Photonic Synaptic Transistor with Memory Mode Switching for Neuromorphic Visual System
Chao Han, Jiayue Han, Meiyu He, Xingwei Han, Zhiming Wu, He Yu, Jun Gou, Jun Wang
Abstract
Abstract The human retina is able to extract key feature information from a large amount of redundant visual information, which is the basis for efficient information processing in the human visual system. However, current retina‐inspired photonic synaptic devices lack fast noise filtering capabilities, limiting the speed of image preprocessing in neuromorphic visual systems. Here, a photonic synaptic transistor (PST) based on graphene/organic heterojunction that exhibits high photosensitivity and optically tunable synaptic characteristics from visible to near‐infrared (488–1310 nm) is demonstrated. The PST enables light‐intensity‐controlled memory‐free and long‐memory mode switching, allowing to achieve fast image noise filtering in a PST‐based vision sensor (processing times as low as 30 ms). In addition, image recognition in an artificial neural network connected by the PST, and the efficiency and accuracy of image recognition can be significantly improved by performing image noise filtering at the front‐end is demonstrated. This work provides the potential to improve the information processing speed of bio‐inspired neuromorphic visual systems and contribute to the development of machine vision applications.