Genetic algorithm initialized artificial neural network based temperature dependent small-signal modeling technique for GaN high electron mobility transistors
Jarndal A. Husain S. Hashmi M.
March 2021John Wiley and Sons Inc
International Journal of RF and Microwave Computer-Aided Engineering
2021#31Issue 3
This paper explores and develops efficient temperature-dependent small-signal modeling approaches for GaN high electron mobility transistors (HEMTs). The multilayer perceptron (MLP) architecture and cascaded MLP architecture of artificial neural network are employed to model temperature dependence of 2-mm GaN-on-silicon device. It is identified that both architectures face problem of dependence on initials values of weights and biases. To overcome this issue, the genetic algorithm (GA) is incorporated in both MLP and cascaded MLP architectures. The models are trained on a large set of operating conditions (bias voltages and ambient temperatures) over a frequency range of 0.1 to 20 GHz and then tested for both temperature interpolation and extrapolation cases to assess their accuracy and robustness. An excellent agreement between the measured and the modeled S-parameters over the entire frequency range demonstrate the quality and robustness of the proposed technique. It is also shown that the cascaded MLP with GA exhibits better performance but with increased complexity.
ANN , cascade MLP , GaN-on-silicon HEMT , genetic algorithm , MLP , small-signal modeling
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Electrical Engineering Department, University of Sharjah, Sharjah, United Arab Emirates
Electrical and Computer Engineering Department, Nazarbayev University, Astana, Kazakhstan
Electrical Engineering Department
Electrical and Computer Engineering Department
10 лет помогаем публиковать статьи Международный издатель
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