Name
Title | ||
|---|---|---|
Nonparametric Virtual Sensors For Semiconductor Manufacturing Using Information Theoretic Learning And Kernel Machines |
Abstract | ||
|---|---|---|
In this paper, a novel learning methodology is presented and discussed with reference to the application of virtual sensors in the semiconductor manufacturing environment. Density estimation techniques are used jointly with Renyi's entropy to define a loss function for the learning problem (relying on Information Theoretic Learning concepts). Furthermore, Reproducing Kernel Hilbert Spaces (RKHS) theory is employed to handle nonlinearities and include regularization capabilities in the model. The proposed algorithm allows to estimate the structure of the predictive model, as well as the associated probabilistic uncertainty, in a nonparametric fashion. The methodology is then validated using simulation studies and process data from the semiconductor manufacturing industry. The proposed approach proves to be especially effective in strongly nongaussian environments and presents notable outlier filtering capabilities. |
Year | Venue | Keywords |
|---|---|---|
2011 | ICINCO 2011: PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, VOL 2 | Semiconductors, Machine learning, Entropy, Kernel methods |
Field | DocType | Citations |
Kernel (linear algebra),Computer science,Semiconductor device fabrication,Nonparametric statistics,Virtual sensors,Artificial intelligence,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Schirru | 1 | 45 | 7.98 |
| Simone Pampuri | 2 | 84 | 10.20 |
| Cristina De Luca | 3 | 19 | 3.32 |
| Giuseppe De Nicolao | 4 | 738 | 76.26 |