Modeling of an inductively-coupled Cl-2/Ar plasma using neural network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Moonkeun | - |
dc.contributor.author | Jang, Hanbyeol | - |
dc.contributor.author | Lee, Yong-Hwa | - |
dc.contributor.author | Kwon, Kwang-Ho | - |
dc.contributor.author | Park, Kang-Bak | - |
dc.date.accessioned | 2021-09-06T14:08:33Z | - |
dc.date.available | 2021-09-06T14:08:33Z | - |
dc.date.created | 2021-06-15 | - |
dc.date.issued | 2012-10-30 | - |
dc.identifier.issn | 0040-6090 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/107174 | - |
dc.description.abstract | In this study, a neural network model for an inductively-coupled Cl-2/Ar plasma (ICP) process has been proposed. Plasma experiments were performed in a planar inductively coupled plasma reactor. The input parameters considered for plasma modeling were the gas mixing ratio, and the source and bias powers, which were varied in the ranges 0-100% Cl-2/Ar, 500-800 W, and 50-300 W, respectively. Plasma diagnostics were performed by double Langmuir probe measurements. Analysis of voltage-current curves in order to obtain electron temperature and total positive ion density was carried out. A back propagation neural network model with a pre-processor was constructed. The prediction errors for the proposed model were shown to be very small compared to those of a conventional neural network model. In the experiments conducted, it was found that an increase in Ar mixing ratio or input source power resulted in an increase in the total positive ion density. An increase in the input bias power, however, corresponded to a decrease in the total positive ion density. The electron temperature increased as the Ar fraction, input source power, or input bias power increased. (c) 2012 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE SA | - |
dc.subject | ZRO2 THIN-FILMS | - |
dc.subject | GLOBAL-MODEL | - |
dc.subject | HIGH-DENSITY | - |
dc.subject | HFO2 FILMS | - |
dc.subject | SI | - |
dc.subject | MECHANISM | - |
dc.subject | O-2 | - |
dc.title | Modeling of an inductively-coupled Cl-2/Ar plasma using neural network | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kwon, Kwang-Ho | - |
dc.contributor.affiliatedAuthor | Park, Kang-Bak | - |
dc.identifier.doi | 10.1016/j.tsf.2012.03.076 | - |
dc.identifier.wosid | 000309905900010 | - |
dc.identifier.bibliographicCitation | THIN SOLID FILMS, v.521, pp.38 - 41 | - |
dc.relation.isPartOf | THIN SOLID FILMS | - |
dc.citation.title | THIN SOLID FILMS | - |
dc.citation.volume | 521 | - |
dc.citation.startPage | 38 | - |
dc.citation.endPage | 41 | - |
dc.type.rims | ART | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Coatings & Films | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.relation.journalWebOfScienceCategory | Physics, Condensed Matter | - |
dc.subject.keywordPlus | ZRO2 THIN-FILMS | - |
dc.subject.keywordPlus | GLOBAL-MODEL | - |
dc.subject.keywordPlus | HIGH-DENSITY | - |
dc.subject.keywordPlus | HFO2 FILMS | - |
dc.subject.keywordPlus | SI | - |
dc.subject.keywordPlus | MECHANISM | - |
dc.subject.keywordPlus | O-2 | - |
dc.subject.keywordAuthor | Neural network | - |
dc.subject.keywordAuthor | Pre-processor | - |
dc.subject.keywordAuthor | Cl-2/Ar | - |
dc.subject.keywordAuthor | Inductively coupled plasma | - |
dc.subject.keywordAuthor | Langmuir probe | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.