A monitoring method of semiconductor manufacturing processes using Internet of Things-based big data analysis
DC Field | Value | Language |
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dc.contributor.author | Jang, Seok-Woo | - |
dc.contributor.author | Kim, Gye-Young | - |
dc.date.available | 2018-05-08T14:38:38Z | - |
dc.date.created | 2018-04-17 | - |
dc.date.issued | 2017-07 | - |
dc.identifier.issn | 1550-1477 | - |
dc.identifier.uri | http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/6308 | - |
dc.description.abstract | This article proposes an intelligent monitoring system for semiconductor manufacturing equipment, which determines spec-in or spec-out for a wafer in process, using Internet of Things-based big data analysis. The proposed system consists of three phases: initialization, learning, and prediction in real time. The initialization sets the weights and the effective steps for all parameters of equipment to be monitored. The learning performs a clustering to assign similar patterns to the same class. The patterns consist of a multiple time-series produced by semiconductor manufacturing equipment and an after clean inspection measured by the corresponding tester. We modify the Line, Buzo, and Gray algorithm for classifying the time-series patterns. The modified Line, Buzo, and Gray algorithm outputs a reference model for every cluster. The prediction compares a time-series entered in real time with the reference model using statistical dynamic time warping to find the best matched pattern and then calculates a predicted after clean inspection by combining the measured after clean inspection, the dissimilarity, and the weights. Finally, it determines spec-in or spec-out for the wafer. We will present experimental results that show how the proposed system is applied on the data acquired from semiconductor etching equipment. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SAGE PUBLICATIONS INC | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS | - |
dc.subject | CLASSIFICATION | - |
dc.subject | RECOGNITION | - |
dc.subject | INFORMATION | - |
dc.subject | SERVICES | - |
dc.subject | SYSTEM | - |
dc.subject | MODEL | - |
dc.title | A monitoring method of semiconductor manufacturing processes using Internet of Things-based big data analysis | - |
dc.type | Article | - |
dc.identifier.doi | 10.1177/1550147717721810 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, v.13, no.7 | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000406150200001 | - |
dc.identifier.scopusid | 2-s2.0-85026760828 | - |
dc.citation.number | 7 | - |
dc.citation.title | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS | - |
dc.citation.volume | 13 | - |
dc.contributor.affiliatedAuthor | Kim, Gye-Young | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.oadoiVersion | published | - |
dc.subject.keywordAuthor | Monitoring | - |
dc.subject.keywordAuthor | learning | - |
dc.subject.keywordAuthor | prediction | - |
dc.subject.keywordAuthor | matched pattern | - |
dc.subject.keywordAuthor | Internet of Things | - |
dc.subject.keywordAuthor | reference model | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | RECOGNITION | - |
dc.subject.keywordPlus | INFORMATION | - |
dc.subject.keywordPlus | SERVICES | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.subject.keywordPlus | MODEL | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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