Deep learning in optical metrology: a review

Chao Zuo; Jiaming Qian; Shijie Feng; Wei Yin; Yixuan Li; Pengfei Fan; Jing Han; Kemao Qian; Qian Chen

doi:10.1038/s41377-022-00714-x

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Deep learning in optical metrology: a review

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- Deep learning in optical metrology: a review
- Light: Science & Applications Vol. 11, Issue 4, Pages: 466-519(2022)
- Affiliations：
  
  1.Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, 210094, Nanjing, Jiangsu Province, China
  2.Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, 210094, Nanjing, Jiangsu Province, China
  3.School of Engineering and Materials Science, Queen Mary University of London, London, E1 4NS, UK
  4.School of Computer Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
- Author bio：
  
  Chao Zuo (zuochao@njust.edu.cn)
  Kemao Qian (mkmqian@ntu.edu.sg)
  Qian Chen (chenqian@njust.edu.cn)
- Funds：
- DOI：10.1038/s41377-022-00714-x
  CLC：
- Published：30 April 2022，
  
  Published Online：23 February 2022，
  
  Received：11 July 2021，
  
  Revised：03 January 2022，
  
  Accepted：11 January 2022
- Accepted：
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Zuo, C. et al. Deep learning in optical metrology: a review. Light: Science & Applications, 11, 466-519 (2022).
DOI：

Zuo, C. et al. Deep learning in optical metrology: a review. Light: Science & Applications, 11, 466-519 (2022). DOI： 10.1038/s41377-022-00714-x.

摘要

Abstract

With the advances in scientific foundations and technological implementations

optical metrology has become versatile problem-solving backbones in manufacturing

fundamental research

and engineering applications

such as quality control

nondestructive testing

experimental mechanics

and biomedicine. In recent years

deep learning

a subfield of machine learning

is emerging as a powerful tool to address problems by learning from data

largely driven by the availability of massive datasets

enhanced computational power

fast data storage

and novel training algorithms for the deep neural network. It is currently promoting increased interests and gaining extensive attention for its utilization in the field of optical metrology. Unlike the traditional "physics-based" approach

deep-learning-enabled optical metrology is a kind of "data-driven" approach

which has already provided numerous alternative solutions to many challenging problems in this field with better performances. In this review

we present an overview of the current status and the latest progress of deep-learning technologies in the field of optical metrology. We first briefly introduce both traditional image-processing algorithms in optical metrology and the basic concepts of deep learning

followed by a comprehensive review of its applications in various optical metrology tasks

such as fringe denoising

phase retrieval

phase unwrapping

subset correlation

and error compensation. The open challenges faced by the current deep-learning approach in optical metrology are then discussed. Finally

the directions for future research are outlined.

关键词

Keywords

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