Name
Abstract | ||
|---|---|---|
Root Cause Analysis (RCA) is a critical technology for yield improvement in integrated circuit manufacture. Traditional RCA prefers unsupervised algorithms such as Expectation Maximization based on Bayesian models. However, these methods are severely limited by the weak predictive capability of statistical models and can’t effectively transfer the yield learning experience from old designs and pro... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ITC50571.2021.00010 | 2021 IEEE International Test Conference (ITC) |
Keywords | DocType | ISSN |
Root cause analysis,yield improvement,neural network,self-adaptive learning | Conference | 1089-3539 |
ISBN | Citations | PageRank |
978-1-6654-1695-5 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Huang | 1 | 0 | 0.34 |
| Min Qin | 2 | 0 | 0.34 |
| Ruosheng Xu | 3 | 0 | 0.34 |
| Cheng Chen | 4 | 0 | 0.34 |
| Shangling Jui | 5 | 1 | 3.05 |
| Zhihao Ding | 6 | 0 | 0.34 |
| Pengyun Li | 7 | 0 | 0.34 |
| Yu Huang | 8 | 0 | 0.34 |