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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Bibliografische Detailangaben

Hauptverfasser:	Uk Jo, Seoung Bum Kim
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	IEEE 2025-01-01
Schriftenreihe:	IEEE Access
Schlagworte:	Data augmentation defect patterns classification semiconductor manufacturing semi-supervised learning wafer bin maps
Online-Zugang:	https://ieeexplore.ieee.org/document/10813350/
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