Distribution-guided heuristic search for nonlinear parameter estimation with an application in semiconductor manufacturing
DC Field | Value | Language |
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dc.contributor.author | Kim, Hyungjin | - |
dc.contributor.author | Park, Chuljin | - |
dc.contributor.author | Kang, Yoonshik | - |
dc.date.accessioned | 2022-07-07T11:12:40Z | - |
dc.date.available | 2022-07-07T11:12:40Z | - |
dc.date.created | 2021-05-12 | - |
dc.date.issued | 2020-11 | - |
dc.identifier.issn | 2472-5854 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/144386 | - |
dc.description.abstract | Estimating a batch of parameter vectors of a nonlinear model is considered, where there exists a model interpreting the independent and the dependent variables, and the parameter vectors of the model are assumed to be sampled from a multivariate normal distribution. The mean vector and the covariance matrix of the parameter distribution can be assumed and such a parameter distribution is referred to as the hypothetical underlying distribution. A new framework is proposed, namely, the distribution-guided heuristic search framework, which uses the information of the hypothetical underlying distribution with the following two main concepts: (i) changing the coordinate of the parameter vectors via linear transformation and (ii) probabilistically filtering a parameter vector sampled by a heuristic algorithm. The framework is not a stand-alone algorithm, but it works with any heuristic algorithms to solve the target problem. The framework was tested in two simulation studies and was applied to a real example of measuring the critical dimensions of a 2-dimensional high-aspect-ratio structure of a wafer in semiconductor manufacturing. The test results show that a heuristic algorithm within the proposed framework outperforms the original heuristic algorithm as well as other existing algorithms. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | TAYLOR & FRANCIS INC | - |
dc.title | Distribution-guided heuristic search for nonlinear parameter estimation with an application in semiconductor manufacturing | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Park, Chuljin | - |
dc.identifier.doi | 10.1080/24725854.2019.1709135 | - |
dc.identifier.scopusid | 2-s2.0-85079217684 | - |
dc.identifier.wosid | 000515265400001 | - |
dc.identifier.bibliographicCitation | IISE TRANSACTIONS, v.52, no.11, pp.1246 - 1261 | - |
dc.relation.isPartOf | IISE TRANSACTIONS | - |
dc.citation.title | IISE TRANSACTIONS | - |
dc.citation.volume | 52 | - |
dc.citation.number | 11 | - |
dc.citation.startPage | 1246 | - |
dc.citation.endPage | 1261 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Operations Research & Management Science | - |
dc.relation.journalWebOfScienceCategory | Engineering, Industrial | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.subject.keywordPlus | GENETIC ALGORITHM | - |
dc.subject.keywordPlus | ELECTROMAGNETIC INDUCTION | - |
dc.subject.keywordPlus | OPTIMIZATION | - |
dc.subject.keywordPlus | SYSTEMS | - |
dc.subject.keywordPlus | MODELS | - |
dc.subject.keywordAuthor | Nonlinear parameter estimation | - |
dc.subject.keywordAuthor | inverse problem | - |
dc.subject.keywordAuthor | heuristic search | - |
dc.subject.keywordAuthor | optical critical dimension | - |
dc.subject.keywordAuthor | semiconductor manufacturing | - |
dc.identifier.url | https://www.tandfonline.com/doi/full/10.1080/24725854.2019.1709135 | - |
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