MIM capacitor modeling by support vector regression

被引：9
|
作者：
Yang, Z. Q. [1 ]
Yang, T. [1 ]
Liu, Y. [1 ]
Han, S. H. [1 ]
机构：
[1] Univ Elect Sci & Technol China, Microwave Ctr, Chengdu 610054, Peoples R China
来源：
JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS | 2008年 / 22卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Data sets - Learning machines - Minimization principle - Support vector regression (SVR) method;
D O I：
10.1163/156939308783122788
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The support vector regression (SVR) method is introduced to model the MIM capacitor in this paper. SVM is a type of learning machine based on the statistical learning theory, which implements the structural risk minimization principle to obtain a good generalization from limited size data sets. The SVR model of the MIM capacitor is trained and tested by using the data generated from EM simulation. Once the model is constructed, it can provides results approaching the accuracy of the EM simulated results without increasing the analysis time significantly, which proves the validity of the method.
引用
收藏
页码：61 / 67
页数：7
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