Zhu Yiyue, Huang Wen, He Yifei, Yin Lei, Zhang Yiqiang, Yang Deren, Pi Xiaodong
State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China.
School of Materials Science and Engineering, Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou, Henan 450001, China.
Research (Wash D C). 2020 Sep 2;2020:7538450. doi: 10.34133/2020/7538450. eCollection 2020.
Silicon- (Si-) based optoelectronic synaptic devices mimicking biological synaptic functionalities may be critical to the development of large-scale integrated optoelectronic artificial neural networks. As a type of important Si materials, Si nanocrystals (NCs) have been successfully employed to fabricate optoelectronic synaptic devices. In this work, organometal halide perovskite with excellent optical asborption is employed to improve the performance of optically stimulated Si-NC-based optoelectronic synaptic devices. The improvement is evidenced by the increased optical sensitivity and decreased electrical energy consumption of the devices. It is found that the current simulation of biological synaptic plasticity is essentially enabled by photogating, which is based on the heterojuction between Si NCs and organometal halide perovskite. By using the synaptic plasticity, we have simulated the well-known biased and correlated random-walk (BCRW) learning.
模仿生物突触功能的硅基光电突触器件对于大规模集成光电人工神经网络的发展可能至关重要。作为一种重要的硅材料，硅纳米晶体已成功用于制造光电突触器件。在这项工作中，具有优异光吸收性能的有机金属卤化物钙钛矿被用于提高光刺激的硅纳米晶体基光电突触器件的性能。器件光灵敏度的提高和电能消耗的降低证明了这种改进。研究发现，生物突触可塑性的电流模拟本质上是由光控实现的，光控基于硅纳米晶体与有机金属卤化物钙钛矿之间的异质结。通过使用突触可塑性，我们模拟了著名的有偏相关随机游走（BCRW）学习。
