Three-terminal quantum dot light-emitting synapse with active adaptive photoelectric outputs for complex image processing/parallel computing

Abstract

Machine vision enables machines to extract rich information from image or video data and make intelligent decisions. However, approaches using artificial synapse hardware systems significantly limit the real-time and accuracy in machine vision segmentation amid complex environments. Addressing this, we propose a novel three-terminal adaptive artificial-light-emitting synapse (AALS) capable of photoelectric double output along with adaptive behavior. The device uses silver nanowires (AgNWs) as polar conductive bridges to reduce reliance on transparent electrodes, while polyvinyl alcohol (PVA) dielectric layers adaptively modulate charge carrier concentrations in conductive channels. Additionally, we have designed an adaptive parallel neural network (APNN) and applied it to autonomous driving image processing. This innovation significantly reduces adaptation time and notably enhances mean pixel accuracy (MPA) for semantic segmentation under overexposure and low-light conditions by 142.2% and 304.4%, respectively. Therefore, this work introduces new strategies for advanced adaptive vision, promising significant potential in intelligent driving and neuromorphic computing.