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

doi:10.1117/12.3016130

16 January 2024 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

Author Affiliations +

Proceedings Volume 12973, Workshop on Electronics Communication Engineering (WECE 2023); 1297309 (2024) https://doi.org/10.1117/12.3016130
Event: Workshop on Electronics Communication Engineering (WECE 2023), 2023, Guilin, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Massimo Orazio Spata, Sebastiano Battiato, Alessandro Ortis, Francesco Rundo, Michele Calabretta, Carmelo Pino, and Angelo Messina "Deep learning algorithm for advanced level-3 inverse-modeling of silicon-carbide power MOSFET devices", Proc. SPIE 12973, Workshop on Electronics Communication Engineering (WECE 2023), 1297309 (16 January 2024); https://doi.org/10.1117/12.3016130

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