Neuromorphic Visual Computing with ZnMgO QDs‐Based UV‐Responsive Optoelectronic Synaptic Devices for Image Encryption and Recognition

Abstract

Retina-inspired optoelectronic neuromorphic devices integrating optical sensing and computation are the key components in realizing neuromorphic visual computing. In particular, UV-responsive optoelectronic synaptic devices hold significant value for advanced neuromorphic vision systems, as they can expand human visual perception. Herein, we demonstrate a UV-responsive optoelectronic synaptic device based on ZnMgO quantum dots (QDs) designed for in-sensor computing in neuromorphic vision applications. The device demonstrates voltage-driven short-term and long-term synaptic plasticity, as well as multiple photoinduced synaptic functions. Based on this device, an in-sensor image-blending encryption method has been designed, which can effectively reduce the risk of data leakage during transmission. Furthermore, an in-sensor reservoir computing (RC) system with image processing functions is constructed, which integrates a photonic reservoir layer (PRL) for image preprocessing and a multilayer perceptron (MLP) capable of image recognition. The system achieves 98.6% accuracy in recognizing Fashion-MNIST images and maintains 83% accuracy under 60% random noise, showcasing its robustness. This work introduces a novel approach for developing UV-responsive optoelectronic synaptic devices equipped with dual-mode modulation of both electrical and optical signals, offering new perspectives and solutions for integrated applications in neuromorphic vision systems.