Main > Advanced Photonics >  Volume 2 >  Issue 2 >  Page 026003 > Article
  • Abstract
  • Abstract
  • Figures (7)
  • Tables (0)
  • Equations (5)
  • References (89)
  • Suppl. Mat.
  • Get PDF
  • View Full Text
  • Paper Information
  • Received: Mar. 15, 2020

    Accepted: Apr. 13, 2020

    Posted: Apr. 30, 2020

    Published Online: Apr. 30, 2020

    The Author Email: Xu Lei (lei.xu@ntu.ac.uk), Rahmani Mohsen (mohsen.rahmani@anu.edu.au), Ma Yixuan (yixuanma@mail.nankai.edu.cn), Smirnova Daria A. (daria.smirnova@anu.edu.au), Kamali Khosro Zangeneh (khosro.zangeneh@anu.edu.au), Deng Fu (u_deng@foxmail.com), Chiang Yan Kei (y.chiang@adfa.edu.au), Huang Lujun (lujun.huang@adfa.edu.au), Zhang Haoyang (zhangh49@qut.edu.au), Gould Stephen (stephen.gould@anu.edu.au), Neshev Dragomir N. (Dragomir.neshev@anu.edu.au), Miroshnichenko Andrey E. (andrey.miroshnichenko@unsw.edu.au)

    DOI: 10.1117/1.AP.2.2.026003

  • Get Citation
  • Copy Citation Text

    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[J]. Advanced Photonics, 2020, 2(2): 026003

    Download Citation

  • Category
  • Research Articles
  • Share
Advanced Photonics, Vol. 2, Issue 2, 026003 (2020)

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

Lei Xu1,2, Mohsen Rahmani2,3,4,*, Yixuan Ma1, Daria A. Smirnova3, Khosro Zangeneh Kamali3,4, Fu Deng1, Yan Kei Chiang1, Lujun Huang1, Haoyang Zhang5, Stephen Gould6, Dragomir N. Neshev3,4, and Andrey E. Miroshnichenko1,*

Author Affiliations

  • 1University of New South Wales, School of Engineering and Information Technology, Canberra, Australia
  • 2Nottingham Trent University, School of Science & Technology, Department of Engineering, Advanced Optics and Photonics Laboratory, Nottingham, United Kingdom
  • 3Australian National University, Research School of Physics, Nonlinear Physics Centre, Canberra, Australia
  • 4Australian National University, Research School of Physics, ARC Centre of Excellence for Transformative Meta-Optical Systems (TMOS), Canberra, Australia
  • 5Queensland University of Technology, School of Electrical Engineering and Computer Science, Brisbane, Queensland, Australia
  • 6Australian National University, College of Engineering and Computer Science, Canberra, Australia

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.

keywords

Please Enter Your Email: