Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이동희 | - |
dc.date.accessioned | 2019-03-12T01:04:40Z | - |
dc.date.available | 2019-03-12T01:04:40Z | - |
dc.date.issued | 2016-10 | - |
dc.identifier.citation | APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, v. 32, No.5, Page. 648-659 | en_US |
dc.identifier.issn | 1524-1904 | - |
dc.identifier.issn | 1526-4025 | - |
dc.identifier.uri | https://onlinelibrary.wiley.com/doi/full/10.1002/asmb.2185 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/100699 | - |
dc.description.abstract | Semiconductors 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. Copyright (c) 2016 John Wiley & Sons, Ltd. | en_US |
dc.description.sponsorship | This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2015R1C1A1A01051952). | en_US |
dc.language.iso | en | en_US |
dc.publisher | WILEY-BLACKWELL | en_US |
dc.subject | CMP | en_US |
dc.subject | semiconductor | en_US |
dc.subject | slurry | en_US |
dc.subject | multi-response surface optimization | en_US |
dc.subject | dual response surface optimization | en_US |
dc.title | 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 | en_US |
dc.type | Article | en_US |
dc.relation.volume | 32 | - |
dc.identifier.doi | 10.1002/asmb.2185 | - |
dc.relation.page | 648-659 | - |
dc.relation.journal | APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY | - |
dc.contributor.googleauthor | Seo, Jihoon | - |
dc.contributor.googleauthor | Lee, Dong-Hee | - |
dc.contributor.googleauthor | Lee, Kangchun | - |
dc.contributor.googleauthor | Kim, Kijung | - |
dc.contributor.googleauthor | Kim, Kwang-Jae | - |
dc.relation.code | 2016004275 | - |
dc.sector.campus | S | - |
dc.sector.daehak | DIVISION OF INDUSTRIAL INFORMATION STUDIES[S] | - |
dc.sector.department | DIVISION OF INDUSTRIAL INFORMATION STUDIES | - |
dc.identifier.pid | dh | - |
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