Multichannel meta-imager for parallel front-end optical computations

Abstract

Abstract Optical computing has demonstrated significant advantages over electronic computing, including parallelism, high-speed processing, extensive capacity, and low energy consumption. Optical computing front ends leveraging metasurfaces provide advantages such as miniaturization and seamless integration, but have a serious constraint of single computing functionality. Here, we propose a meta-imager, optical computing front end that integrates two coherent transfer functions corresponding to differential and integral convolution kernels into a built-in metasurface. In this architecture, the meta-imager enables parallel processing of multiple all-optical operations for signal computing tasks such as edge enhancement and denoising. We demonstrate the robust integral and differential operations on image signals of noisy patterns and onion cells at multiple visible wavelengths. This optical computing meta-imager paves a promising pathway towards multifunctional image processing for artificial intelligence and biological observation, and shows the potential to expedite and potentially supplant certain digital neural network algorithms.