Recent Advances in Metasurface Design and Quantum Optics Applications with Machine Learning, Physics-Informed Neural Networks, and Topology Optimization Methods

Kavli Affiliate: Simon Groblacher

| First 5 Authors: Wenye Ji, Jin Chang2, He-Xiu Xu, Jian Rong Gao, Simon Gröblacher

| Summary:

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 summarise 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.

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