{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T15:29:55Z","timestamp":1760369395428},"reference-count":34,"publisher":"SAGE Publications","issue":"7","license":[{"start":{"date-parts":[[2017,7,1]],"date-time":"2017-07-01T00:00:00Z","timestamp":1498867200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Distributed Sensor Networks"],"published-print":{"date-parts":[[2017,7]]},"abstract":"<jats:p> 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\u2013based 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. <\/jats:p>","DOI":"10.1177\/1550147717721810","type":"journal-article","created":{"date-parts":[[2017,7,22]],"date-time":"2017-07-22T06:59:49Z","timestamp":1500706789000},"page":"155014771772181","update-policy":"http:\/\/dx.doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["A monitoring method of semiconductor manufacturing processes using Internet of Things\u2013based big data analysis"],"prefix":"10.1177","volume":"13","author":[{"given":"Seok-Woo","family":"Jang","sequence":"first","affiliation":[{"name":"Department of Digital Media, Anyang University, Anyang, Korea"}]},{"given":"Gye-Young","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Software, Soongsil University, Seoul, 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