Prediction of radio frequency impedance matching in plasma equipment using neural network

  • Authors:

  • Byungwhan Kim;Donghwan Kim;Seung Soo Han

  • Affiliations:

  • Department of Electronic Engineering, Sejong University, Seoul, Korea;School of Mechanical Design & Automation Engineering, Seoul National University of Technology, Seoul, Korea;Department of Information Engineering, Myongji University, Yongin, Korea

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2006

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Abstract

Optimizing a plasma impedance match process requires construction of prediction model. In this study, generalized regression neural network (GRNN) combined with genetic algorithm (GA) was used to build a match prediction model. A real-time match monitor system was used to collect steady match positions according to a statistical experimental design. GA-GRNN models were compared to GRNN and statistical regression models. Compared to GRNN models, GA-GRNN models demonstrated improved predictions of about 81% and 77% for the impedance and phase positions, respectively. With respect to statistical regression models, GA-GRNN models yielded an improvement of about 80% and 78%, respectively. Moreover, for either model type, the improvements for the training errors were more than about 90% for both positions.