Enabling high reliability power modules: A multidisciplinary task

Gonzalez, Jose Ortiz; Li, Ran; Soli, A. Mohamed Motalab Ali; Davletzhanova, Zarina; Alatise, Olayiwola; Mawby, P.A.; Hu, Borong; Zeng, Zheng; Ren, Hai; Li, Hui; Xu, Shengyou

doi:10.1109/3dpeim.2016.7570567

Cited by 4 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Assuring and assessing the reliability performance of power electronic modules (PEMs) deployed in different applications remains a key challenge for the industry [1][2][3]. Power components based on current packaging architectures are most susceptible to thermally induced fatigue damage of their wire bonds and the die attachment layer.…”

Section: Introductionmentioning

confidence: 99%

Physics-informed Machine Learning for Predicting Fatigue Damage of Wire Bonds in Power Electronic Modules

Stoyanov,

Tilford,

Zhang

et al. 2024

2024 25th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and

View full text Add to dashboard Cite

This paper details a novel physics-informed datadriven approach for developing computationally fast metamodels for predicting fatigue damage and its spatial distribution at common failure sites of power electronic components. The proposed metamodels aim to serve the end-users of these power components by allowing an informative model-based assessment 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 (FE) models. The proposed methodology is demonstrated with two different metamodel structures, a multi-quadratic function, and a neural network, for the problem of predicting the thermal fatigue damage due to temperature cycling loads in the wire bonds of an IGBT power electronic module (PEM). The results confirmed that the proposed approach and the modelling technology can offer FE-matching accuracy and capability to map highly nonlinear spatial distributions of the damage parameter over local sub-domains associated with material fatigue degradation and failure due to material/interfacial cracking.

show abstract

Section: Introductionmentioning

confidence: 99%

Physics-informed Machine Learning for Predicting Fatigue Damage of Wire Bonds in Power Electronic Modules

Stoyanov,

Tilford,

Zhang

et al. 2024

2024 25th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and

View full text Add to dashboard Cite

show abstract

“…One of the main driving forces of the power electronics technology along with cost, volume, weight, and functionality is reliability. The reliability of power electronic packages is a serious concern and a major challenge for the end-users of power electronic components and modules because high-reliability requirements mark almost any application of power electronics [2,3]. Despite the significant body of reliability-focused research and studies in the public domain, on technology, design, test and simulation, the informed deployment of packaged power electronic devices in different applications remains a challenging task.…”

Section: Introductionmentioning

confidence: 99%

Reliability Meta-modelling of Power Components

Stoyanov,

Tilford,

Shen

et al. 2024

2024 47th International Spring Seminar on Electronics Technology (ISSE)

View full text Add to dashboard Cite

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 userfriendly 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 FElike 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.

show abstract

Review of SiC MOSFET based three-phase inverter lifetime prediction

Lyu

Yadav

et al. 2017

2017 IEEE Applied Power Electronics Conference and Exposition (APEC)

View full text Add to dashboard Cite

Enabling high reliability power modules: A multidisciplinary task

Cited by 4 publications

References 7 publications

Physics-informed Machine Learning for Predicting Fatigue Damage of Wire Bonds in Power Electronic Modules

Physics-informed Machine Learning for Predicting Fatigue Damage of Wire Bonds in Power Electronic Modules

Reliability Meta-modelling of Power Components

Review of SiC MOSFET based three-phase inverter lifetime prediction

Contact Info

Product

Resources

About