Nonlinear AlGaN/GaN HEMT model using multiple artificial neural networks

In this work, a complete nonlinear-transistor-model extraction-method is described. As a case study, the AlGaN/GaN High Electron Mobility Transistor manufactured on SiC substrate is modeled. The parasitic components model is proposed, and its extraction results are presented. Low- and high-frequency...

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Hauptverfasser: Barmuta, P., Plonski, P., Czuba, K., Avolio, G., Schreurs, D.
Format: Tagungsbericht
Sprache:eng
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Zusammenfassung:In this work, a complete nonlinear-transistor-model extraction-method is described. As a case study, the AlGaN/GaN High Electron Mobility Transistor manufactured on SiC substrate is modeled. The parasitic components model is proposed, and its extraction results are presented. Low- and high-frequency large-signal measurement data are involved in order to produce multiple artificial neural networks. The network topologies of multilayer perceptron networks are established automatically. A complete learning procedure using back propagation algorithm is described. A good agreement between the measurement data and the model has been observed.
DOI:10.1109/MIKON.2012.6233556