Binary Neural Network Based on a Programmable Graphene/Si Schottky Diode for In-Sensor Processing Image Sensors

Recent advancements in in-sensor computing technology have demonstrated significant advantages in time latency and energy efficiency in visual information processing through device-level integration of photosensing and neuromorphic computing. However, current implementations face challenges due to their single-layer architecture, creating an urgent demand for the development of devices that integrate front-end in-sensor processing with back-end computing layers. Here, we report a programmable graphene/Si Schottky diode (PGSSD) featuring gate-voltage-programmed photoresponsivity and rectification direction. The programmability of the photoresponsivity enables the application of reconfigurable convolution kernels to implement in-sensor convolution of optical images. Simultaneously, the programmable rectification direction permits analog-domain execution of quasi-binary multiply-accumulate (MAC) operations. Based on these capabilities, we constructed a complete binary neural network (BNN) using the PGSSDs and demonstrated its application for image recognition. The BNN combines front-end convolution processing and back-end computing layers, achieving an inference accuracy of 98.35% on the MNIST database.