Deep Learning Algorithm for Advanced Level-3 Inverse-Modeling of
  Silicon-Carbide Power MOSFET Devices
Massimo Orazio Spata Sebastiano Battiato Alessandro Ortis Francesco Rundo Michele Calabretta Carmelo Pino Angelo Messina
Inverse modelling with deep learning algorithms involves training deep
architecture to predict device's parameters from its static behaviour. Inverse
device modelling is suitable to reconstruct drifted physical parameters of
devices temporally degraded or to retrieve physical configuration. There are
many variables that can influence the performance of an inverse modelling
method. In this work the authors propose a deep learning method trained for
retrieving physical parameters of Level-3 model of Power Silicon-Carbide MOSFET
(SiC Power MOS). The SiC devices are used in applications where classical
silicon devices failed due to high-temperature or high switching capability.
The key application of SiC power devices is in the automotive field (i.e. in
the field of electrical vehicles). Due to physiological degradation or
high-stressing environment, SiC Power MOS shows a significant drift of physical
parameters which can be monitored by using inverse modelling. The aim of this
work is to provide a possible deep learning-based solution for retrieving
physical parameters of the SiC Power MOSFET. Preliminary results based on the
retrieving of channel length of the device are reported. Channel length of
power MOSFET is a key parameter involved in the static and dynamic behaviour of
the device. The experimental results reported in this work confirmed the
effectiveness of a multi-layer perceptron designed to retrieve this parameter.