Phase unwrapping in optical metrology via denoised and convolutional segmentation networks

Junchao Zhang, Xiaobo Tian, Jianbo Shao, Haibo Luo, Rongguang Liang

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

The interferometry technique is commonly used to obtain the phase information of an object in optical metrology. The obtained wrapped phase is subject to a 2π ambiguity. To remove the ambiguity and obtain the correct phase, phase unwrapping is essential. Conventional phase unwrapping approaches are time-consuming and noise sensitive. To address those issues, we propose a new approach, where we transfer the task of phase unwrapping into a multi-class classification problem and introduce an efficient segmentation network to identify classes. Moreover, a noise-to-noise denoised network is integrated to preprocess noisy wrapped phase. We have demonstrated the proposed method with simulated data and in a real interferometric system.

Original languageEnglish (US)
Pages (from-to)14903-14912
Number of pages10
JournalOptics Express
Volume27
Issue number10
DOIs
StatePublished - Jan 1 2019

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

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