an expensive limit. If individual chips now, and later terrabyte memory blocks, memory macros, and processing cores, can be tightly linked in optimally designed and processed small footprint vertical stacks, then performance can be increased, power reduced and cost contained. This book reviews for the electronics industry engineer, professional and student the critical areas of development for 3D vertical memory chips including: gate-all-around and junction-less nanowire memories, stacked thin film and double gate memories, terrabit vertical channel and vertical gate stacked NAND flash, large scale stacking of Resistance RAM cross-point arrays, and 2.5D/3D stacking of memory and processor chips with through-silicon-via connections now and remote links later. Key features: Presents a review of the status and trends in 3-dimensional vertical memory chip technologies. Extensively reviews advanced vertical memory chip technology and development Explores technology process routes and 3D chip integration in a single reference

Advances in Nonvolatile Memory and Storage Technology, Second Edition, addresses recent developments in the non-volatile memory spectrum, from fundamental understanding, to technological aspects. The book provides up-to-date information on the current memory technologies as related by leading experts in both academia and industry. To reflect the rapidly changing field, many new chapters have been included to feature the latest in RRAM technology, STT-RAM, memristors and more. The new edition describes the emerging technologies including oxide-based ferroelectric memories, MRAM technologies, and 3D memory. Finally, to further widen the discussion on the applications space, neuromorphic computing aspects have been included. This book is a key resource for postgraduate students and academic researchers in physics, materials science and electrical engineering. In addition, it will be a valuable tool for research and development managers concerned with electronics, semiconductors, nanotechnology, solid-state memories, magnetic materials, organic materials and portable electronic devices. - Discusses emerging devices and research trends, such as neuromorphic computing and oxide-based ferroelectric memories - Provides an overview on developing nonvolatile memory and storage technologies and explores their strengths and weaknesses - Examines improvements to flash technology, charge trapping and resistive random access memory

Advanced memory technologies are impacting the information era, representing a vibrant research area of huge interest in the electronics industry. The demand for data storage, computing performance and energy efficiency is increasing exponentially and will exceed the capabilities of current information technologies. Alternatives to traditional silicon technology and novel memory principles are expected to meet the need of modern data-intensive applications such as “big data” and artificial intelligence (AI). Functional materials or methodologies may find a key role in building novel, high speed and low power consumption computing and data storage systems. This book covers functional materials and devices in the data storage areas, alongside electronic devices with new possibilities for future computing, from neuromorphic next generation AI to in-memory computing. Summarizing different memory materials and devices to emphasize the future applications, graduate students and researchers can systematically learn and understand the design, materials characteristics, device operation principles, specialized device applications and mechanisms of the latest reported memory materials and devices.

This conference proceeding contains papers presented at the 6th International Conference on Machinery, Materials Science and Engineering Applications (MMSE 2016), held 28-30 October, 2016 in Wuhan, China. The conference proceeding contributions cover a large number of topics, both theoretical and applied, including Material science, Electrical Engineering and Automation Control, Electronic Engineering, Applied Mechanics, Mechanical Engineering, Aerospace Science and Technology, Computer Science and Information technology and other related engineering topics. MMSE provides a perfect platform for scientists and engineering researchers to exchange ideas, build cooperative relationships and discuss the latest scientific achievements. MMSE will be of interest for academics and professionals working in a wide range of industrial, governmental and academic sectors, including Material Science, Electrical and Electronic Engineering, Information Technology and Telecommunications, Civil Engineering, Energy Production, Manufacturing, Mechanical Engineering, Nuclear Engineering, Transportation and Aerospace Science and Technology.

