Li-Ion Doped Artificial Synaptic Memristor for Highly Linear Neuromorphic Computing

作者：Jialin Meng, Zhenhai Li, Yuqing Fang, Qingxuan Li, Zhenyu He, Tianyu Wang, Hao Zhu, Ji Li, Qingqing Sun, David Wei Zhang, Lin Chen · 发表于：IEEE Electron Device Letters · 年份：2022 · DOI：10.1109/led.2022.3211520 · 被引用次数：31 · 研究领域：Advanced Memory and Neural Computing、Photoreceptor and optogenetics research、Neuroscience and Neural Engineering

Linear weights modulation in neuromorphic memristor plays an important role in high-accuracy image recognition task. Herein, a Li+ doped organic artificial memristor for neuromorphic computing was proposed for linear weights update, which shows active ions diffusive dynamics as Ca2+ diffusion in biological synapse. The memristor exhibits gradual resistive switching, multi-state storage and typical synaptic behaviors. In addition, the synaptic learning capability of letter “T” was demonstrated in a memristors array. By designing consecutive pulse waveforms with enhanced amplitude, the linearity of memristor for weight update in long-term potentiation and depression (LTP/LTD) could be improved from 6.8 to 0.4. Based on the great nonlinearity factor in LTP ($\alpha _{\text {p}}={1.5}$) and LTD ($\alpha _{\text {d}}={0.4}$), face recognition was achieved with high accuracy of 96% by artificial neural network consisting of ion doped memristors. The ion doped organic memristor with highly linear weights update provides guidelines for the development of bio-inspired ion diffusive neuromorphic computing system.