Electron Device Model Parameter Identification Through Large-Signal-Predictive Small-Signal-Based Error Functions

Empirical electron device models based on lumped equivalent circuits are usually identified through nonlinear optimization procedures, which are based on the best fitting between the extrinsic model behavior and measurements carried out under multibias static and small-signal excitations. In this paper, a new error function is proposed for equivalent circuit model parameter optimization. Although still being defined through standard static and small-signal measurement data, the new error function can be configured so as to obtain models tailored to specific large-signal applications. Experimental results, which confirm the validity of the proposed identification approach, are provided for a GaAs microwave pseudomorphic HEMT model aimed at the design of highly linear power amplifiers.