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Cited 13 time in webofscience Cited 13 time in scopus
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dc.contributor.authorDong Hee Lee-
dc.contributor.authorKim, KJ-
dc.date.accessioned2016-03-31T08:20:16Z-
dc.date.available2016-03-31T08:20:16Z-
dc.date.created2014-02-06-
dc.date.issued2013-07-
dc.identifier.issn1524-1904-
dc.identifier.other2013-OAK-0000028713-
dc.identifier.urihttps://oasis.postech.ac.kr/handle/2014.oak/15080-
dc.description.abstractAfter Cleaning Inspection Critical Dimension (ACICD), one of the main variables in the etch process, affects the electrical characteristics of fabricated semiconductor chips. Its target value should be determined to minimize the bias and variability of these electrical characteristics. This paper presents a case study in which the target value of ACICD is determined by the dual response optimization method. In particular, the recently developed posterior approach to dual response optimization is employed allowing the analyst to determine easily the optimal compromise between bias and variability in the electrical characteristics. The performance at the obtained optimal ACICD setting has been shown to be better than that at the existing setting. Copyright (c) 2013 John Wiley & Sons, Ltd.-
dc.description.statementofresponsibilityX-
dc.languageEnglish-
dc.publisherJohn Wiley & Sons, Ltd.-
dc.relation.isPartOfApplied Stochastic Models in Business and Industry-
dc.subjectACICD-
dc.subjectsemiconductor-
dc.subjectdual response optimization-
dc.subjectPREFERENCE ARTICULATION APPROACH-
dc.subjectMULTIRESPONSE OPTIMIZATION-
dc.titleDetermining the target value of ACICD to optimize the electrical characteristics of semiconductors using dual response surface optimization-
dc.typeArticle-
dc.contributor.college산업경영공학과-
dc.identifier.doi10.1002/ASMB.1973-
dc.author.googleLee, DH-
dc.author.googleKim, KJ-
dc.relation.volume29-
dc.relation.issue4-
dc.relation.startpage377-
dc.relation.lastpage386-
dc.contributor.id10084322-
dc.relation.journalApplied Stochastic Models in Business and Industry-
dc.relation.indexSCI급, SCOPUS 등재논문-
dc.relation.sciSCI-
dc.collections.nameJournal Papers-
dc.type.rimsART-
dc.identifier.bibliographicCitationApplied Stochastic Models in Business and Industry, v.29, no.4, pp.377 - 386-
dc.identifier.wosid000322918800006-
dc.date.tcdate2019-01-01-
dc.citation.endPage386-
dc.citation.number4-
dc.citation.startPage377-
dc.citation.titleApplied Stochastic Models in Business and Industry-
dc.citation.volume29-
dc.contributor.affiliatedAuthorKim, KJ-
dc.identifier.scopusid2-s2.0-84882449592-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.wostc8-
dc.description.scptc8*
dc.date.scptcdate2018-05-121*
dc.type.docTypeArticle-
dc.subject.keywordAuthorACICD-
dc.subject.keywordAuthorsemiconductor-
dc.subject.keywordAuthordual response optimization-
dc.relation.journalWebOfScienceCategoryOperations Research & Management Science-
dc.relation.journalWebOfScienceCategoryMathematics, Interdisciplinary Applications-
dc.relation.journalWebOfScienceCategoryStatistics & Probability-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaOperations Research & Management Science-
dc.relation.journalResearchAreaMathematics-

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김광재KIM, KWANG JAE
Dept. of Industrial & Management Eng.
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