Optical metrology embraces deep learning: keeping an open mind

Bing Pan

doi:10.1038/s41377-022-00829-1

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Optical metrology embraces deep learning: keeping an open mind

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- Optical metrology embraces deep learning: keeping an open mind
- Light: Science & Applications Vol. 11, Issue 6, Pages: 1023-1025(2022)
- Affiliations：
  
  School of Aeronautic Science and Engineering, Beihang University, 100191 Beijing, China
- Author bio：
  
  Bing Pan (panb@buaa.edu.cn)
- Funds：
- DOI：10.1038/s41377-022-00829-1
  CLC：
- Published：30 June 2022，
  
  Published Online：17 May 2022，
- Accepted：
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Pan, B. Optical metrology embraces deep learning: keeping an open mind. Light: Science & Applications, 11, 1023-1025 (2022).
DOI：

Pan, B. Optical metrology embraces deep learning: keeping an open mind. Light: Science & Applications, 11, 1023-1025 (2022). DOI： 10.1038/s41377-022-00829-1.

摘要

Abstract

Optical metrology practitioners ought to embrace deep learning with an open mind

while devote continuing effortsto look for its theoretical groundwork and maintain an awareness of its limits.

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references

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Wang, K. Q. et al. One-step robust deep learning phase unwrapping.Opt. Express27, 15100–15115 (2019)..

Yin, W. et al. Temporal phase unwrapping using deep learning.Sci. Rep.9, 20175 (2019)..

Qian, J. M. et al. Deep-learning-enabled geometric constraints and phase unwrapping for single-shot absolute 3D shape measurement.APL Photonics5, 046105 (2020)..

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Feng, S. J. et al. Deep-learning-based fringe-pattern analysis with uncertainty estimation.Optica8, 1507–1510 (2021)..

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