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/CaN 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.