DECOR：具有方向鲁棒性的深度嵌入聚类
DECOR: Deep Embedding Clustering with Orientation Robustness
AI总结：
DECOR通过引入方向鲁棒性，实现了在复杂、不平衡的晶圆数据中可靠且高效的聚类，提升自动化视觉检测系统的性能。
摘要：
在半导体制造中，早期检测晶圆缺陷对产品良率优化至关重要。然而，来自晶圆质量测试的原始晶圆数据往往复杂、无标签、不平衡，并且可能在单块晶圆上包含多种缺陷，这使得设计在如此不完美数据条件下仍能保持可靠的聚类方法至关重要。我们引入了DECOR，一种具有方向鲁棒性的深度聚类框架，将晶圆地图中的复杂缺陷模式分组为一致的聚类。我们在开源的MixedWM38数据集上评估了我们的方法，证明了其在无需手动调参的情况下发现聚类的能力。DECOR明确考虑了晶圆地图中的方向变化，确保空间上相似的缺陷在无论其旋转或对齐的情况下都能一致地聚类。实验表明，我们的方法优于现有的聚类基线方法，从而在自动化视觉检测系统中提供了一种可靠且可扩展的解决方案。
英文摘要：
In semiconductor manufacturing, early detection of wafer defects is critical for product yield optimization. However, 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. We introduce DECOR, a deep clustering with orientation robustness framework that groups complex defect patterns from wafer maps into consistent clusters. We evaluate our method on the open source MixedWM38 dataset, demonstrating its ability to discover clusters without manual tuning. DECOR explicitly accounts for orientation variations in wafer maps, ensuring that spatially similar defects are consistently clustered regardless of its rotation or alignment. Experiments indicate that our method outperforms existing clustering baseline methods, thus providing a reliable and scalable solution in automated visual inspection systems.
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