Diagnosis model of radio frequency impedance matching in plasma equipment by using neural network and wavelets

Authors:
Byungwhan Kim;Jae Young Park;Dong Hwan Kim;Seung Soo Han
Affiliations:
Department of Electronic Engineering, Sejong University, Seoul, Korea;Department of Electronic Engineering, Kwangwoon University, Seoul, Korea;School of Mechanical Design and Automation Engineering, Seoul National University of Technology, Seoul, Korea;Department of Information Engineering, Myongji University, Yongin, Korea
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

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Abstract

A new calibration model for plasma diagnosis was constructed by combining radio frequency impedance match data, wavelet, and neural network. A total of 30 fault symptoms were simulated with the variations in the four process parameters. Both discrete wavelet transformation (DWT) and continuous wavelet transformation (CWT) were utilized to filter the sensor information. Three types of diagnosis models (raw-, DWT-, and CWT-based models) were constructed. The comparisons revealed that the improvement in the prediction performance of DWT and CWT data models over the raw data model were about 42% and 30%, respectively.