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Machine learning enhances light-matter interactions in dielectric nanostructures

Date:
April 30, 2020
Source:
SPIE--International Society for Optics and Photonics
Summary:
A new discovery has promising possibilities for the development of a wide range of photonic devices and applications including those involved in optical sensing, optoacoustic vibrations, and narrowband filtering.
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A paper published in Advanced Photonics "Enhanced light-matter interactions in dielectric nanostructures via machine-learning approach," suggests that machine-learning techniques can be used to enhance metasurfaces, optimizing them for nonlinear optics and optomechanics. The discovery has promising possibilities for the development of a wide range of photonic devices and applications including those involved in optical sensing, optoacoustic vibrations, and narrowband filtering.

Metasurfaces are versatile platforms used to manipulate the scattering, color, phase, or intensity of light that can be used for light emission, detection, modulation, control and/or amplification at the nanoscale. In recent years, metasurfaces have been a subject of undergoing intense study as their optical properties can be adapted to a diverse set of applications, including superlenses, tunable images, and holograms.

According to Advanced Photonics Co-Editor-in-Chief, SPIE Fellow, and Head of Photonics & Nanotechnology Group at King's College London Anatoly Zayats, this work marks an exciting advancement in nanophotonics.

"Optimization of metasurfaces and metamaterials for particular applications is an important and time-consuming problem," said Zayats. "With traditional approaches, only few parameters can be optimised, so that the resulting performance is better than for some other designs but not necessarily the best. Using machine learning, one can search for the best design and cover the space of parameters not possible with traditional approaches."


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Materials provided by SPIE--International Society for Optics and Photonics. Note: Content may be edited for style and length.


Journal Reference:

  1. Lei Xu, Mohsen Rahmani, Yixuan Ma, Daria A. Smirnova, Khosro Zangeneh Kamali, Fu Deng, Yan Kei Chiang, Lujun Huang, Haoyang Zhang, Stephen Gould, Dragomir N. Neshev, Andrey E. Miroshnichenko. Enhanced light–matter interactions in dielectric nanostructures via machine-learning approach. Advanced Photonics, 2020; 2 (02): 1 DOI: 10.1117/1.AP.2.2.026003

Cite This Page:

SPIE--International Society for Optics and Photonics. "Machine learning enhances light-matter interactions in dielectric nanostructures." ScienceDaily. ScienceDaily, 30 April 2020. <www.sciencedaily.com/releases/2020/04/200430150226.htm>.
SPIE--International Society for Optics and Photonics. (2020, April 30). Machine learning enhances light-matter interactions in dielectric nanostructures. ScienceDaily. Retrieved March 28, 2024 from www.sciencedaily.com/releases/2020/04/200430150226.htm
SPIE--International Society for Optics and Photonics. "Machine learning enhances light-matter interactions in dielectric nanostructures." ScienceDaily. www.sciencedaily.com/releases/2020/04/200430150226.htm (accessed March 28, 2024).

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