Reliability Meta-modelling of Power Components

Finite element (FE) modelling integrated with lifetime prediction models is an attractive and powerful approach for predicting and improving the thermal fatigue reliability of power electronic components and modules subjected to temperature cycling loads. The challenge with the FE-based modelling approach is the model development effort, device characterisation data requirements and the computational cost of the high-fidelity simulation. This paper presents a modelling methodology for developing fast and user-friendly damage prediction models for power components that benefit from the combined deployment of meta-modelling and machine learning (ML), using physics-informed damage data. The main attribute of the meta-modelling framework is the FE-like mapping of the spatial distribution of the thermal fatigue damage parameter in the local domain of the failure site. The thermal fatigue predictions for the planar solder interconnection layer in a conventional wire-bonded, Si-based Insulated-Gate Bipolar Transistor (IGBT) power electronic module (PEM) are demonstrated using the proposed methodology under parameterised thermal cycling load. The results show that the metamodel with location-dependent model parameters can retain the accuracy of damage predictions obtained with the full-order FE simulations and can accurately inform on the damage spatial distribution in the solder layer. The metamodel is found to have superior performance compared to a unified Neural Network model with the same spatial damage prediction attribute.