KU-KIST Graduate School of Converging Science and Technology, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.
Adv Mater. 2020 Dec;32(51):e2004659. doi: 10.1002/adma.202004659. Epub 2020 Oct 1.
Memristors have recently attracted significant interest due to their applicability as promising building blocks of neuromorphic computing and electronic systems. The dynamic reconfiguration of memristors, which is based on the history of applied electrical stimuli, can mimic both essential analog synaptic and neuronal functionalities. These can be utilized as the node and terminal devices in an artificial neural network. Consequently, the ability to understand, control, and utilize fundamental switching principles and various types of device architectures of the memristor is necessary for achieving memristor-based neuromorphic hardware systems. Herein, a wide range of memristors and memristive-related devices for artificial synapses and neurons is highlighted. The device structures, switching principles, and the applications of essential synaptic and neuronal functionalities are sequentially presented. Moreover, recent advances in memristive artificial neural networks and their hardware implementations are introduced along with an overview of the various learning algorithms. Finally, the main challenges of the memristive synapses and neurons toward high-performance and energy-efficient neuromorphic computing are briefly discussed. This progress report aims to be an insightful guide for the research on memristors and neuromorphic-based computing.
忆阻器最近因其在神经形态计算和电子系统中作为有前途的构建模块的应用而引起了极大的兴趣。忆阻器的动态重新配置基于施加的电刺激的历史，可以模拟基本的模拟突触和神经元功能。它们可以用作人工神经网络中的节点和终端设备。因此，理解、控制和利用忆阻器的基本开关原理和各种类型的器件结构对于实现基于忆阻器的神经形态硬件系统是必要的。本文重点介绍了用于人工突触和神经元的各种忆阻器和与忆阻器相关的器件。依次介绍了器件结构、开关原理以及基本突触和神经元功能的应用。此外，还介绍了忆阻人工神经网络及其硬件实现的最新进展，以及各种学习算法的概述。最后，简要讨论了忆阻器突触和神经元在高性能和高能效神经形态计算方面面临的主要挑战。本进展报告旨在为忆阻器和基于神经形态的计算研究提供有见地的指导。
