期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 39, 期 4, 页码 4075-4083出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2011.09.088
关键词
Novelty detection; Faulty wafer detection; Semiconductor manufacturing; Virtual metrology; Dimensionality reduction
类别
资金
- Brain Korea 21 program
- Seoul RD Program [TR080589M0209722]
- NRF (National Research Foundation)
- Ministry of Education, Science and Technology (MEST) [400-20110010, 2011-0021893]
- Engineering Research Institute of SNU
- National Research Foundation of Korea [2011-0021893, 과C6A2503] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Since semiconductor manufacturing consists of hundreds of processes, a faulty wafer detection system, which allows for earlier detection of faulty wafers, is required. statistical process control (SPC) and virtual metrology (VM) have been used to detect faulty wafers. However, there are some limitations in that SPC requires linear, unimodal and single variable data and VM underestimates the deviations of predictors. In this paper, seven different machine learning-based novelty detection methods were employed to detect faulty wafers. The models were trained with Fault Detection and Classification (FDC) data to detect wafers having faulty metrology values. The real world semiconductor manufacturing data collected from a semiconductor fab were tested. Since the real world data have more than 150 input variables, we employed three different dimensionality reduction methods. The experimental results showed a high True Positive Rate (TPR). These results are promising enough to warrant further study. (C) 2011 Elsevier Ltd. All rights reserved.
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