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Inverse Design of Optical Switch Based on Bilevel Optimization Inspired by Meta-Learning

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    Inverse Design of Optical Switch Based on Bilevel Optimization Inspired by Meta-Learning
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    • Beicheng Lou*
      Beicheng Lou
      Department of Applied Physics and Ginzton Laboratory, Stanford University, Stanford, California 94305, United States
      *E-mail: [email protected]
      More by Beicheng Lou
      Orcidhttps://orcid.org/0000-0001-7846-5551
    • Jesse Alexander Rodriguez
      Jesse Alexander Rodriguez
      Department of Mechanical Engineering, Stanford University, Stanford, California 94305, United States
      More by Jesse Alexander Rodriguez
    • Benjamin Wang
      Benjamin Wang
      Department of Mechanical Engineering, Stanford University, Stanford, California 94305, United States
      More by Benjamin Wang
    • Mark Cappelli
      Mark Cappelli
      Department of Mechanical Engineering, Stanford University, Stanford, California 94305, United States
      More by Mark Cappelli
    • Shanhui Fan*
      Shanhui Fan
      Department of Electrical Engineering and Ginzton Laboratory, Stanford University, Stanford, California 94305, United States
      *E-mail: [email protected]
      More by Shanhui Fan
      Orcidhttps://orcid.org/0000-0002-0081-9732
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    ACS Photonics

    Cite this: ACS Photonics 2023, 10, 6, 1806–1812
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    https://doi.org/10.1021/acsphotonics.3c00113
    Published June 1, 2023
    Copyright © 2023 American Chemical Society
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    Abstract

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    We introduce the concept of meta-learning into the design of active optical switches. An optical switch consists of both tunable and nontunable elements. It has been difficult to apply conventional inverse design methods to optical switches, since the optimal choice of the tunable elements depends on the design of the nontunable elements. Here we show that a bilevel optimization scheme, closely related to the concept of meta-learning, can be used for the design of active optical switches. In this scheme, the inner and outer loops correspond to the optimization of the tunable and nontunable elements, respectively. We illustrate this scheme with two designs of optical switches based on different tuning mechanisms. This approach is generally applicable for the design of optical switches as well as other active and tunable optical devices.

    Copyright © 2023 American Chemical Society

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    This article is cited by 9 publications.

    1. Hao Chen, Mingyuan Zhang, Yeyu Tong. Always-Feasible Photonic Inverse Design with a Differentiable Conditional Design Generator. ACS Photonics 2024, 11 (10) , 4461-4471. https://doi.org/10.1021/acsphotonics.4c01522
    2. Beicheng Lou, Haoning Tang, Fan Du, Guangqi Gao, Eric Mazur, Shanhui Fan. Free-Space Beam Steering with Twisted Bilayer Photonic Crystal Slabs. ACS Photonics 2024, 11 (9) , 3636-3643. https://doi.org/10.1021/acsphotonics.4c00736
    3. Anna Mikhailovskaya, Konstantin Grotov, Dmytro Vovchuk, Dmitry Dobrykh, Carsten Rockstuhl, Pavel Ginzburg. Superradiant Broadband Magnetoelectric Arrays Empowered by Meta-Learning. IEEE Transactions on Antennas and Propagation 2025, 73 (4) , 2596-2604. https://doi.org/10.1109/TAP.2024.3524423
    4. Jintao Chen, Zihan Zhang, Zhequn Huang, Kehang Cui. Thermal emission modulation of fabrication-friendly, free-form metasurfaces via explainable deep-learning Bayesian optimization. Applied Physics Letters 2025, 126 (5) https://doi.org/10.1063/5.0250273
    5. Beicheng Lou, Shanhui Fan. RCWA4D: Electromagnetic solver for layered structures with incommensurate periodicities. Computer Physics Communications 2025, 306 , 109356. https://doi.org/10.1016/j.cpc.2024.109356
    6. Sean Hooten, Peng Sun, Liron Gantz, Marco Fiorentino, Raymond Beausoleil, Thomas Van Vaerenbergh. Automatic Differentiation Accelerated Shape Optimization Approaches to Photonic Inverse Design in FDFD/FDTD. Laser & Photonics Reviews 2025, 19 (2) https://doi.org/10.1002/lpor.202301199
    7. Pavel Terekhov, Shengyuan Chang, Md Tarek Rahman, Sadman Shafi, Hyun-Ju Ahn, Linghan Zhao, Xingjie Ni. Enhancing metasurface fabricability through minimum feature size enforcement. Nanophotonics 2024, 13 (17) , 3147-3154. https://doi.org/10.1515/nanoph-2024-0150
    8. Dongyu Hu, Shaowei He, Shibin Li, Weiming Zhu. A dynamic beam switching metasurface based on angular mode-hopping effect. Frontiers in Physics 2024, 12 https://doi.org/10.3389/fphy.2024.1392115
    9. M. Sanchez, C. Everly, P. A. Postigo. Advances in machine learning optimization for classical and quantum photonics. Journal of the Optical Society of America B 2024, 41 (2) , A177. https://doi.org/10.1364/JOSAB.507268
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    ACS Photonics

    Cite this: ACS Photonics 2023, 10, 6, 1806–1812
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acsphotonics.3c00113
    Published June 1, 2023
    Copyright © 2023 American Chemical Society
    Request reuse permissions

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