IEEE Electron Device Letters · 2022 · 35 citations · 15 references
EngineeringComputer ScienceElectrical EngineeringElectronic MaterialsMicroelectronicsSemiconductor DevicesCurrent-voltage CharacteristicsDeep LearningDevice CharacterizationDevice ModelingSemiconductor Device MaterialsWide-bandgap Semiconductor DevicesCompact Model Parameter ExtractionGallium Nitride (Gan) MaterialsGallium Nitride Power DevicesJunction ModelingNetwork Parameter ExtractionParameter ExtractionSemiconductor Device Modeling
A fast and accurate deep learning (DL) based ASM-HEMT I-V model parameter extraction is presented for the first time. DL-based extraction starts with 120k training data-sets comprising of 374 million I-V data points. Training data-sets are generated through Monte Carlo simulations. The trained DL-model is demonstrated to successfully model 114 GaN HEMTs from a typical GaN fabrication process. The predicted parameters show an excellent fit for the I-V data. In addition, the root-mean-square(RMS) error incurred for key electrical parameters such as pinch-off voltage, linear condition current and the maximum current is 2.2%, 17.6%, and 2.4% respectively. The proposed approach is verified for multiple GaN HEMTs of different sizes. The developed technique can provide a very fast means for parameter extraction with a reasonable accuracy.
15