Use of adaptive network fuzzy inference system to predict plasma charging damage on electrical MOSFET properties
DC Field | Value | Language |
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dc.contributor.author | Kim, Byungwhan | - |
dc.contributor.author | Kwon, Hee Ju | - |
dc.contributor.author | Choi, Seongjin | - |
dc.date.accessioned | 2021-09-08T18:27:42Z | - |
dc.date.available | 2021-09-08T18:27:42Z | - |
dc.date.created | 2021-06-10 | - |
dc.date.issued | 2009-04 | - |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/120330 | - |
dc.description.abstract | A prediction model of plasma-induced charging damage is presented. The model was constructed using adaptive network fuzzy inference system (ANFIS). The prediction performance of ANFIS model was optimized as a function of training factors, including a step-size, a normalization factor, and type of membership function. Charging damage data were obtained from antenna-structured MOSFET with the variations in process parameters. For a systematic modeling, the experiment was characterized by means of a face-centered Box Wilson experiment. Electrical properties modeled include a threshold voltage (V), a subthreshold swing (S), and a transconductance (G). Both S and G were found to be considerably affected by the normalization factor. For the variations in the type of membership function, either V or S was the most significantly influenced. The optimized root mean square errors are about 0.041 (V), 5.040 (mV/decade), and 12.311 (x 10(-6)/Omega), respectively. Better predictions were demonstrated against statistical regression models and the improvements were even more than 15% for V and S models. (C) 2008 Elsevier Ltd. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.title | Use of adaptive network fuzzy inference system to predict plasma charging damage on electrical MOSFET properties | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Choi, Seongjin | - |
dc.identifier.doi | 10.1016/j.eswa.2008.07.034 | - |
dc.identifier.scopusid | 2-s2.0-58349094924 | - |
dc.identifier.wosid | 000263817100099 | - |
dc.identifier.bibliographicCitation | EXPERT SYSTEMS WITH APPLICATIONS, v.36, no.3, pp.6570 - 6573 | - |
dc.relation.isPartOf | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.citation.title | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.citation.volume | 36 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 6570 | - |
dc.citation.endPage | 6573 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Operations Research & Management Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.subject.keywordAuthor | Charging damage | - |
dc.subject.keywordAuthor | Metal-oxide-semiconcluctor field-effect transistors | - |
dc.subject.keywordAuthor | Adaptive network fuzzy inference system | - |
dc.subject.keywordAuthor | Plasma | - |
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