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

Byungwhan Kim; Donghwan Kim; Seung Soo Han

doi:10.1007/11760191_150

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

Byungwhan Kim, Donghwan Kim, Seung Soo Han

Dept. of Mechanical System and Design Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Advances in Neural Networks - ISNN 2006
Subtitle of host publication	Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III
Publisher	Springer Verlag
Pages	1028-1035
Number of pages	8
ISBN (Print)	3540344829, 9783540344827
DOIs	https://doi.org/10.1007/11760191_150
State	Published - 2006
Event	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu, China Duration: 28 May 2006 → 1 Jun 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3973 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
Country/Territory	China
City	Chengdu
Period	28/05/06 → 1/06/06

Access to Document

10.1007/11760191_150

Cite this

Kim, B., Kim, D., & Han, S. S. (2006). Prediction of radio frequency impedance matching in plasma equipment using neural network. In Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III (pp. 1028-1035). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3973 LNCS). Springer Verlag. https://doi.org/10.1007/11760191_150

Kim, Byungwhan ; Kim, Donghwan ; Han, Seung Soo. / Prediction of radio frequency impedance matching in plasma equipment using neural network. Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III. Springer Verlag, 2006. pp. 1028-1035 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{b652068923534983836b59ac442cf0cf,

title = "Prediction of radio frequency impedance matching in plasma equipment using neural network",

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.",

author = "Byungwhan Kim and Donghwan Kim and Han, \{Seung Soo\}",

year = "2006",

doi = "10.1007/11760191\_150",

language = "English",

isbn = "3540344829",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "1028--1035",

booktitle = "Advances in Neural Networks - ISNN 2006",

note = "3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks ; Conference date: 28-05-2006 Through 01-06-2006",

}

Kim, B, Kim, D & Han, SS 2006, Prediction of radio frequency impedance matching in plasma equipment using neural network. in Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3973 LNCS, Springer Verlag, pp. 1028-1035, 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks, Chengdu, China, 28/05/06. https://doi.org/10.1007/11760191_150

Prediction of radio frequency impedance matching in plasma equipment using neural network. / Kim, Byungwhan; Kim, Donghwan; Han, Seung Soo.
Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III. Springer Verlag, 2006. p. 1028-1035 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3973 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Kim B, Kim D, Han SS. Prediction of radio frequency impedance matching in plasma equipment using neural network. In Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III. Springer Verlag. 2006. p. 1028-1035. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11760191_150

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

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