A systemic comparison between using augmented data and synthetic data as means of enhancing wafermap defect classification

Rajaa Alqudah,Amjed A. Al-Mousa, Yazan Abu Hashyeh, Omar Z. Alzaibaq

Computers in Industry(2023)

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Abstract
The analysis of defects in wafermaps is a very crucial task in enhancing the yield. With a fast defect classification, companies can address the root cause of the problem and thus increase the yield quickly. Several researchers have used the WM-811K dataset to train and test their machine learning models, but with only 3.1% of the wafers labeled in this dataset. Researchers resorted to using supplementary techniques, such as augmented data and synthetic data to train the model.
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