Semi-Supervised Learning With Wafer-Specific Augmentations for Wafer Defect Classification

Semi-supervised learning (SSL) models, which leverage both labeled and unlabeled datasets, have been increasingly applied to classify wafer bin map patterns in semiconductor manufacturing. These models typically outperform supervised learning models in scenarios where labeled data are scarce. Howeve...

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Hlavní autoři:	Uk Jo, Seoung Bum Kim
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	IEEE 2025-01-01
Edice:	IEEE Access
Témata:	Data augmentation defect patterns classification semiconductor manufacturing semi-supervised learning wafer bin maps
On-line přístup:	https://ieeexplore.ieee.org/document/10813350/
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