Name
Abstract | ||
|---|---|---|
The EDS wafer test yield is the most important criteria to evaluate FAB's productivity, so the manufacturing operation's main purpose is to secure new product yield early and maintaining the yield of mass-produced products high. Defining a failed characteristic that's compatible to the device and classifying wafers depending on failure type helps tasks searching for error from FAB become automated. This would be more efficient then existing failed analysis operations and strive to become the basis for improvement in yield and quality. For this method, this research is trying to use a high speed recognition algorithm called SVM (support vector machine) that will define wafer's failed type and automatically classify each one. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11552451_183 | KES (2) |
Keywords | Field | DocType |
support vector machine,high speed recognition algorithm,failed characteristic,eds wafer test yield,manufacturing operation,failed type,classifying wafer,important criterion,failure type,main purpose,decision tree,analysis operation,automatic classification | Structured support vector machine,Decision tree,Data mining,Computer science,Support vector machine,Artificial intelligence,Recognition algorithm,Machine learning,New product development | Conference |
Volume | ISSN | ISBN |
3682 | 0302-9743 | 3-540-28895-3 |
Citations | PageRank | References |
1 | 0.50 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Youngshin Han | 1 | 27 | 8.28 |
| Chil-Gee Lee | 2 | 47 | 16.85 |