Recent advances in metasurface design and quantum optics applications with machine learning, physics-informed neural networks, and topology optimization methods

Wenye Ji; Jin Chang; He-Xiu Xu; Jian Rong Gao; Simon Gröblacher; H. Paul Urbach; Aurèle J. L. Adam

doi:10.1038/s41377-023-01218-y

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Recent advances in metasurface design and quantum optics applications with machine learning, physics-informed neural networks, and topology optimization methods

Reviews

- Recent advances in metasurface design and quantum optics applications with machine learning, physics-informed neural networks, and topology optimization methods
- Light: Science & Applications Vol. 12, Issue 8, Pages: 1494-1509(2023)
- Affiliations：
  
  1.Department of Imaging Physics, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, The Netherlands
  2.Department of Quantum Nanoscience, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, The Netherlands
  3.Shaanxi Key Laboratory of Flexible Electronics (KLoFE), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an 710072, China
  4.SRON Netherlands Institute for Space Research, Niels Bohrweg 4, 2333 CA Leiden, The Netherlands
- Author bio：
  
  Jin Chang (J.Chang-1@tudelft.nl)
  He-Xiu Xu (hxxuellen@gmail.com)
  H. Paul Urbach (H.P.Urbach@tudelft.nl)
- Funds：
- DOI：10.1038/s41377-023-01218-y
  CLC：
- Published：31 August 2023，
  
  Published Online：07 July 2023，
  
  Received：02 February 2023，
  
  Revised：22 May 2023，
  
  Accepted：25 June 2023
- Accepted：
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Ji, W. Y. et al. Recent advances in metasurface design and quantum optics applications with machine learning, physics-informed neural networks, and topology optimization methods. Light: Science & Applications, 12, 1494-1509 (2023).
DOI：

Ji, W. Y. et al. Recent advances in metasurface design and quantum optics applications with machine learning, physics-informed neural networks, and topology optimization methods. Light: Science & Applications, 12, 1494-1509 (2023). DOI： 10.1038/s41377-023-01218-y.

摘要

Abstract

As a two-dimensional planar material with low depth profile

a metasurface can generate non-classical phase distributions for the transmitted and reflected electromagnetic waves at its interface. Thus

it offers more flexibility to control the wave front. A traditional metasurface design process mainly adopts the forward prediction algorithm

such as Finite Difference Time Domain

combined with manual parameter optimization. However

such methods are time-consuming

and it is difficult to keep the practical meta-atom spectrum being consistent with the ideal one. In addition

since the periodic boundary condition is used in the meta-atom design process

while the aperiodic condition is used in the array simulation

the coupling between neighboring meta-atoms leads to inevitable inaccuracy. In this review

representative intelligent methods for metasurface design are introduced and discussed

including machine learning

physics-information neural network

and topology optimization method. We elaborate on the principle of each approach

analyze their advantages and limitations

and discuss their potential applications. We also summarize recent advances in enabled metasurfaces for quantum optics applications. In short

this paper highlights a promising direction for intelligent metasurface designs and applications for future quantum optics research and serves as an up-to-date reference for researchers in the metasurface and metamaterial fields.

关键词

Keywords

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