Abstract
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.
Abstract (translated)
未经监督的芯片图缺陷模式聚类具有挑战性,因为某些缺陷模式的显现存在显著差异。这包括缺陷区域的形状、位置、密度和旋转的变化。我们提出了一个收获方法,可以对具有挑战性的芯片图缺陷模式进行良好的聚类。我们方法的关键在于通过迭代进行维度降维和聚类,并在每个迭代过程中根据轮廓评分排除一个聚类。这种方法在一般聚类性能方面提高了聚类性能,尤其是在难以分类的缺陷模式上。较低的计算开销允许快速评估大型数据集,并可用于支持手动标注努力。我们与文献中相关的 approaches 进行了基准测试,并在真实工业数据集上取得了更好的结果。
URL
https://arxiv.org/abs/2404.15436
