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Optimizing a blend of a mixture slurry in chemical mechanical planarization for advanced semiconductor manufacturing using a posterior preference articulation approach to dual response surface optimization

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dc.contributor.authorSeo, Jihoon-
dc.contributor.authorLee, Dong-Hee-
dc.contributor.authorLee, Kangchun-
dc.contributor.authorKim, Kijung-
dc.contributor.authorKim, Kwang-Jae-
dc.date.accessioned2024-01-10T03:37:19Z-
dc.date.available2024-01-10T03:37:19Z-
dc.date.issued2016-09-
dc.identifier.issn1524-1904-
dc.identifier.issn1526-4025-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/194049-
dc.description.abstractSemiconductors are fabricated through unit processes including photolithography, etching, diffusion, ion implantation, deposition, and planarization processes. Chemical mechanical planarization, which is essential in advanced semiconductor manufacturing processes, aims to achieve high planarity across the wafer surface. This paper presents a case study in which the optimal blend of mixture slurry was obtained to improve the two response variables (material loss and roughness) at the same time. The mixture slurry consists of several pure slurries; when all of the abrasive particles within the slurry are of the same size, the slurry is referred to as a pure slurry. The optimal blend was obtained by applying a multiresponse surface optimization method. In particular, the recently developed posterior approach to dual response surface optimization was employed, which allows the chemical mechanical planarization process engineer to investigate tradeoffs between the two response variables. The two responses were better with the obtained blend than the existing blend.-
dc.format.extent12-
dc.language영어-
dc.language.isoENG-
dc.publisherJohn Wiley & Sons Inc.-
dc.titleOptimizing a blend of a mixture slurry in chemical mechanical planarization for advanced semiconductor manufacturing using a posterior preference articulation approach to dual response surface optimization-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1002/asmb.2185-
dc.identifier.scopusid2-s2.0-84979021479-
dc.identifier.wosid000386064600009-
dc.identifier.bibliographicCitationApplied Stochastic Models in Business and Industry, v.32, no.5, pp 648 - 659-
dc.citation.titleApplied Stochastic Models in Business and Industry-
dc.citation.volume32-
dc.citation.number5-
dc.citation.startPage648-
dc.citation.endPage659-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaOperations Research & Management Science-
dc.relation.journalResearchAreaMathematics-
dc.relation.journalWebOfScienceCategoryOperations Research & Management Science-
dc.relation.journalWebOfScienceCategoryMathematics, Interdisciplinary Applications-
dc.relation.journalWebOfScienceCategoryStatistics & Probability-
dc.subject.keywordPlusMULTIPLE RESPONSES-
dc.subject.keywordAuthorCMP-
dc.subject.keywordAuthorsemiconductor-
dc.subject.keywordAuthorslurry-
dc.subject.keywordAuthormulti-response surface optimization-
dc.subject.keywordAuthordual response surface optimization-
dc.identifier.urlhttps://onlinelibrary.wiley.com/doi/10.1002/asmb.2185-
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