Enabling the development of accurate intrinsic parameter extraction model for GaN HEMT using support vector regression (SVR)

This study employs support vector regression (SVR) to develop an accurate and reliable intrinsic parameter extraction model for gallium nitride (GaN) high electron mobility transistors (HEMT) using two different geometries of 2 x 200 mu m and 4 x 100 mu m. The key aspect of the proposed approach is the use of nonlinear Gaussian kernel to transform the input space into a high-dimensional feature space. It then allows the application of learning technique to develop a reliable procedure for parameter extraction. The proposed extraction model of GaN HEMT has been developed for a broad range of frequency, from 1 to 18 GHz, with multi-biasing sets for HEMTs of two different geometries. Moreover, the proposed model is made scalable in terms of geometry parameters and therefore can be used to predict the intrinsic parameters and enumerate scaling efficiency of GaN HEMTs by investigating the geometry parameters. A good agreement is observed between the measured S-parameters and the proposed model for the complete frequency range. It is shown that the proposed approach is simple, novel and can be readily incorporated into computer-aided design tool for an accurate and expedited design process of RF and microwave circuits.