PhotoniX, Volume. 3, Issue 1, 15(2022)

Deep learning-enabled compact optical trigonometric operator with metasurface

Zihan Zhao1、†, Yue Wang2、†, Chunsheng Guan2、†, Kuang Zhang2, Qun Wu2, Haoyu Li1、*, Jian Liu1, Shah Nawaz Burokur3、**, and Xumin Ding1、***
Author Affiliations
  • 1Advanced Microscopy and Instrumentation Research Center, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150080, China
  • 2Department of Microwave Engineering, Harbin Institute of Technology, Harbin 150001, China
  • 3LEME, UPL, Univ Paris Nanterre, 92410 Ville d’Avray, France
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    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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    Zihan Zhao, Yue Wang, Chunsheng Guan, Kuang Zhang, Qun Wu, Haoyu Li, Jian Liu, Shah Nawaz Burokur, Xumin Ding. Deep learning-enabled compact optical trigonometric operator with metasurface[J]. PhotoniX, 2022, 3(1): 15

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    Paper Information

    Category: Research Articles

    Received: May. 13, 2022

    Accepted: Jul. 2, 2022

    Published Online: Jul. 10, 2023

    The Author Email: Li Haoyu (lihaoyu@hit.edu.cn), Burokur Shah Nawaz (sburokur@parisnanterre.fr), Ding Xumin (xuminding@hit.edu.cn)

    DOI:10.1186/s43074-022-00062-4

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