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Getting More From the Semiconductor Test: Data Mining With Defect-Cluster Extraction
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DOI：10.1109/TIM.2011.2122430.png)
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High-volume production data shows that dies, which failed probe test on a semiconductor wafer, have a tendency to form certain unique patterns, i.e., defect clusters. Identifying such clusters is one of the crucial steps toward improvement of the fabrication process and design for manufacturing. This paper proposes a new technique for defect-cluster identification that combines data mining with a defect-cluster extraction using a Segmentation, Detection, and Cluster-Extraction algorithm. It offers high defect-extraction accuracy, without any significant increase in test time and cost.
Keyword:
Data mining
defect-cluster extraction
probe testing
segmentation
semiconductor manufacturing
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IF:
5.9
论文数:
2.0W
被引数:
5.8W
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