Deep learning-enabled compact optical trigonometric operator with metasurface

In this paper, a novel strategy based on a metasurface composed of simple and compact unit cells to achieve ultra-high-speed trigonometric operations under specific input values is theoretically and experimentally demonstrated. An electromagnetic wave (EM)-based optical diffractive neural network with only one hidden layer is physically built to perform four trigonometric operations (sine, cosine, tangent, and cotangent functions). Under the unique composite input mode strategy, the designed optical trigonometric operator responds to incident light source modes that represent different trigonometric operations and input values (within one period), and generates correct and clear calculated results in the output layer. Such a wave-based operation is implemented with specific input values, and the proposed concept work may offer breakthrough inspiration to achieve integrable optical computing devices and photonic signal processors with ultra-fast running speeds.

Automated summary

This paper demonstrates a novel strategy based on a metasurface composed of simple and compact unit cells to achieve ultra-high-speed trigonometric operations under specific input values. An electromagnetic wave-based optical diffractive neural network with only one hidden layer is built to perform four trigonometric operations. The designed optical trigonometric operator responds to different trigonometric operations and input values and generates accurate results. This research is significant for achieving integrable optical computing devices and photonic signal processors with ultra-fast running speeds.