Multispectral In-Sensor Computing for Image Recognition Based on the Opposite Photogating Photosynapse
Yanni Zou, Yan Liu, Xiaolong Zhao, Yilin Wang, Yuxia Xin, Haoyan Zhan, Xiao Feng, Shunjie Yu, Weitao Ding, Zhitao Fu, Xiaohu Hou, Shibing Long
Abstract
Through the integration of sensing and computing functions into a single photosynapse, the neuromorphic visual system mitigates the substantial data redundancy caused by frequent data conversion and transmission in Von Neumann architectures. However, most reported photosynapses can produce unidirectional light responses only without electric modulation and are limited to narrow spectral ranges, which limits their effectiveness in target recognition in complex real-world optical scenes. Here, we present a four-color reservoir computing (RC) system based on an opposite photogating (OPG)-engineered multispectral photosynapse. The OPG effect, characterized by light-modulated oppositely shifted threshold voltage ( V th ), originates from different carrier dynamics in a Ga 2 O 3 /WSe 2 heterojunction field-effect transistor. Specifically, hole trapping in Ga 2 O 3 under deep ultraviolet (DUV) light induces negative V th shifts (excitatory responses), while electron trapping in WSe 2 under visible light causes positive V th shifts (inhibitory responses). The nonlinear photoresponse and tunable short-term memory under external light stimuli make the photosynapse suitable for photoelectric reservoirs. The DUV-specific corona discharge, a critical challenge in high-voltage transmission systems, causes exacerbated equipment aging and significant energy losses. By integration of DUV-specific discharge signals and visible environmental information, the system achieves 88.3% accuracy in localizing the corona discharge among six high-risk components in high-voltage systems. Our multispectral RC system demonstrates a pathway toward precise intelligent image recognition in real-world multispectral scenarios.