Multi-step ART1 algorithm for recognition of defect patterns on semiconductor wafers
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Gyunghyun | - |
dc.contributor.author | Kim, Sung-Hee | - |
dc.contributor.author | Ha, Chunghun | - |
dc.contributor.author | Bae, Suk Joo | - |
dc.date.accessioned | 2022-07-16T15:05:20Z | - |
dc.date.available | 2022-07-16T15:05:20Z | - |
dc.date.created | 2021-05-12 | - |
dc.date.issued | 2012-06 | - |
dc.identifier.issn | 0020-7543 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/165381 | - |
dc.description.abstract | The integrated circuits (ICs) on wafers are highly vulnerable to defects generated during the semiconductor manufacturing process. The spatial patterns of locally clustered defects are likely to contain information related to the defect generating mechanism. For the purpose of yield management, we propose a multi-step adaptive resonance theory (ART1) algorithm in order to accurately recognise the defect patterns scattered over a wafer. The proposed algorithm consists of a new similarity measure, based on the p-norm ratio and run-length encoding technique and pre-processing procedure: the variable resolution array and zooming strategy. The performance of the algorithm is evaluated based on the statistical models for four types of simulated defect patterns, each of which typically occurs during fabrication of ICs: random patterns by a spatial homogeneous Poisson process, ellipsoid patterns by a multivariate normal, curvilinear patterns by a principal curve, and ring patterns by a spherical shell. Computational testing results show that the proposed algorithm provides high accuracy and robustness in detecting IC defects, regardless of the types of defect patterns residing on the wafer. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | TAYLOR & FRANCIS LTD | - |
dc.title | Multi-step ART1 algorithm for recognition of defect patterns on semiconductor wafers | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Choi, Gyunghyun | - |
dc.contributor.affiliatedAuthor | Bae, Suk Joo | - |
dc.identifier.doi | 10.1080/00207543.2011.574502 | - |
dc.identifier.scopusid | 2-s2.0-84863209634 | - |
dc.identifier.wosid | 000305705600007 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, v.50, no.12, pp.3274 - 3287 | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH | - |
dc.citation.title | INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH | - |
dc.citation.volume | 50 | - |
dc.citation.number | 12 | - |
dc.citation.startPage | 3274 | - |
dc.citation.endPage | 3287 | - |
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 | Engineering, Manufacturing | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.subject.keywordPlus | NEURAL-NETWORK APPROACH | - |
dc.subject.keywordPlus | SPATIAL-PATTERN | - |
dc.subject.keywordPlus | BIN MAP | - |
dc.subject.keywordPlus | YIELD | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | FABRICATION | - |
dc.subject.keywordAuthor | spatial defects | - |
dc.subject.keywordAuthor | neural network | - |
dc.subject.keywordAuthor | pattern recognition | - |
dc.subject.keywordAuthor | similarity | - |
dc.subject.keywordAuthor | wafer map | - |
dc.subject.keywordAuthor | yield management | - |
dc.identifier.url | https://www.tandfonline.com/doi/full/10.1080/00207543.2011.574502 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.