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.

通过特征提取、维度削减和聚类等步骤，本论文提出了一种针对半导体晶圆映射缺陷图案的无监督聚类方法，其中创新之处在于通过筛选轮廓分数选择性过滤聚类来改善聚类性能，尤其适用于困难的缺陷图案，并在实际工业数据集上对比了文献中的相关方法并展示了改进的结果。