Automatic defect segmentation by unsupervised anomaly learning

• Published in: arXiv.org
• Main Authors: Ofir, Nati, Yacobi, Ran, Granoviter, Omer, Levant, Boris, Ore Shtalrid
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 21.06.2022
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Datasets; Defects; Image segmentation; Net shape; Unsupervised learning

This paper addresses the problem of defect segmentation in semiconductor manufacturing. The input of our segmentation is a scanning-electron-microscopy (SEM) image of the candidate defect region. We train a U-net shape network to segment defects using a dataset of clean background images. The sample...