Module Parasitics-Based Current and Temperature Sensing Using Explainable Neural Networks

Title data

Lautner, Frank ; Bakran, Mark-M.:
Module Parasitics-Based Current and Temperature Sensing Using Explainable Neural Networks.
In: Sensors. Vol. 26 (2026) . - 2235.
ISSN 1424-8220
DOI: https://doi.org/10.3390/s26072235

Project information

Abstract in another language

This paper examines the application of simple neural networks for current measurement and the determination of the junction temperature in power semiconductor modules. On the one hand, the focus was not on the use of conventional sensors such as current sensors or temperature sensors, but rather on utilising parasitic components within the power semiconductor module, from which useful signals can be extracted. Namely, these are the voltage across parasitic inductances in a module, the semiconductor’s on-state voltage, and its turn-on delay time. Because these signals are often affected by other parameters, the desired information must be extracted, which was found to be an application case for artificial neural networks. On the other hand, the application of ANNs in the simplest and most effective way possible was presented. Furthermore, a method is introduced that takes a first step towards the interpretability of neural networks in a straightforward manner to overcome the main drawback for the user—the usual black-box structure of neural networks.