Automatic defect segmentation by unsupervised anomaly learning
- URL: http://arxiv.org/abs/2202.02998v1
- Date: Mon, 7 Feb 2022 08:33:36 GMT
- Title: Automatic defect segmentation by unsupervised anomaly learning
- Authors: Nati Ofir, Ran Yacobi, Omer Granoviter, Boris Levant and Ore Shtalrid
- Abstract summary: 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.
Our experiments show that we succeed to segment real defects with high quality, even though our dataset contains no defect examples.
- Score: 0.5999777817331318
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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 robustness to 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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