Name
Abstract | ||
|---|---|---|
For the past decades, Virtual Metrology (VM) has been widely studied and covered in the literature for semiconductor industries where cycle time is a critical aspect and the elimination of non-productive metrology measurements is expected to significantly contribute to its reduction. A wide variety of approaches has been proposed but not effectively implemented. An ideal VM model should be able to provide accurate predictions and to reveal the hidden relationship among production/process factors. For this aim, we employ the Gaussian Bayesian Network (GBN) to investigate the implicit relationship not only between the metrology and the control factors but also among the production/process parameters. Instead of working purely as a black-box data-driven methodology, GBN enables the flexibility to integrate domain knowledge through the corresponding connected graph. The effectiveness of proposed approach is validated using real industrial data from a Chemical-Mechanical Polishing (CMP) process.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/WSC.2018.8632485 | WSC |
Field | DocType | ISSN |
Data modeling,Data mining,Markov process,Domain knowledge,Semiconductor device modeling,Computer science,Simulation,Metrology,Bayesian network,Gaussian,Virtual metrology | Conference | 0891-7736 |
ISBN | Citations | PageRank |
978-1-5386-6570 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei-Ting Yang | 1 | 1 | 1.02 |
| Jakey Blue | 2 | 14 | 5.15 |
| Agnes Roussy | 3 | 0 | 0.34 |
| Marco S. Reis | 4 | 13 | 6.49 |
| Jacques Pinaton | 5 | 19 | 12.98 |