An extraction technique for small signal intrinsic parameters of HEMTs based on artificial neural networks

This paper presents a fast and accurate procedure for extraction of small signal intrinsic parameters of AlGaAs/GaAs high electron mobility transistors (HEMTs) using artificial neural network (ANN) techniques. The extraction procedure has been done in a wide range of frequencies and biases at various temperatures. Intrinsic parameters of HEMT are acquired using its values of common-source S-parameters. Two different ANN structures have been constructed in this work to extract the parameters, multi layer perceptron (MLP) and radial basis function (RBF) neural networks. These two kinds of ANNs are compared to each other in terms of accuracy, speed and memory usage. To validate the capability of the proposed method in small signal modeling of GaAs HEMTs, data and modeled values of S-parameters of a 200 μm gate width 0.25 μm GaAs HEMT are compared to each other and very good agreement between them is achieved up to 30 GHz. The effect of bias, temperature and frequency conditions on the extracted parameters of HEMT has been investigated, and the obtained results match the theoretical expectations. The proposed model can be inserted to computer-aided design (CAD) tools in order to have an accurate and fast design, simulation and optimization of microwave circuits including GaAs HEMTs.