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Inverse modelling with deep learning algorithms involves training deeparchitecture to predict device's parameters from its static behaviour. Inversedevice modelling is suitable to reconstruct drifted physical parameters ofdevices temporally degraded or to retrieve physical configuration. There aremany variables that can influence the performance of an inverse modellingmethod. In this work the authors propose a deep learning method trained forretrieving physical parameters of Level-3 model of Power Silicon-Carbide MOSFET(SiC Power MOS). The SiC devices are used in applications where classicalsilicon devices failed due to high-temperature or high switching capability.The key application of SiC power devices is in the automotive field (i.e. inthe field of electrical vehicles). Due to physiological degradation orhigh-stressing environment, SiC Power MOS shows a significant drift of physicalparameters which can be monitored by using inverse modelling. The aim of thiswork is to provide a possible deep learning-based solution for retrievingphysical parameters of the SiC Power MOSFET. Preliminary results based on theretrieving of channel length of the device are reported. Channel length ofpower MOSFET is a key parameter involved in the static and dynamic behaviour ofthe device. The experimental results reported in this work confirmed theeffectiveness of a multi-layer perceptron designed to retrieve this parameter.

Deep Learning Algorithm for Advanced Level-3 Inverse-Modeling of Silicon-Carbide Power MOSFET Devices