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Automatic Defect Segmentation by Unsupervised Anomaly Learning

Citation Author(s):
Nati Ofir, Ran Yacobi, Omer Granoviter, Boris Levant, Ore Shtalrid
Submitted by:
Nati Ofir
Last updated:
15 July 2022 - 1:17am
Document Type:
Poster
Document Year:
2022
Event:
Presenters:
Nati Ofir
Paper Code:
1225

Abstract

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 samples of the training phase are produced automatically such that no manual labeling is required. To enrich the dataset of clean background samples, we apply defect implant augmentation. To that end, we apply a copy-and-paste of a random image patch in the clean specimen. To improve the robustness of the unlabeled data scenario, we train the features of the network with unsupervised learning methods and loss functions. Our experiments show that we succeed to segment real defects with high quality, even though our dataset contains no defect examples. Our approach performs accurately also on the problem of supervised and labeled defect segmentation.

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