Bidirectional Photovoltage‐Driven Oxide Transistors for Neuromorphic Visual Sensors
Chenxing Jin, Jingwen Wang, Shenglan Yang, Yang Ding, Jianhui Chang, Wanrong Liu, Yunchao Xu, Xiaofang Shi, Pengshan Xie, Johnny C. Ho, Changjin Wan, Zijian Zheng, Jia Sun, Lei Liao, Junliang Yang
Abstract
Biological vision is one of the most important parts of the human perception system. However, emulating biological visuals is challenging because it requires complementary photoexcitation and photoinhibition. Here, the study presents a bidirectional photovoltage-driven neuromorphic visual sensor (BPNVS) that is constructed by monolithically integrating two perovskite solar cells (PSCs) with dual-gate ion-gel-gated oxide transistors. PSCs act as photoreceptors, converting external visual stimuli into electrical signals, whereas oxide transistors generate neuromorphic signal outputs that can be adjusted to produce positive and negative photoresponses. This device mimics the human vision system's ability to recognize colored and color-mixed patterns. The device achieves a static color recognition accuracy of 96% by utilizing the reservoir computing system for feature extraction. The BPNVS mem-reservoir chip is also proposed for handing object movement and dynamic color recognition. This work is a significant step forward in neuromorphic sensing and complex pattern recognition.