Title :
A Support Vector Machine Method for Electrothermal Modeling of Power FETs
Author :
Guo, Yunchuan ; Xu, Yuehang ; Wang, Lei ; Xu, Ruimin
Author_Institution :
Univ. of Electron. Sci. & Technol., Chengdu
Abstract :
An accurate electrothermal modeling method for power FETs is presented. The thermal models are setup by using Support Vector Machine Regression (SVR) approach, which is like artificial neural network (ANN) method leading a knowledge-based model. Unlike traditional ANNs, Support Vector Machine (SVM )method requires fewer samples in statistical learning and is free of local minima in optimization. A comparison among the SVM model, the empirical model and the measurement data of a GaAs power pHEMT are given out to validate the proposed approach.
Keywords :
III-V semiconductors; gallium arsenide; neural nets; power HEMT; power field effect transistors; regression analysis; semiconductor device models; support vector machines; GaAs; GaAs - Interface; artificial neural network; electrothermal modeling; knowledge-based model; power FET; power pHEMT; statistical learning; support vector machine regression; Artificial neural networks; Electrothermal effects; FETs; Gallium arsenide; Integrated circuit modeling; Microwave technology; Microwave theory and techniques; PHEMTs; Statistical learning; Support vector machines; Electrothermal model; field effect transistor (FET); support vector machine (SVM);
Conference_Titel :
Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, 2007 International Symposium on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4244-1045-3
Electronic_ISBN :
978-1-4244-1045-3
DOI :
10.1109/MAPE.2007.4393537
