Non-pixelated in-materia retinomorphic sensor via photocarrier dynamics for precise spatiotemporal perception

Real-time perception of dynamic visual scenes requires efficient extraction of spatiotemporal features. However, conventional image sensors fail to capture inter-pixel correlations, leading to redundant data transfer, high power consumption and latency. Here, we present a non-pixelated in-materia retinomorphic sensor (IMRS) that exploits the intrinsic spatiotemporal dynamics and correlated distributions of photocarriers for visual information processing. Built on a large-area graphene/silicon heterostructure, the IMRS integrates circumferentially arranged sampling electrodes that harness the lateral photovoltaic effect to convert incident optical patterns into spatial carrier distributions, which are further encoded as object-shape-dependent photovoltages. Mimicking the lateral inhibition of biological retinas, this sensor enables in-sensor spatiotemporal perception without image reconstruction. We demonstrate human motion recognition with over 98% accuracy while compressing raw visual data from 10,000 to 48 bytes, reducing postprocessing networks parameters by two orders of magnitude. These results establish spatiotemporal photocarrier dynamics in low-dimensional heterostructures as a computational primitive for energy-efficient, ultralow-latency processing of high-dimensional spatiotemporal information.