Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 백동현 | - |
dc.date.accessioned | 2024-06-25T05:58:57Z | - |
dc.date.available | 2024-06-25T05:58:57Z | - |
dc.date.issued | 2022-12 | - |
dc.identifier.citation | 한국산업경영시스템학회지, v. 45, no 4, page. 86-98 | en_US |
dc.identifier.issn | 2005-0461 | en_US |
dc.identifier.issn | 2287-7975 | en_US |
dc.identifier.uri | https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002911640 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/190963 | - |
dc.description.abstract | Recently, many studies have been conducted to improve quality by applying machine learning models to semiconductor manu- facturing process data. However, in the semiconductor manufacturing process, the ratio of good products is much higher than that of defective products, so the problem of data imbalance is serious in terms of machine learning. In addition, since the number of features of data used in machine learning is very large, it is very important to perform machine learning by extracting only important features from among them to increase accuracy and utilization. This study proposes an anomaly detection method- ology that can learn excellently despite data imbalance and high-dimensional characteristics of semiconductor process data. The anomaly detection methodology applies the LIME algorithm after applying the SMOTE method and the RFECV method. The proposed methodology analyzes the classification result of the anomaly classification model, detects the cause of the anomaly, and derives a semiconductor process requiring action. The proposed methodology confirmed applicability and feasibility through application of cases. | en_US |
dc.description.sponsorship | This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2019S1A5C2A04083153) | en_US |
dc.language | ko | en_US |
dc.publisher | 한국산업경영시스템학회 | en_US |
dc.relation.ispartofseries | v. 45, no 4;86-98 | - |
dc.subject | Semiconductor Fabrication Process | en_US |
dc.subject | SMOTE | en_US |
dc.subject | RFECV | en_US |
dc.subject | Anomaly Detection | en_US |
dc.subject | LIME | en_US |
dc.title | LIME을 활용한 준지도 학습 기반 이상 탐지 모델: 반도체 공정을 중심으로 | en_US |
dc.title.alternative | Anomaly Detection Model Based on Semi-Supervised Learning Using LIME: Focusing on Semiconductor Process | en_US |
dc.type | Article | en_US |
dc.relation.no | 4 | - |
dc.relation.volume | 45 | - |
dc.identifier.doi | https://doi.org/10.11627/jksie.2022.45.4.086 | en_US |
dc.relation.page | 86-98 | - |
dc.relation.journal | 한국산업경영시스템학회지 | - |
dc.contributor.googleauthor | 안강민 | - |
dc.contributor.googleauthor | 신주은 | - |
dc.contributor.googleauthor | 백동현 | - |
dc.relation.code | 2022001575 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF BUSINESS AND ECONOMICS[E] | - |
dc.sector.department | SCHOOL OF BUSINESS ADMINISTRATION | - |
dc.identifier.pid | estarbaek | - |
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