Enhanced light–matter interactions in dielectric nanostructures via machine-learning approach

Xu, L ORCID: https://orcid.org/0000-0001-9071-4311, Rahmani, M ORCID: https://orcid.org/0000-0001-9268-4793, Ma, Y, Smirnova, DA, Kamali, KZ, Deng, F, Chiang, YK, Huang, L, Zhang, H, Gould, S, Neshev, DN and Miroshnichenko, AE, 2020. Enhanced light–matter interactions in dielectric nanostructures via machine-learning approach. Advanced Photonics, 2 (2): 026003. ISSN 2577-5421

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Abstract

A key concept underlying the specific functionalities of metasurfaces is the use of constituent components to shape the wavefront of the light on demand. Metasurfaces are versatile, novel platforms for manipulating the scattering, color, phase, or intensity of light. Currently, one of the typical approaches for designing a metasurface is to optimize one or two variables among a vast number of fixed parameters, such as various materials’ properties and coupling effects, as well as the geometrical parameters. Ideally, this would require multidimensional space optimization through direct numerical simulations. Recently, an alternative, popular approach allows for reducing the computational cost significantly based on a deep-learning-assisted method. We utilize a deep-learning approach for obtaining high-quality factor (high-Q) resonances with desired characteristics, such as linewidth, amplitude, and spectral position. We exploit such high-Q resonances for enhanced light–matter interaction in nonlinear optical metasurfaces and optomechanical vibrations, simultaneously. We demonstrate that optimized metasurfaces achieve up to 400-fold enhancement of the third-harmonic generation; at the same time, they also contribute to 100-fold enhancement of the amplitude of optomechanical vibrations. This approach can be further used to realize structures with unconventional scattering responses.

Item Type:	Journal article
Publication Title:	Advanced Photonics
Creators:	Xu, L., Rahmani, M., Ma, Y., Smirnova, D.A., Kamali, K.Z., Deng, F., Chiang, Y.K., Huang, L., Zhang, H., Gould, S., Neshev, D.N. and Miroshnichenko, A.E.
Publisher:	SPIE-International Society for Optical Engineering
Date:	29 April 2020
Volume:	2
Number:	2
ISSN:	2577-5421
Identifiers:	Number Type 10.1117/1.ap.2.2.026003 DOI 1354971 Other
Rights:	© the authors. Published by SPIE and CLP under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	21 Sep 2020 08:52
Last Modified:	31 May 2021 15:13
URI:	https://irep.ntu.ac.uk/id/eprint/40830

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