System Identification Methods for Plasma Enhanced Chemical Vapor Deposition
Conference Paper, Proceedings of 13th IFAC World Congress, Vol. 29, No. 1, pp. 6113 - 6118, June, 1996
Abstract
The introduction of new in situ sensing creates the possibility of directly controlling critical process variables in plasma enhanced chemical vapor deposition systems (PECVD). The complexity of this process makes it necessary to develop empirical models of the system dynamics. This paper describes the experimental and numerical procedures for identifying both transfer function and recurrent neural net multi-input multi-output models of PECVD dynamics. The models are compared in terms of the resulting mean-squared-prediction-error for verification data sets. The neural net models are shown to be mildly superior in cases where the input signals vary widely, stimulating the nonlinear response of the system.
BibTeX
@conference{Gibson-1996-14174,author = {M. Gibson and Enrique Ferreira and X. Cheng and T. Knight and Dave Greve and Bruce Krogh},
title = {System Identification Methods for Plasma Enhanced Chemical Vapor Deposition},
booktitle = {Proceedings of 13th IFAC World Congress},
year = {1996},
month = {June},
volume = {29},
pages = {6113 - 6118},
}
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.