Name
Abstract | ||
|---|---|---|
GDBN (good die in bad neighborhood) methodology has been regarded as an effective technique for reducing DPPM (defect parts per million), by identifying and rejecting suspicious dice even though they test good. Instead of examining eight immediate neighbors or exploiting simple linear regression, in this paper we propose to employ a window of larger size for broad-sighted recognition of neighborho... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/VTS50974.2021.9441055 | 2021 IEEE 39th VLSI Test Symposium (VTS) |
Keywords | DocType | ISSN |
Authorization,IEEE merchandise,Linear regression,Artificial neural networks,Very large scale integration,Encoding | Conference | 1093-0167 |
ISBN | Citations | PageRank |
978-1-6654-1949-9 | 0 | 0.34 |
References | Authors | |
0 | 10 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cheng-Hao Yang | 1 | 0 | 0.34 |
| Chia-Heng Yen | 2 | 0 | 0.34 |
| Ting-Rui Wang | 3 | 0 | 0.34 |
| Chun-Teng Chen | 4 | 0 | 0.34 |
| Mason Chern | 5 | 2 | 1.40 |
| Ying-Yen Chen | 6 | 2 | 1.74 |
| Jih-Nung Lee | 7 | 14 | 3.13 |
| Shu-Yi Kao | 8 | 2 | 1.40 |
| Kai-Chiang Wu | 9 | 113 | 13.98 |
| Mango C. -T. Chao | 10 | 14 | 4.11 |