Deep learning-enabled compact optical trigonometric operator with metasurface

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