Machine Learning-Based Large-Signal Parameter Extraction for ASM-HEMT

A new machine learning (ML)-based large-signal parameter extraction for ASM-HEMT model has been presented for the first time. The proposed technique uses a 20k training sample generated by Monte Carlo simulations. The training samples of simulated output power
$P_{\text {out}}$
and power-added efficiency (PAE) are used to train an ML extractor to extract the ASM-HEMT model parameters. The trained ML extractor has been evaluated on measurements performed on a commercial GaN device which was previously modeled using ASM-HEMT using manual extraction. The results show that the ML extractor could extract ASM-HEMT large-signal parameters to model
$P_{\text {out}}$
, gain, and PAE, producing a level of accuracy comparable to the conventional manual parameter extraction. The proposed parameter extraction technique takes less than a second while removing the complexity and the need for expertise for extraction. This shows the promise of ML toward parameter extraction for large-signal models.