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.

翻译：晶圆图缺陷模式的无监督聚类具有挑战性，因为某些缺陷模式的外观差异显著，包括缺陷区域在晶圆上的形状、位置、密度和旋转变化。我们提出一种收割方法，能够有效聚类甚至具有挑战性的晶圆图缺陷模式。该方法采用经典的三步流程：特征提取、降维和聚类。其创新之处在于迭代重复降维与聚类过程，并在每次迭代中根据轮廓系数过滤掉一个聚类。这一方法普遍提升了聚类性能，尤其适用于难以处理的缺陷模式。低计算开销使其能够快速评估大规模数据集，并可用于辅助人工标注工作。我们与文献中的相关方法进行基准对比，在真实工业数据集上展示了更优的结果。