Adaptive Network-Based Fuzzy Inference Model of Plasma Enhanced Chemical Vapor Deposition Process

Authors:
Byungwhan Kim;Seongjin Choi
Affiliations:
Department of Electronic Engineering, Sejong University, Seoul, Korea;Department of Electronics and Information Engineering, Korea University, Yeongi, Korea
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

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Design and Analysis of Experiments

Design and Analysis of Experiments
Prediction of plasma enhanced deposition process using GA-Optimized GRNN

ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III

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Abstract

In this study, a prediction model of plasma enhanced chemical deposition (PECVD) data was constructed by using an adaptive network-based fuzzy inference system (ANFIS). The PECVD process was characterized by means of a Box Wilson statistical experiment. The film characteristics modeled are deposition rate and stored charge. The prediction performance of ANFIS models was evaluated as a function of training factors, including the step-size, type of membership functions, and normalization factor of inputs-output pairs. The effects of each training factor were sequentially optimized. The root mean square errors of optimized deposition rate and charge models were 11.94 Å/min and 1.37 ×1012/cm2, respectively. Compared to statistical regression models, ANFIS models yielded an improvement of more than 20%. This indicates that ANFIS can effectively capture nonlinear plasma dynamics.