Phase unwrapping in optical metrology via denoised and convolutional segmentation networks

• Published in: Optics express Vol. 27; no. 10; p. 14903
• Main Authors: Zhang, Junchao, Tian, Xiaobo, Shao, Jianbo, Luo, Haibo, Liang, Rongguang
• Format: Journal Article
• Language: English
• Published: United States 13.05.2019
• ISSN: 1094-4087; 1094-4087
• DOI: 10.1364/OE.27.014903

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.