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

Review (2023)
Author(s)

W. Ji (TU Delft - ImPhys/Adam group)

J. Chang (TU Delft - QN/Groeblacher Lab)

He Xiu Xu (Northwestern Polytechnical University)

J.R. Gao (TU Delft - ImPhys/Adam group, SRON–Netherlands Institute for Space Research)

S. Gröblacher (TU Delft - QN/Groeblacher Lab, TU Delft - QN/Quantum Nanoscience)

Paul Urbach (TU Delft - ImPhys/Urbach group)

Aurlé Adam (TU Delft - ImPhys/Adam group)

Research Group
QN/Groeblacher Lab
Copyright
© 2023 W. Ji, J. Chang, He Xiu Xu, J.R. Gao, S. Groeblacher, Paul Urbach, A.J.L. Adam
DOI related publication
https://doi.org/10.1038/s41377-023-01218-y
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 W. Ji, J. Chang, He Xiu Xu, J.R. Gao, S. Groeblacher, Paul Urbach, A.J.L. Adam
Research Group
QN/Groeblacher Lab
Issue number
1
Volume number
12
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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.