A Support Vector Regression Nonlinear Model for SiC MESFET

Author

Guo, Yunchuan ; Xu, Yuehang ; Xu, Ruimin ; Yan, Bo

Author_Institution

School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, P. R. China. Email: ycguo@uestc.edu.cn

fYear

2007

fDate

3-4 June 2007

Firstpage

153

Lastpage

156

Abstract

Support vector machine (SVM) regression approach is introduced in this paper for table-based nonlinear modeling of field effect transistors (FET). Support vector machine, which based on statistical learning theory and structural risk minimization (SRM) principle, is provided with good generalization ability. For the purpose of demonstration, a table-based SVM regression model is established using a set of training data and testing data produced by an available empirical nonlinear model of SiC MESFET. Experimental results are also given out to validate its good ability in predicting electrical performance.

Keywords

Capacitance; FETs; MESFETs; Predictive models; Risk management; Silicon carbide; Statistical learning; Support vector machine classification; Support vector machines; Training data; Nonlinear model; SiC MESFET; support vector machine (SVM);

fLanguage

English

Publisher

ieee

Conference_Titel

Electron Devices and Semiconductor Technology, 2007. EDST 2007. Proceeding of 2007 International Workshop on

Conference_Location

Tsinghua University

Print_ISBN

1-4244-1098-3

Electronic_ISBN

1-4244-1098-3

Type

conf

DOI

10.1109/EDST.2007.4289800

Filename

4289800