In-situ artificial retina with all-in-one reconfigurable photomemristor networks
Yichen Cai, Yizhou Jiang, Chenxu Sheng, Zhiyong Wu, Luqiu Chen, Bobo Tian, Chun‐Gang Duan, Shisheng Xiong, Yiqiang Zhan, Chunxiao Cong, Zhi‐Jun Qiu, Yajie Qin, Ran Liu, Laigui Hu
Abstract
Abstract Despite that in-sensor processing has been proposed to remove the latency and energy consumption during the inevitable data transfer between spatial-separated sensors, memories and processors in traditional computer vision, its hardware implementation for artificial neural networks (ANNs) with all-in-one device arrays remains a challenge, especially for organic-based ANNs. With the advantages of biocompatibility, low cost, easy fabrication and flexibility, here we implement a self-powered in-sensor ANN using molecular ferroelectric (MF)-based photomemristor arrays. Tunable ferroelectric depolarization was intentionally introduced into the ANN, which enables reconfigurable conductance and photoresponse. Treating photoresponsivity as synaptic weight, the MF-based in-sensor ANN can operate analog convolutional computation, and successfully conduct perception and recognition of white-light letter images in experiments, with low processing energy consumption. Handwritten Chinese digits are also recognized and regressed by a large-scale array, demonstrating its scalability and potential for low-power processing and the applications in MF-based in-situ artificial retina.