Device modeling with NVNAs and X-parameters

This paper reviews and contrasts two complementary device modeling approaches based on data readily obtainable from a nonlinear vector network analyzer (NVNA). The first approach extends the application of waveform data to improve the characterization, parameter extraction, and validation methodologies for “compact” transistor models. NVNA data is used to train artificial neural network -based constitutive relations depending on multiple coupled dynamic variables, including temperature and trap states for an advanced compact model suitable for GaAs and GaN transistors. The second approach is based on load-dependent X-parameters*, measured using an output tuner working with the NVNA. It is demonstrated that X-parameters measured versus load at the fundamental frequency predict well the independent effects of harmonic load tuning on a 10W GaN packaged transistor without having to independently control harmonic loads during characterization. A comparison of the respective merits of the two approaches is presented.