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Micromachines
Special Issues
Resistive Switches: Understanding Device Mechanisms, Performance Enhancement, and Memory/Computing Applications
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Resistive Switches: Understanding Device Mechanisms, Performance Enhancement, and Memory/Computing Applications

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "E：Engineering and Technology".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 7636

Special Issue Editors

Prof. Dr. Linfeng Sun

E-Mail Website
Guest Editor
1. School of Physics, Beijing Institute of Technology, Beijing 102488, China
2. Key Lab of Advanced Optoelectronic Quantum, Architecture and Measurement, Ministry of Education, Beijing Institute of Technology, Beijing 102488, China
Interests: emerging low-dimensional materials; volatile and non-volatile memory device; neuromorphic computing; in-sensor computing
Prof. Dr. Zhongrui Wang

grade E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
Interests: resistive switching; RRAM; memristors; in-memory computing; processing-in-memory

Special Issue Information

Dear Colleagues,

Resistive switches are two-terminal electronic devices with tunable conductance that depends on the history of the applied external electric field. Such devices meet the general definition of a memristor, conceptualized by Prof. Leon Chua back in 1970s and discovered by HP lab in 2008. The resistive switches can be powered by various underlying physical mechanisms including redox reaction, phase transition, magnetoresistance, and ferroelectric tunneling resistance, among others. These devices feature superior scalability, 3D-stackability, and low fabrication cost, and bear a wide spectrum of applications to the next generation of nonvolatile memory (e.g., resistive random access memory or RRAM/ReRAM), in-memory processing to accelerate machine learning and neuromorphic computing, as well as cybersecurity. Such a novel type of device may pave the way toward future information processing systems with compact footprints and high energy efficiency. In this Special Issue, we seek to showcase research papers and review articles that focus on (1) fabrication, characterization, and modeling of resistive switches; and (2) circuit or system level applications involving resistive switches.

Prof. Dr. Linfeng Sun
Prof. Dr. Zhongrui Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Micromachines is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • resistive switch
  • memristor
  • RRAM
  • ReRAM
  • in-memory computing
  • processing-in-memory

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Published Papers (2 papers)

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Research

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12 pages, 2700 KiB  
Article
A High-Precision Implementation of the Sigmoid Activation Function for Computing-in-Memory Architecture
by Siqiu Xu, Xi Li, Chenchen Xie, Houpeng Chen, Cheng Chen and Zhitang Song
Micromachines 2021, 12(10), 1183; https://doi.org/10.3390/mi12101183 - 29 Sep 2021
Cited by 9 | Viewed by 2510
Abstract
Computing-In-Memory (CIM), based on non-von Neumann architecture, has lately received significant attention due to its lower overhead in delay and higher energy efficiency in convolutional and fully-connected neural network computing. Growing works have given the priority to researching the array of memory and [...] Read more.
Computing-In-Memory (CIM), based on non-von Neumann architecture, has lately received significant attention due to its lower overhead in delay and higher energy efficiency in convolutional and fully-connected neural network computing. Growing works have given the priority to researching the array of memory and peripheral circuits to achieve multiply-and-accumulate (MAC) operation, but not enough attention has been paid to the high-precision hardware implementation of non-linear layers up to now, which still causes time overhead and power consumption. Sigmoid is a widely used non-linear activation function and most of its studies provided an approximation of the function expression rather than totally matched, inevitably leading to considerable error. To address this issue, we propose a high-precision circuit implementation of the sigmoid, matching the expression exactly for the first time. The simulation results with the SMIC 40 nm process suggest that the proposed circuit implemented high-precision sigmoid perfectly achieves the properties of the ideal sigmoid, showing the maximum error and average error between the proposed simulated sigmoid and ideal sigmoid is 2.74% and 0.21%, respectively. In addition, a multi-layer convolutional neural network based on CIM architecture employing the simulated high-precision sigmoid activation function verifies the similar recognition accuracy on the test database of handwritten digits compared to utilize the ideal sigmoid in software, with online training achieving 97.06% and with offline training achieving 97.74%. Full article
(This article belongs to the Special Issue Resistive Switches: Understanding Device Mechanisms, Performance Enhancement, and Memory/Computing Applications)
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Review

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19 pages, 5652 KiB  
Review
Oscillator-Network-Based Ising Machine
by Yi Zhang, Yi Deng, Yinan Lin, Yang Jiang, Yujiao Dong, Xi Chen, Guangyi Wang, Dashan Shang, Qing Wang, Hongyu Yu and Zhongrui Wang
Micromachines 2022, 13(7), 1016; https://doi.org/10.3390/mi13071016 - 27 Jun 2022
Cited by 11 | Viewed by 4085
Abstract
With the slowdown of Moore’s law, many emerging electronic devices and computing architectures have been proposed to sustain the performance advancement of computing. Among them, the Ising machine is a non-von-Neumann solver that has received wide attention in recent years. It is capable [...] Read more.
With the slowdown of Moore’s law, many emerging electronic devices and computing architectures have been proposed to sustain the performance advancement of computing. Among them, the Ising machine is a non-von-Neumann solver that has received wide attention in recent years. It is capable of solving intractable combinatorial optimization (CO) problems, which are difficult to be solve using conventional digital computers. In fact, many CO problems can be mapped to finding the corresponding ground states of Ising model. At present, Ising machine prototypes based on different physical principles, such as emerging memristive oscillators, have been demonstrated, among which the Ising Hamiltonian solver based on the coupled oscillator network simultaneously holds the advantages of room-temperature operation, compact footprint, low power consumption, and fast speed to solution. This paper comprehensively surveys the recent developments in this important field, including the types of oscillators, the implementation principle of the Ising model, and the solver’s performance. Finally, methods to further improve the performance have also been suggested. Full article
(This article belongs to the Special Issue Resistive Switches: Understanding Device Mechanisms, Performance Enhancement, and Memory/Computing Applications)
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