Multichannel meta-imagers based on electrically tunable metasurfaces for accelerating matrix operations.

Optical neural networks have facilitated groundbreaking advancements in machine vision, intelligent healthcare, and autonomous driving systems. However, fixed optical network architectures encounter a dramatic challenge in achieving large-scale parameter tuning without necessitating modifications to the physical components, thereby limiting their capacity to handle intricate application scenarios. Here, a meta-imager based on tunable metasurfaces is proposed, which acts as an optoelectronic front end in collaboration with a digital back end to transfer computationally intensive convolution operations to super integration, highly parallel optical devices.