International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

Modern computers are based on the von Neumann architecture in which computation and storage are physically separated: data are fetched from the memory unit, shuttled to the processing unit (where computation takes place) and then shuttled back to the memory unit to be stored. The rate at which data can be transferred between the processing unit and the memory unit represents a fundamental limitation of modern computers, known as the memory wall. In-memory computing is an approach that attempts to address this issue by designing systems that compute within the memory, thus eliminating the energy-intensive and time-consuming data movement that plagues current designs. Here we review the development of in-memory computing using resistive switching devices, where the two-terminal structure of the devices, their resistive switching properties, and direct data processing in the memory can enable area- and energy-efficient computation. We examine the different digital, analogue, and stochastic computing schemes that have been proposed, and explore the microscopic physical mechanisms involved. Finally, we discuss the challenges in-memory computing faces, including the required scaling characteristics, in delivering next-generation computing. This Review Article examines the development of in-memory computing using resistive switching devices.

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In-memory computing with resistive switching devices

Traditional von Neumann computing systems involve separate processing and memory units. However, data movement is costly in terms of time and energy and this problem is aggravated by the recent explosive growth in highly data-centric applications related to artificial intelligence. This calls for a radical departure from the traditional systems and one such non-von Neumann computational approach is in-memory computing. Hereby certain computational tasks are performed in place in the memory itself by exploiting the physical attributes of the memory devices. Both charge-based and resistance-based memory devices are being explored for in-memory computing. In this Review, we provide a broad overview of the key computational primitives enabled by these memory devices as well as their applications spanning scientific computing, signal processing, optimization, machine learning, deep learning and stochastic computing. This Review provides an overview of memory devices and the key computational primitives for in-memory computing, and examines the possibilities of applying this computing approach to a wide range of applications.

Memory devices and applications for in-memory computing

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

Resistive switching (RS) is an interesting property shown by some materials systems that, especially during the last decade, has gained a lot of interest for the fabrication of electronic devices, with electronic nonvolatile memories being those that have received the most attention. The presence and quality of the RS phenomenon in a materials system can be studied using different prototype cells, performing different experiments, displaying different figures of merit, and developing different computational analyses. Therefore, the real usefulness and impact of the findings presented in each study for the RS technology will be also different. This manuscript describes the most recommendable methodologies for the fabrication, characterization, and simulation of RS devices, as well as the proper methods to display the data obtained. The idea is to help the scientific community to evaluate the real usefulness and impact of an RS study for the development of RS technology. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

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Recommended Methods to Study Resistive Switching Devices

This article is a comprehensive review of the state-of-theart of memristor-based logic circuit design concepts of the recent literature. Amongst all the identified circuit design approaches, those discussed here are all based on collective memristive dynamics and share a number of common characteristics which facilitate their comparison. The focus is on the evolution of the memristor-based logic circuit design strategies from the early proposed sequential stateful logic up to most recently published design schemes which support parallel processing of the applied input signals. The main operational properties of all the selected computational concepts are presented in an accessible manner, aiming to serve as an informative cornerstone for students and scientists who wish to get involved in emerging memristive logic circuit research and development.

Emerging Memristor-Based Logic Circuit Design Approaches: A Review

Over 30 years ago L. Chua proposed the existence of a new class of passive circuit elements, which he called memristors and memristive devices. The unique electrical characteristics associated with them, along with the advantages of crossbar structures, have the potential to revolutionize computing architectures. A well-defined and effective memristor model for circuit design combined with a design paradigm based on well-understood underlying logic design principles would certainly accelerate research on nanoscale circuits and systems. Toward this goal, we propose a memristor crossbar circuit design paradigm in which memristors are modeled using the quantum mechanical phenomenon of tunneling. We use this circuit model to design and simulate various logic circuit designs capable of universal computation. Finally, we develop and present a new design paradigm for memristor-based crossbar circuits.

A Novel Design and Modeling Paradigm for Memristor-Based Crossbar Circuits

Memristor-based nanoelectronic computing circuits and architectures

The recent discovery of the 'modern' memristor has drawn great attention of both academia and industry. Given their favorable performance merits, memristors are expected to play a fundamental role in electronic industry. Modeling of memristive devices is essential for circuit design, and a number of Simulation Program with Integrated Circuit Emphasis SPICE models have already been introduced. The common problem in most models is that there is no threshold consideration; hence, only a few address the nonlinear nature of the device. This paper aims to present a SPICE implementation of a threshold-type switching model of a voltage-controlled memristive device that attributes the switching effect to a tunneling distance modulation. Threshold-type switching is closer to the actual behavior of most experimentally realizable memristive systems, and our modeling approach addresses the issue of programming thresholds. Both the netlist and the simple schematic are provided, thus making it easy to comprehend and ready to be used. Compared with other modeling solutions, it involves significantly low-complexity operation under an unlimited set of frequencies, and its simulation results are in good qualitative and quantitative agreement with the theoretical formulation. The proposed model is used to simulate an antiserial memristive switch, proving that it can be efficiently introduced in complex memristive circuits. Copyright © 2013 John Wiley & Sons, Ltd.

SPICE modeling of nonlinear memristive behavior

This brief contributes to the design of computational and reconfigurable structures that exploit unique threshold-dependent switching response of single memristors and their compositions. A new logic circuit design paradigm, which assumes parallel processing of input signals, is proposed, along with a methodology for the construction of robust programmable composite memristive switches of variable precision. This methodology is applied to the design of memristive computing circuits. A SPICE simulation-based validation of the proposed circuits and systems is provided.

Ioannis Vourkas

Papers

Emerging Memristor-Based Logic Circuit Design Approaches: A Review

A Novel Design and Modeling Paradigm for Memristor-Based Crossbar Circuits

Memristor-based nanoelectronic computing circuits and architectures

SPICE modeling of nonlinear memristive behavior

Boolean Logic Operations and Computing Circuits Based on Memristors