Abstract
Current study results on Machine Learning have been published. According to news reporting originating from North Ryde, Australia, by NewsRx correspondents, research stated, “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.” Our news editors obtained a quote from the research from Macquarie University, “The training samples of simulated output power P-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-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.”
NETL
NSTL
IEEE