Journal
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Volume 44, Issue 13, Pages 2605-2623Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207540600558049
Keywords
manufacturing; modelling; silicon wafer; waviness removal; statistical learning theory; support vector regression
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The manufacturing of silicon wafers forms the most important step in the construction of integrated circuit (IC) chips. One of the difficulties in this manufacture process is the removal of the waviness from the resulting wafers. In this paper, mathematical modelling and analysis of this removal process is carried out by the use of the support vector regression (SVR) algorithm. The results show that SVR is ideally suited for the modelling of this complicated process. Furthermore, by the use of the learning ability of SVR, the model can be continuously improved as more data become available. Based on the resulting model, the influences of the various factors on the rate of removal and the ease of control of the removal process are also discussed.
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