Ferroelectric-Configured In-Sensor Dynamic Computing with 2D Perovskites for Dim Object Recognition.

Abstract

Machine vision systems face significant challenges in accurately extracting critical features from dim objects under complex scenarios. Here, we demonstrate a ferroelectric-configured weight-reconfigurable photovoltaic device array for in-sensor dynamic computing, enabling robust recognition of dim objects. A series of 2D perovskite ferroelectric nanoplates with controllable size, high crystallinity, and excellent yield are directly synthesized. Reconfigurable and nonvolatile photovoltaics in a graphene/ferroelectric/graphene heterostructure are modulated through switchable ferroelectric polarization. Leveraging the ferroelectric-configured photoresponsivity, a convolution kernel optoelectronic sensor array with dynamic correlation of adjacent units is designed for in-sensor dynamic computing. Compared with traditional static optoelectronic convolution processing, our approach selectively amplifies subtle differences of local image pixels, enabling effective edge feature extraction even in low-contrast scenes. Integrated with a convolutional neural network, the system significantly enhances the robustness and accuracy of dim object detection, offering a promising platform for advanced machine vision applications.