Iterative Cluster Harvesting for Wafer Map Defect Patterns
arxiv(2024)
摘要
Unsupervised clustering of wafer map defect patterns is challenging because
the appearance of certain defect patterns varies significantly. This includes
changing shape, location, density, and rotation of the defect area on the
wafer. We present a harvesting approach, which can cluster even challenging
defect patterns of wafer maps well. Our approach makes use of a well-known,
three-step procedure: feature extraction, dimension reduction, and clustering.
The novelty in our approach lies in repeating dimensionality reduction and
clustering iteratively while filtering out one cluster per iteration according
to its silhouette score. This method leads to an improvement of clustering
performance in general and is especially useful for difficult defect patterns.
The low computational effort allows for a quick assessment of large datasets
and can be used to support manual labeling efforts. We benchmark against
related approaches from the literature and show improved results on a
real-world industrial dataset.
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