NDLI: A Support Vector Machine Method for Electrothermal Modeling of Power FETs

Content Provider	IEEE Xplore Digital Library
Author	Yunchuan Guo Yuehang Xu Lei Wang Ruimin Xu
Copyright Year	2007
Description	Author affiliation: Univ. of Electron. Sci. & Technol., Chengdu (Yunchuan Guo; Yuehang Xu; Lei Wang; Ruimin Xu)
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.
Sponsorship	IEEE Beijing Sect.
Starting Page	1387
Ending Page	1389
File Size	992351
Page Count	3
File Format	PDF
ISBN	9781424410446
DOI	10.1109/MAPE.2007.4393537
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrothermal model Gallium arsenide Statistical learning PHEMTs support vector machine (SVM) Electrothermal effects Artificial neural networks field effect transistor (FET) Support vector machines Microwave technology Microwave theory and techniques Integrated circuit modeling FETs
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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