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

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.