Modelling and analysis of waviness reduction in soft-pad grinding of wire-sawn silicon wafers by support vector regression

被引:7
|
作者
Shen, Judong [1 ]
Pei, Z. J.
Lee, E. S.
Fisher, G. R.
机构
[1] Kansas State Univ, Dept Ind & Mfg Syst Engn, Manhattan, KS 66506 USA
[2] MEMC Elect Mat Inc, St Peters, MO 63376 USA
基金
美国国家科学基金会;
关键词
manufacturing; modelling; silicon wafer; waviness removal; statistical learning theory; support vector regression;
D O I
10.1080/00207540600558049
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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.
引用
收藏
页码:2605 / 2623
页数:19
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