Knowledge-based neural network (KBNN) modeling of HBT junction temperature and thermal resistance from electrical measurements

A knowledge-based neural network (KBNN) modeling and analysis method is presented for determining junction temperature, Tj, and thermal resistance, Rth, from simple electrical measurements of HBTs. The method retains sound physical principles of classical approaches but provides significant addition...

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Bibliographic Details
Published in2017 IEEE MTT-S International Microwave Symposium (IMS) pp. 1069 - 1071
Main Authors Iwamoto, Masaya, Jianjun Xu, Wenfan Zhou, Root, David E.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2017
Subjects
Artificial neural networks (ANNs)
device modeling
Electrical resistance measurement
Heterojunction bipolar transistors
heterojunction bipolar transistors (HBTs)
Junctions
Temperature distribution
Temperature measurement
thermal modeling
Thermal resistance
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Summary:A knowledge-based neural network (KBNN) modeling and analysis method is presented for determining junction temperature, Tj, and thermal resistance, Rth, from simple electrical measurements of HBTs. The method retains sound physical principles of classical approaches but provides significant additional practical benefits for modeling and prediction based on the mathematical properties of neural networks when endowed with additional a priori "knowledge". The method returns explicit, infinitely differentiable approximations for Tj and Rth as functions of ambient temperature, Tamb, and power dissipation, Pdiss. The method enables reliable predictions of Tj over a very wide range (e.g. 30°C to 260°C) by working with any complete set of experimental variables. The method also provides an automatically trained, measurement-based DC electro-thermal transistor model as a function of bias and temperature.
DOI:10.1109/MWSYM.2017.8058777