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Enhanced Light-Matter Interactions in Dielectric Nanostructures via Machine Learning Approach

Authors :
Xu, Lei
Rahmani, Mohsen
Ma, Yixuan
Smirnova, Daria A.
Kamali, Khosro Zangeneh
Deng, Fu
Chiang, Yan Kei
Huang, Lujun
Zhang, Haoyang
Gould, Stephen
Neshev, Dragomir N.
Miroshnichenko, Andrey E.
Source :
Advanced Photonics, 2(2), 026003 (2020)
Publication Year :
2019

Abstract

A key concept underlying the specific functionalities of metasurfaces, i.e. arrays of subwavelength nanoparticles, is the use of constituent components to shape the wavefront of the light, on-demand. Metasurfaces are versatile and novel platforms to manipulate the scattering, colour, phase or the intensity of the 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, it would require a multi-dimensional space optimization through direct numerical simulations. Recently, an alternative approach became quite popular allowing to reduce the computational cost significantly based on a deep-learning-assisted method. In this paper, 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 the enhanced light-matter interaction in nonlinear optical metasurfaces and optomechanical vibrations, simultaneously. We demonstrate that optimized metasurfaces lead up to 400+ folds enhancement of the third harmonic generation (THG); at the same time, they also contribute to 100+ folds enhancement in optomechanical vibrations. This approach can be further used to realize structures with unconventional scattering responses.<br />Comment: 29 pages, 7 figures

Subjects

Subjects :
Physics - Optics

Details

Database :
arXiv
Journal :
Advanced Photonics, 2(2), 026003 (2020)
Publication Type :
Report
Accession number :
edsarx.1912.10212
Document Type :
Working Paper
Full Text :
https://doi.org/10.1117/1.AP.2.2.026003