Reconfigurable optoelectronic memristor for in-sensor computing applications

Abstract Inspired by human brain and visual system, optoelectronic memristors-based neuromorphic computing has attracted the interests of researchers to overcome the limitation of traditional von Neumann architecture. With advantages of highly parallel computing and massive interconnection, the optical memristors could construct light-inspired artificial neural network for neuromorphic computing tasks. Besides, nonvolatile optoelectronic memristors provide a promising path for reconfigurable logic operations, greatly promoting the development of novel in-memory computing technology. In this work, the photoelectric perception, storage and in situ computing functions were integrated in optoelectronic memristors array, which could greatly decrease the footprint of multifunctional device and improve the work efficiency of chip. The neuromorphic computing capability of the photonic memristors was verified using face images of different people with accuracy of 86.7%. Moreover, with the advantages of photoelectric cooperative modulation, the reconfigurable logic functions including “AND” and “OR” were achieved by optoelectronic memristors. The present results demonstrate the attractive bio-inspired in-sensor computing behaviors of the optoelectronic memristors, opening up potential applications of optoelectronic memristors in next-generation reconfigurable sensing-memory-computing integrated paradigms.