Name
Title | ||
|---|---|---|
Getting More From the Semiconductor Test: Data Mining With Defect-Cluster Extraction. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TIM.2011.2122430 | IEEE T. Instrumentation and Measurement |
Keywords | Field | DocType |
data mining,production engineering computing,semiconductor device testing,semiconductor industry,cluster-extraction algorithm,data mining,defect-cluster extraction,defect-cluster identification,defect-extraction accuracy,detection algorithm,dies,high-volume production data,segmentation algorithm,semiconductor test,semiconductor wafer,Data mining,defect-cluster extraction,probe testing,segmentation,semiconductor manufacturing | Data mining,Cluster (physics),Wafer,Segmentation,Semiconductor device fabrication,Test data,Cluster analysis,Design for manufacturability,Mathematics,Fabrication | Journal |
Volume | Issue | ISSN |
60 | 10 | 0018-9456 |
Citations | PageRank | References |
2 | 0.41 | 13 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Melanie Po-Leen Ooi | 1 | 70 | 18.35 |
| Eric Kwang Joo Sim | 2 | 2 | 0.75 |
| Ye Chow Kuang | 3 | 72 | 19.81 |
| Serge N. Demidenko | 4 | 84 | 19.38 |
| Lindsay Kleeman | 5 | 736 | 85.03 |
| Chris W. K. Chan | 6 | 2 | 0.75 |