Grouping Complex Wafer Defect Patterns Into Meaningful Clusters
A new technical paper titled DECOR: Deep Embedding Clustering with Orientation Robustness was published by researchers at Oregon State University and Micron Technology.
In semiconductor manufacturing, early detection of wafer defects is critical for product yield optimization.
Raw wafer data from wafer quality tests are often complex, unlabeled, imbalanced and can contain multiple defects on a single wafer, making it crucial to design clustering methods that remain reliable under such imperfect data conditions.
Author's summary: Researchers propose a new method for clustering wafer defects.
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