Name
Abstract | ||
|---|---|---|
The International Technology Roadmap for Semiconductors (ITRS) identifies production test data as an essential element in improving design and technology in the manufacturing process feedback loop. One of the observations made from the high-volume production test data is that dies that fail due to a systematic failure have a tendency to form certain unique patterns that manifest as defect clusters at the wafer level. Identifying and categorising such clusters is a crucial step towards manufacturing yield improvement and implementation of real-time statistical process control. Addressing the semiconductor industry's needs, this research proposes an automatic defect cluster recognition system for semiconductor wafers that achieves up to 95% accuracy (depending on the product type). |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.engappai.2012.03.016 | Eng. Appl. of AI |
Keywords | Field | DocType |
semiconductor wafer,real-time statistical process control,defect cluster,defect cluster recognition system,production test data,certain unique pattern,automatic defect cluster recognition,manufacturing process feedback loop,high-volume production test data,international technology roadmap,semiconductor industry,feature extraction | Cluster (physics),Wafer,Computer science,Feedback loop,International Technology Roadmap for Semiconductors,Die (manufacturing),Artificial intelligence,Test data,Statistical process control,Reliability engineering,Machine learning,Semiconductor | Journal |
Volume | Issue | ISSN |
26 | 3 | 0952-1976 |
Citations | PageRank | References |
7 | 0.57 | 16 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Melanie Po-Leen Ooi | 1 | 70 | 18.35 |
| Hong Kuan Sok | 2 | 15 | 1.80 |
| Ye Chow Kuang | 3 | 72 | 19.81 |
| Serge Demidenko | 4 | 47 | 7.78 |
| Chris Chan | 5 | 7 | 0.57 |