Photonic-dispersion neural networks for inverse scattering problems

Tongyu Li; Ang Chen; Lingjie Fan; Minjia Zheng; Jiajun Wang; Guopeng Lu; Maoxiong Zhao; Xinbin Cheng; Wei Li; Xiaohan Liu; Haiwei Yin; Lei Shi; Jian Zi

doi:10.1038/s41377-021-00600-y

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Photonic-dispersion neural networks for inverse scattering problems

Articles

- Photonic-dispersion neural networks for inverse scattering problems
- Photonic-dispersion neural networks for inverse scattering problems
- LSA 2021年10卷第10期页码：1819-1828
- Affiliations：
  
  1.State Key Laboratory of Surface Physics, Key Laboratory of Micro- and Nano-Photonics Structures (Ministry of Education) and Department of Physics, Fudan University, Shanghai 200433, China
  2.Shanghai Engineering Research Center of Optical Metrology for Nano-fabrication (SERCOM), Shanghai 200433, China
  3.Institute of Precision Optical Engineering, School of Physics Science and Engineering, Tongji University, Shanghai 200092, China
  4.National Institute of Metrology, Beijing 100029, China
  5.Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
- Author bio：
  
  Lei Shi (lshi@fudan.edu.cn)
  Jian Zi (jzi@fudan.edu.cn)
- Funds：
- DOI：10.1038/s41377-021-00600-y
  中图分类号：
- 纸质出版日期：2021-10-31，
  
  网络出版日期：2021-07-27，
  
  收稿日期：2020-11-30，
  
  修回日期：2021-07-10，
  
  录用日期：2021-07-13
- Accepted：
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Photonic-dispersion neural networks for inverse scattering problems[J]. LSA, 2021,10(10):1819-1828.

Li, T. Y. et al. Photonic-dispersion neural networks for inverse scattering problems. Light: Science & Applications, 10, 1819-1828 (2021).
Photonic-dispersion neural networks for inverse scattering problems[J]. LSA, 2021,10(10):1819-1828. DOI： 10.1038/s41377-021-00600-y.

Li, T. Y. et al. Photonic-dispersion neural networks for inverse scattering problems. Light: Science & Applications, 10, 1819-1828 (2021). DOI： 10.1038/s41377-021-00600-y.

摘要

Abstract

Inferring the properties of a scattering objective by analyzing the optical far-field responses within the framework of inverse problems is of great practical significance. However

it still faces major challenges when the parameter range is growing and involves inevitable experimental noises. Here

we propose a solving strategy containing robust neural-networks-based algorithms and informative photonic dispersions to overcome such challenges for a sort of inverse scattering problem—reconstructing grating profiles. Using two typical neural networks

forward-mapping type and inverse-mapping type

we reconstruct grating profiles whose geometric features span hundreds of nanometers with nanometric sensitivity and several seconds of time consumption. A forward-mapping neural network with a parameters-to-point architecture especially stands out in generating analytical photonic dispersions accurately

featured by sharp Fano-shaped spectra. Meanwhile

to implement the strategy experimentally

a Fourier-optics-based angle-resolved imaging spectroscopy with an all-fixed light path is developed to measure the dispersions by a single shot

acquiring adequate information. Our forward-mapping algorithm can enable real-time comparisons between robust predictions and experimental data with actual noises

showing an excellent linear correlation (

0.982) with the measurements of atomic force microscopy. Our work provides a new strategy for reconstructing grating profiles in inverse scattering problems.

关键词

Keywords

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