Two neural approaches for small-signal modelling of GaAs HEMTs

Focus of this paper is on the neural approach in small-signal modelling of GaAs HEMTs. Two modelling approaches based on artificial neural networks are discussed and compared. The first approach is completely based on artificial neural networks, while the second is a hybrid approach putting together artificial neural networks and an equivalent circuit representation of a microwave transistor. Both models consider the device gate width and therefore both are scalable. Results of modelling of three different AlGaAs/GaAs HEMTs in a wide range of operating bias conditions using the considered approaches are given. Different modelling aspects are discussed. A special attention is paid to the model development procedure and accuracy of the models.