A detailed, practical review of state-of-the-art implementations of memory in IoT hardware As the Internet of Things (IoT) technology continues to evolve and become increasingly common across an array of specialized and consumer product applications, the demand on engineers to design new generations of flexible, low-cost, low power embedded memories into IoT hardware becomes ever greater. This book helps them meet that demand. Coauthored by a leading international expert and multiple patent holder, this book gets engineers up to speed on state-of-the-art implementations of memory in IoT hardware. Memories for the Intelligent Internet of Things covers an array of common and cutting-edge IoT embedded memory implementations. Ultra-low-power memories for IoT devices-including plastic and polymer circuitry for specialized applications, such as medical electronics-are described. The authors explore microcontrollers with embedded memory used for smart control of a multitude of Internet devices. They also consider neuromorphic memories made in Ferroelectric RAM (FeRAM), Resistance RAM (ReRAM), and Magnetic RAM (MRAM) technologies to implement artificial intelligence (AI) for the collection, processing, and presentation of large quantities of data generated by IoT hardware. Throughout the focus is on memory technologies which are complementary metal oxide semiconductor (CMOS) compatible, including embedded floating gate and charge trapping EEPROM/Flash along with FeRAMS, FeFETs, MRAMs and ReRAMs. Provides a timely, highly practical look at state-of-the-art IoT memory implementations for an array of product applications Synthesizes basic science with original analysis of memory technologies for Internet of Things (IoT) based on the authors' extensive experience in the field Focuses on practical and timely applications throughout Features numerous illustrations, tables, application requirements, and photographs Considers memory related security issues in IoT devices Memories for the Intelligent Internet of Things is a valuable working resource for electrical engineers and engineering managers working in the electronics system and semiconductor industries. It is also an indispensable reference/text for graduate and advanced undergraduate students interested in the latest developments in integrated circuit devices and systems.

The new edition of the most detailed and comprehensive single-volume reference on major semiconductor devices The Fourth Edition of Physics of Semiconductor Devices remains the standard reference work on the fundamental physics and operational characteristics of all major bipolar, unipolar, special microwave, and optoelectronic devices. This fully updated and expanded edition includes approximately 1,000 references to original research papers and review articles, more than 650 high-quality technical illustrations, and over two dozen tables of material parameters. Divided into five parts, the text first provides a summary of semiconductor properties, covering energy band, carrier concentration, and transport properties. The second part surveys the basic building blocks of semiconductor devices, including p-n junctions, metal-semiconductor contacts, and metal-insulator-semiconductor (MIS) capacitors. Part III examines bipolar transistors, MOSFETs (MOS field-effect transistors), and other field-effect transistors such as JFETs (junction field-effect-transistors) and MESFETs (metal-semiconductor field-effect transistors). Part IV focuses on negative-resistance and power devices. The book concludes with coverage of photonic devices and sensors, including light-emitting diodes (LEDs), solar cells, and various photodetectors and semiconductor sensors. This classic volume, the standard textbook and reference in the field of semiconductor devices: Provides the practical foundation necessary for understanding the devices currently in use and evaluating the performance and limitations of future devices Offers completely updated and revised information that reflects advances in device concepts, performance, and application Features discussions of topics of contemporary interest, such as applications of photonic devices that convert optical energy to electric energy Includes numerous problem sets, real-world examples, tables, figures, and illustrations; several useful appendices; and a detailed solutions manual for Instructor's only Explores new work on leading-edge technologies such as MODFETs, resonant-tunneling diodes, quantum-cascade lasers, single-electron transistors, real-space-transfer devices, and MOS-controlled thyristors Physics of Semiconductor Devices, Fourth Edition is an indispensable resource for design engineers, research scientists, industrial and electronics engineering managers, and graduate students in the field.

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 drives (SSDs). After reviewing the basics of machine learning in Chapter 1, Chapter 2 shows how neural networks can mimic the human brain; to accomplish this result, neural networks have to perform a specific computation called vector-by-matrix (VbM) multiplication, which is particularly power hungry. In the digital domain, VbM is implemented by means of logic gates which dictate both the area occupation and the power consumption; the combination of the two poses serious challenges to the hardware scalability, thus limiting the size of the neural network itself, especially in terms of the number of processable inputs and outputs. Non-volatile memories (phase change memories in Chapter 3, resistive memories in Chapter 4, and 3D flash memories in Chapter 5 and Chapter 6) enable the analog implementation of the VbM (also called “neuromorphic architecture”), which can easily beat the equivalent digital implementation in terms of both speed and energy consumption. SSDs and flash memories are strictly coupled together; as 3D flash scales, there is a significant amount of work that has to be done in order to optimize the overall performances of SSDs. Machine learning has emerged as a viable solution in many stages of this process. After introducing the main flash reliability issues, Chapter 7 shows both supervised and un-supervised machine learning techniques that can be applied to NAND. In addition, Chapter 7 deals with algorithms and techniques for a pro-active reliability management of SSDs. Last but not least, the last section of Chapter 7 discusses the next challenge for machine learning in the context of the so-called computational storage. No doubt that machine learning and non-volatile memories can help each other, but we are just at the beginning of the journey; this book helps researchers understand the basics of each field by providing real application examples, hopefully, providing a good starting point for the next level of development.