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Title: Deep learning in optical metrology: a review
Authors: Zuo, Chao
Qian, Jiaming
Feng, Shijie
Yin, Wei
Li, Yixuan
Fan, Pengfei
Han, Jing
Qian, Kemao
Chen, Qian
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Zuo, C., Qian, J., Feng, S., Yin, W., Li, Y., Fan, P., Han, J., Qian, K. & Chen, Q. (2022). Deep learning in optical metrology: a review. Light, Science & Applications, 11(1), 39-.
Journal: Light, Science & Applications
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.
ISSN: 2047-7538
DOI: 10.1038/s41377-022-00714-x
Schools: School of Computer Science and Engineering 
Rights: © 2022 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit
Fulltext Permission: open
Fulltext Availability: With Fulltext
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