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

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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Bibliographic Details
Main Authors: Uk Jo, Seoung Bum Kim
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Data augmentation
defect patterns classification
semiconductor manufacturing
semi-supervised learning
wafer bin maps
Online Access:https://ieeexplore.ieee.org/document/10813350/
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