Abstract
Wafer bin map (WBM) defect patterns play a crucial role in identifying the root cause of manufacturing defects in the semiconductor industry. Although various deep learning-based approaches have been proposed for automated defect pattern classification, they often demand a large amount of labeled data for effective training. However, manual labeling is a costly and time-consuming process that requires specialized expertise. To address this challenge, this work introduces a novel active learning framework aimed at reducing the labeling cost by strategically selecting which WBMs should be labeled. An approach is proposed to mitigate issues related to class imbalance; the WBM patterns from identified classes are clustered, and a set of samples from each cluster is carefully selected, with a particular emphasis on classes with limited labeled data. This intelligent selection process effectively reduces human labeling efforts and mitigates problems associated with class-imbalanced training. The effectiveness of the proposed approach is demonstrated through significant improvements compared to other active learning methods. Remarkably, the state-of-the-art F1 score of 91.6% on the large-scale public WBM dataset, WM-811K, is achieved using only 4.3K labeled WBM images, while existing approaches require over 100K labeled images to achieve similar results. This outcome showcases the efficiency and practicality of the proposed approach. The code is available at (https://github.com/M-Siyamalan/PLAL).
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A publicly available dataset was used.
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Manivannan, S. Pseudo-labeling and clustering-based active learning for imbalanced classification of wafer bin map defects. SIViP 18, 2391–2401 (2024). https://doi.org/10.1007/s11760-023-02915-2
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DOI: https://doi.org/10.1007/s11760-023-02915-2