Lifter assignment algorithm of overhead hoist transport system in multi-floor semiconductor fab using deep learning다층 구조 반도체 펩 내 자동 반송 시스템의 딥러닝 기반 리프터 선택

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dc.contributor.advisorJang, Young Jae-
dc.contributor.advisor장영재-
dc.contributor.authorMoon, Hangyul-
dc.date.accessioned2022-04-21T19:31:22Z-
dc.date.available2022-04-21T19:31:22Z-
dc.date.issued2021-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=963728&flag=dissertationen_US
dc.identifier.urihttp://hdl.handle.net/10203/295335-
dc.description학위논문(석사) - 한국과학기술원 : 산업및시스템공학과, 2021.8,[iv, 33 p. :]-
dc.description.abstractThis paper presents a lifter assignment problem in an overhead hoist transport (OHT) system in a multifloor semiconductor fab where each floor is connected via a lifter. Previous studies provided rule-based methods to consider only input port status and the expected transfer time. As the size of the fab has been increased, more factors should be considered when choosing a lifter, however, rule-based methods are limited in the increase of factors to be considered. In this work, we analyze and propose field-available methods by considering various factors affecting the lifter assignment. In this study, we propose the lifter assignment method that includes various factors that existing methods have not considered and verify performance improvement by using deep neural networks. Validation used Applied Materials’ AutoMod™ (version 14.0) simulation software.-
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectOHT system▼aDeep learning▼aLifter▼aSemiconductor fab▼aMulti floor fab-
dc.subject자동반송 시스템▼a심층신경망▼a리프터▼a반도체 펩▼a다층 구조 펩-
dc.titleLifter assignment algorithm of overhead hoist transport system in multi-floor semiconductor fab using deep learning-
dc.title.alternative다층 구조 반도체 펩 내 자동 반송 시스템의 딥러닝 기반 리프터 선택-
dc.typeThesis(Master)-
dc.identifier.CNRN325007-
dc.description.department한국과학기술원 :산업및시스템공학과,-
dc.contributor.alternativeauthor문한결-
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