Reconfigurable, non-volatile neuromorphic photovoltaics

Abstract Reconfigurable image sensors for the recognition and understanding of real-world objects are now becoming an essential part of machine vision technology. The neural network image sensor — which mimics neurobiological functions of the human retina —has recently been demonstrated to simultaneously sense and process optical images. However, highly tunable responsivity concurrently with non-volatile storage of image data in the neural network would allow a transformative leap in compactness and function of these artificial neural networks (ANNs) that truly function like a human retina. Here, we demonstrate a reconfigurable and non-volatile neuromorphic device based on two-dimensional (2D) semiconducting metal sulfides (MoS 2 and WS 2 ) that is concurrently a photovoltaic detector. The device is based on a metal/semiconductor/metal (M/S/M) two-terminal structure with pulse-tunable sulfur vacancies at the M/S junctions. By modulating sulfur vacancy concentrations, the polarities of short-circuit photocurrent —can be changed with multiple stable magnitudes. Device characterizations and modeling reveal that the bias-induced motion of sulfur vacancies leads to highly reconfigurable responsivities by dynamically modulating the Schottky barriers. A convolutional neuromorphic network (CNN) is finally designed for image process and object detection using the same device. The results demonstrated the two-terminal reconfigurable and non-volatile photodetectors can be used for future optoelectronics devices based on coupled Ionic-optical-electronic effects for Neuromorphic computing.