{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T13:32:08Z","timestamp":1762522328815},"reference-count":24,"publisher":"World Scientific Pub Co Pte Lt","issue":"08","funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["NRF-2018R1D1A1B07041663"],"award-info":[{"award-number":["NRF-2018R1D1A1B07041663"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Soft. Eng. Knowl. Eng."],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p> The semiconductor manufacturing process is very complex, and it is the most important part of the semiconductor industry. In order to test whether or not wafers are functioning normally, a pass\/fail test is conducted; however, time and cost needed for this testing increase as the number of chips increases. To address this, a machine learning technique is adopted and a high-performance classifier is needed to determine whether a pass\/fail test is accurate or not. In this paper, a deep belief network (DBN)-based multi-classifier is proposed for fault detection prediction in the semiconductor manufacturing process. The proposed method consists of two phases: The first phase is a data pre-processing phase in which features required for semiconductor data sets are extracted and the imbalance problem is solved. The second phase is to configure the multi-DBN using selected features. A DBN classifier is created for each feature and, finally, fault detection prediction is performed. The proposed method showed excellent performance and can be used in the semiconductor manufacturing process efficiently. <\/jats:p>","DOI":"10.1142\/s0218194019400126","type":"journal-article","created":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T06:26:07Z","timestamp":1568960767000},"page":"1125-1139","source":"Crossref","is-referenced-by-count":13,"title":["Fault Detection Prediction Using a Deep Belief Network-Based Multi-Classifier in the Semiconductor Manufacturing Process"],"prefix":"10.1142","volume":"29","author":[{"given":"Jae Kwon","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Medical Informatics, College of Medicine, The Catholic University of Seoul, Banpo-daero 222, Seocho-gu, Seoul 06591, South Korea"}]},{"given":"Jong Sik","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, South Korea"}]},{"given":"Young Shin","family":"Han","sequence":"additional","affiliation":[{"name":"Frontier College, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, South Korea"}]}],"member":"219","published-online":{"date-parts":[[2019,9,19]]},"reference":[{"issue":"1","key":"S0218194019400126BIB001","first-page":"46","volume":"38","author":"Kang P.","year":"2012","journal-title":"J. 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