Zhang Qi-Rui, Ouyang Wei-Lun, Wang Xue-Mei, Yang Fan, Chen Jian-Gang, Wen Zhi-Xing, Liu Jia-Xin, Wang Ge, Liu Qing, Liu Fu-Cai
Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313099, China.
School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
Nanoscale. 2024 Jul 25;16(29):13847-13860. doi: 10.1039/d4nr01445f.
Reservoir computing (RC) has attracted considerable attention for its efficient handling of temporal signals and lower training costs. As a nonlinear dynamic system, RC can map low-dimensional inputs into high-dimensional spaces and implement classification using a simple linear readout layer. The memristor exhibits complex dynamic characteristics due to its internal physical processes, which renders them an ideal choice for the implementation of physical reservoir computing (PRC) systems. This review focuses on PRC systems based on memristors, explaining the resistive switching mechanism at the device level and emphasizing the tunability of their dynamic behavior. The development of memristor-based reservoir computing systems is highlighted, along with discussions on the challenges faced by this field and potential future research directions.
储层计算（RC）因其对时间信号的高效处理和较低的训练成本而备受关注。作为一个非线性动态系统，RC可以将低维输入映射到高维空间，并使用简单的线性读出层实现分类。忆阻器由于其内部物理过程而呈现出复杂的动态特性，这使其成为实现物理储层计算（PRC）系统的理想选择。本综述聚焦于基于忆阻器的PRC系统，在器件层面解释电阻开关机制，并强调其动态行为的可调性。重点介绍了基于忆阻器的储层计算系统的发展，同时讨论了该领域面临的挑战以及未来潜在的研究方向。
