Nonlinear and Isothermal Neural-Based Modeling of the Dual Gate MESFET

In this paper, the authors present neural-based large-signal and isothermal models for the dual gate MESFET as efficient alternatives to existing nonlinear models for such a complex device. The neural model is a combination of two submodels, namely, a static model represented by dc I-V characteristics and a dynamic model represented by pulsed I-V characteristics. The isothermal model is based on pulsed I-V measurements to better represent the RF device behavior and to neutralize the effect of channel self-heating on model accuracy. Insights on the discrepancy between model parameter values extracted from pulse and from dc measurements are also discussed. The measurement and model data are in very good agreement with global model errors of less than 1%.