Deep learning-enabled compact optical trigonometric operator with metasurface - Université Paris Nanterre
Article Dans Une Revue PhotoniX Année : 2022

Deep learning-enabled compact optical trigonometric operator with metasurface

Zihan Zhao
  • Fonction : Auteur
Yue Wang
  • Fonction : Auteur
Chunsheng Guan
  • Fonction : Auteur
Kuang Zhang
  • Fonction : Auteur
Qun Wu
  • Fonction : Auteur
Haoyu Li
  • Fonction : Auteur
Jian Liu
  • Fonction : Auteur
Xumin Ding

Résumé

Abstract 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.
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Dates et versions

hal-04177626 , version 1 (15-01-2024)

Identifiants

Citer

Zihan Zhao, Yue Wang, Chunsheng Guan, Kuang Zhang, Qun Wu, et al.. Deep learning-enabled compact optical trigonometric operator with metasurface. PhotoniX, 2022, 3 (1), pp.15. ⟨10.1186/s43074-022-00062-4⟩. ⟨hal-04177626⟩
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