Bio‐inspired artificial retina with receptive fields for in‐sensor multiply‐and‐accumulate operations

Abstract Bio‐inspired vision systems based on curved image sensors offer a compelling imaging hardware for mobile robots by geometrically matching the image plane to the Petzval surface of single‐lens optics. However, to fully exploit the advantages of biological vision systems, it is essential not only to emulate optical structures but also to integrate sensory‐level processing functions into vision hardware. Here, we propose a robotic vision system that leverages the structural advantages of the human eye (compact single‐lens imaging architecture) and the functional advantages of the biological receptive fields (sensory‐level information pre‐processing for efficient signal transmission and downstream computation). It is enabled by a bio‐inspired artificial retina composed of curved perovskite photoconductors that form artificial receptive fields (ARFs). Each ARF performs multiply‐and‐accumulate (MAC) operations through in‐sensor computing, executing sensory‐level image processing functions (e.g., edge detection). As a result, the artificial retina captures compact yet information‐rich edge images without external post‐processing, thereby improving the speed and energy efficiency of semantic segmentation. image