Broadband Visual Adaption and Image Recognition in a Monolithic Neuromorphic Machine Vision System

Abstract

AbstractBio‐inspired machine visions have caused wide attentions due to the higher time/power efficiencies over the conventional architectures. Although bio‐mimic photo‐sensors and neuromorphic computing have been individually demonstrated, a complete monolithic vision system has rarely been studied. Here, a neuromorphic machine vision system (NMVS) integrating front‐end retinomorphic sensors and a back‐end convolutional neural network (CNN) based on a single ferroelectric‐semiconductor‐transistor (FST) device structure is reported. As a photo‐sensor, the FST shows a broadband (275–808 nm) retina‐like light adaption function with a large dynamic range of 20.3 stops, and as a unit of the CNN, the FST's weight can be linearly programmed. In total, the NMVS has a high recognition accuracy of 93.0% on a broadband‐dim‐image classification task, which is 20% higher than that of an incomplete system without the retinomorphic sensors. Because of the monolithic unit, the NVMS shows high feasibility for integrated bio‐inspired machine vision systems.