Nonparametric virtual sensors for semiconductor manufacturing: Using information theoretic learning and kernel machines

ICINCO 2011 - Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics

Volumn 2, Issue , 2011, Pages 349-358

  Schirru, Andrea ^a   Pampuri, Simone ^a,b   De Luca, Cristina ^b   De Nicolao, Giuseppe ^a  

^a UNIVERSITY OF PAVIA   (Italy)

^b INFINEON TECHNOLOGIES AUSTRIA AG   (Austria)

Author keywords

Entropy; Kernel methods; Machine learning; Semiconductors

Indexed keywords

DENSITY ESTIMATION; INFORMATION THEORETIC LEARNING; KERNEL MACHINE; KERNEL METHODS; LEARNING PROBLEM; LOSS FUNCTIONS; MACHINE-LEARNING; NON-GAUSSIAN; NON-PARAMETRIC; PREDICTIVE MODELS; PROBABILISTIC UNCERTAINTY; PROCESS DATA; RENYI'S ENTROPY; REPRODUCING KERNEL HILBERT SPACES; SEMICONDUCTOR MANUFACTURING; SEMICONDUCTOR MANUFACTURING INDUSTRY; SIMULATION STUDIES; VIRTUAL SENSOR;

DENSITY FUNCTIONAL THEORY; ENTROPY; HILBERT SPACES; INDUSTRIAL APPLICATIONS; INFORMATION THEORY; LEARNING ALGORITHMS; MANUFACTURE; PROBABILITY DENSITY FUNCTION; SEMICONDUCTOR DEVICE MANUFACTURE; SENSORS; UNCERTAINTY ANALYSIS;

ROBOTICS;

EID: 80052569246     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Ackerman, F., Stanton, E., and Bueno, R. (2010). Fat tails, exponents, extreme uncertainty: Simulating catastrophe in DICE. Ecological Economics, 69(8):1657-1665.
2. Carreira-Perpiñán, M. (2002). Mode-finding for mixtures of Gaussian distributions. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(11):1318-1323.
3. Erdogmus, D. and Principe, J. (2002). An error-entropy minimization algorithm for supervised training of nonlinear adaptive systems. Signal Processing, IEEE Transactions on, 50(7):1780-1786. (Pubitemid 34882696)
4. Gray, R. (2010). Entropy and information theory. Springer Verlag.
5. Hastie, T., Tibshirani, R., Friedman, J., and Franklin, J. (2005). The elements of statistical learning: data mining, inference and prediction. The Mathematical Intelligencer, 27(2):83-85.
6. Miller, K. (1964). Multidimensional gaussian distributions. Wiley New York.
7. Parzen, E. (1962). On estimation of a probability density function and mode. The annals of mathematical statistics, 33(3):1065-1076.
8. Popovic, D., Milosavljevic, V., Zekic, A., Macgearailt, N., and Daniels, S. (2009). Impact of low pressure plasma discharge on etch rate of SiO2 wafer. In APS Meeting Abstracts, volume 1, page 8037P.
9. Principe, J. (2010). Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives. Springer Verlag.
10. Principe, J., Xu, D., Zhao, Q., and Fisher, J. (2000). Learning from examples with information theoretic criteria. The Journal of VLSI Signal Processing, 26(1):61-77.
11. Rallo, R., Ferre-Giné, J., Arenas, A., and Giralt, F. (2002). Neural virtual sensor for the inferential prediction of product quality from process variables. Computers & Chemical Engineering, 26(12):1735-1754. (Pubitemid 35303612)
12. Scholkopf, B. and Smola, A. (2002). Learning with kernels, volume 64. Citeseer.
13. Silverman, B. (1986). Density Estimation for Statistics and Data Analysis. Number 26 in Monographs on statistics and applied probability.
14. Wang, P. and Vachtsevanos, G. (2001). Fault prognostics using dynamic wavelet neural networks. AI EDAM, 15(04):349-365. (Pubitemid 32975394)
15. Weber, A. (2007). Virtual metrology and your technology watch list: ten things you should know about this emerging technology. Future Fab International, 22:52-54.