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Phase unwrapping via deep learning for surface shape measurement by using wavelength tuning interferometry

Published: 26 March 2024 Publication History

Abstract

Wavelength tuning interferometry is widely used in optical metrology in order to obtain the phase information of sample. The obtained wrapped phase usually unwraps in the range of [-π, π]. Therefore, phase unwrapping operation is required in order to obtain the right phase. But some factors, such as phase shift miscalibration, coupling error, and noise, always lower the precision of conventional phase shifting algorithm. To address such kind of problems, we proposed a deep learning method using deep convolutional neural network to perform phase unwrapping process by turning the task into a multiclass classification work and a 2N - 1 method for generating the training dataset. The results indicated that proposed method not only can compensate for phase shift miscalibration and coupling error, but also has strong robust and denoise ability, which means that proposed method is outperformed other conventional phase measurement algorithms.

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EBIMCS '23: Proceedings of the 2023 6th International Conference on E-Business, Information Management and Computer Science
December 2023
265 pages
ISBN:9798400709333
DOI:10.1145/3644479
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 26 March 2024

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Author Tags

  1. Phase unwrapping
  2. deep learning
  3. surface shape measurement
  4. wavelength tuning interferometry

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