Cheng Siqi, Zhong Lun, Yin Jinxiang, Duan Huan, Xie Qin, Luo Wenbo, Jie Wenjing
College of Chemistry and Materials Science, Sichuan Normal University, Chengdu, 610066, China.
State Key Laboratory of Electronic Thin Films and Integrated Devices, School of electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Nanoscale. 2023 Mar 9;15(10):4801-4808. doi: 10.1039/d2nr06580k.
Memristors with controllable resistive switching (RS) behavior have been considered as promising candidates for synaptic devices in next-generation neuromorphic computing. In this work, two-terminal memristors with controllable digital and analog RS behavior are fabricated based on two-dimensional (2D) WSe nanosheets. Under a relatively high operating voltage of 4 V, the memristor demonstrates stable and reliable non-volatile bipolar digital RS with a high switching ratio of 6.3 × 10. On the other hand, under a relatively low operation voltage, the memristor exhibits analog RS with a series of tunable resistance states. The fabricated memristors can work as an artificial synapse with fundamental synaptic functions, such as long-term potentiation (LTP) and depression (LTD) as well as paired-pulse facilitation (PPF). More importantly, the memristor demonstrates high conductance modulation linearity with the calculated nonlinear parameter for conductance as -0.82 in the LTP process, which is beneficial to improving the accuracy of neuromorphic computing. Furthermore, the neuromorphic computing of file types and image recognition can be emulated based on a constructed three-layer artificial neural network (ANN) with a recognition accuracy that can reach up to 95.9% for small digits. In addition, memristors can be used to emulate the learning-forgetting experience of the human brain. Consequently, the memristor based on 2D WSe nanosheets not only exhibits controllable RS behavior but also simulates synaptic functions and is expected to be a potential candidate for future neuromorphic computing applications.
具有可控电阻开关(RS)行为的忆阻器被认为是下一代神经形态计算中突触器件的有前途的候选者。在这项工作中,基于二维(2D)WSe纳米片制造了具有可控数字和模拟RS行为的两端忆阻器。在4V的相对较高工作电压下,忆阻器展示出稳定可靠的非易失性双极数字RS,开关比高达6.3×10。另一方面,在相对较低的工作电压下,忆阻器表现出具有一系列可调电阻状态的模拟RS。所制造的忆阻器可以作为具有基本突触功能的人工突触工作,如长时程增强(LTP)和抑制(LTD)以及双脉冲易化(PPF)。更重要的是,忆阻器在LTP过程中展示出高电导调制线性,计算出的电导非线性参数为-0.82,这有利于提高神经形态计算的精度。此外,基于构建的三层人工神经网络(ANN)可以模拟文件类型和图像识别的神经形态计算,对于小数字的识别准确率可达95.9%。此外,忆阻器可用于模拟人类大脑的学习-遗忘体验。因此,基于二维WSe纳米片的忆阻器不仅表现出可控的RS行为,还模拟了突触功能,有望成为未来神经形态计算应用的潜在候选者。