Self-Powered Optoelectronic Synaptic Devices for Neuromorphic Computing with the Lowest Energy Consumption Density
Wen Huang, Huixing Zhang, Jiawei Tang, Zhengjian Lin, Tenglong Guo, Yangming Zhou, Shaojie Jiang, Pengjie Hang, Mingzhi Jiao, Chen Zhu, Lei Wang, Deren Yang, Xuegong Yu, Xing’ao Li
Abstract
Recently, self-powered optoelectronic synaptic devices have attracted great attention due to their bias-free and self-rectifying properties for future computing systems. However, high energy consumption may still be required to generate optical signals for the stimulation of the systems. In this work, self-powered optoelectronic synaptic devices are fabricated based on triple mixed cation perovskites with excellent synaptic stability. Various synaptic functions are mimicked in these devices, which are stimulated by a fully visible light spectral range. These devices demonstrate the lowest energy consumption density and the best consistency properties of all reported self-powered optoelectronic synaptic devices to date. Color classification and speech recognition are successfully implemented with high accuracy in these systems. The results significantly promote the development of self-powered systems in neuromorphic computing.