All-Optically Modulated In-Sensor Computing Device Based on Ionic-Conducting CuInP₂Se₆.

Abstract

Inspired by the human visual system, in-sensor computing has emerged as a promising approach to address growing demands for real-time image processing while overcoming constraints in computational resources. However, existing in-sensor computing optoelectronic devices still face challenges such as complex heterostructures or limited optical modulation for operational efficiency, restricting their practical use. Here, a simple two-terminal optoelectronic device has been fabricated using the 2D material CuInP₂Se₆, achieving neuromorphic functionalities through all-optical modulation. The device exhibits a tunable photoresponse across the visible spectrum (400 to 700 nm) and enables bidirectional conductance modulation in response to light stimuli, driven by the interaction between Cu⁺ ions and photogenerated electrons. It shows high linearity with 300 discrete conductance states under red, green, and blue light, enabling color-specific image feature extraction, processing, and recognition across three channels. This approach significantly enhances color image recognition accuracy by 4.6% when integrated with a three-channel convolutional neural network. Additionally, the bidirectional photoresponse allows for efficient noise suppression during color image preprocessing, leading to a 490% improvement in signal-to-noise ratio. These findings highlight the potential of CuInP₂Se₆-based architecture for robust performance, paving the way for in-sensor neuromorphic vision systems in artificial intelligence and biomimetic computing.