Light‐Stimulated Synaptic Transistor with High PPF Feature for Artificial Visual Perception System Application

作者：Chao Han, Xingwei Han, Jiayue Han, Meiyu He, Silu Peng, Chaoyi Zhang, Xianchao Liu, Jun Gou, Jun Wang · 发表于：Advanced Functional Materials · 年份：2022 · DOI：10.1002/adfm.202113053 · 被引用次数：224 · 研究领域：Advanced Memory and Neural Computing、Photoreceptor and optogenetics research、Neural Networks and Reservoir Computing

Abstract Optoelectronic synaptic devices, which combine the functions of photosensitivity and information processing, are essential for the development of artificial visual perception systems. Nevertheless, improving the paired pulse facilitation (PPF) index of optoelectronic synaptic devices, which is an urgent problem in the construction of high‐precision artificial visual perception systems, has received less attention so far. Herein, a light‐stimulated synaptic transistor (LSST) device with an ultra‐high PPF index ( ≈ 196%) is presented by introducing an ultra‐thin carrier regulator layer hexagonal boron nitride (h‐BN) into a classic graphene‐based hybrid transistor frame (graphene/CsPbBr 3 quantum dots). Crucially, analysis of the rate‐limiting effect of h‐BN on photogenerated carriers reveals the mechanism behind the LSST ultra‐high PPF index. Furthermore, a two‐layer artificial neural network connected by LSST devices demonstrate ≈ 91.5% recognition accuracy of handwritten digits. This work provides an effective method for constructing artificial visual perception systems using a hybrid transistor frame in the future.