Modelling the fatigue damage in power components using machine learning technology

• We used machine learning to investigate a novel meta-modelling approach for failure site-level thermal fatigue damage predictions in power components. • Developed metamodels predicted the magnitude and spatial distribution of damage in the wire bonds of an IGBT power electronic module with the accuracy and fidelity of a finite element simulation. • The study demonstrated key metamodeling advantages of hiding intellectual property and removing requirements for complex characterisation physical tests and specialised software Thermo-mechanical finite element (FE)-based simulation technology has been used extensively for virtual prototyping and to predict material degradation and thermal fatigue damage in electronics assembly materials. However, from an end-user point of view, the deployment of such high-fidelity modelling is not straightforward as it requires comprehensive device and material characterisation data that is not readily available through technical datasheets and must be gathered using costly and time-consuming bespoke characterisation tests and access to metrology instruments. In addition to that, FE modelling requires access to advanced software and specialised FE skill sets. Here, a novel physics-informed Machine Learning (ML) approach for developing computationally fast metamodels for predicting fatigue damage and its spatial distribution at common failure sites of power electronics components is developed, validated and demonstrated. The significance of this work is in the attributes and the capabilities of the proposed modelling technology that enable the end-users of power components to perform insightful model-based assessments of the thermal fatigue damage in the assembly materials due to different application-specific, qualification and user-defined load conditions, removing current requirements for comprehensive device characterisations and deploying complex finite element models. The proposed methodology is demonstrated with two different metamodel structures, a regression decision tree and a neural network, for the problem of predicting the thermal fatigue damage in wire bonds of insulated-gate bipolar transistor (IGBT) power electronics modules (PEMs) exposed to passive temperature cycling loads. The results confirmed that the proposed approach and the modelling technology could offer FE model substitution and the capability to spatially map highly nonlinear three-dimensional spatial distributions of the damage parameter over local sub-domains associated with material fatigue degradation and failure.