Virtual sensors for semiconductor manufacturing: A nonparametric approach - Exploiting 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 quadratic 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. © 2013 Springer-Verlag.