Journal Article

Ultracompact meta-imagers for arbitrary all-optical convolution

Zhao, Dong, Liu, Songde, Tian, Chao, Fu Weiwei, Li Ziqin, Huang, Kun

2022

Overview

Electronic digital convolutions could extract key features of objects for data processing and information identification in artificial intelligence, but they are time-cost and energy consumption due to the low response of electrons. Although massless photons enable high-speed and low-loss analog convolutions, two existing all-optical approaches including Fourier filtering and Green’s function have either limited functionality or bulky volume, thus restricting their applications in smart systems. Here, we report all-optical convolutional computing with a metasurface-singlet or -doublet imager, considered as the third approach, where its point spread function is modified arbitrarily via a complex-amplitude meta-modulator that enables functionality-unlimited kernels. Beyond one- and two-dimensional spatial differentiation, we demonstrate real-time, parallel, and analog convolutional processing of optical and biological specimens with challenging pepper-salt denoising and edge enhancement, which significantly enrich the toolkit of all-optical computing. Such meta-imager approach bridges multi-functionality and high-integration in all-optical convolutions, meanwhile possessing good architecture compatibility with digital convolutional neural networks.An ultra-compact metasurface-based imager with modified point spread function is demonstrated to realize arbitrary all-optical parallel picture convolution that is highly compatible to convolutional neural network.

• Publisher: Springer Nature B.V
• Subject: Artificial intelligence / Information processing / Neural networks / Photons