@inproceedings{BhogarajuContiSchmidetal.2020, author = {Bhogaraju, Sri Krishna and Conti, Fosca and Schmid, Maximilian and Meier, Markus and Schweigart, Helmut and Elger, Gordon}, title = {Development of sinter paste with surface modified copper alloy particles for die-attach bonding}, booktitle = {ETG-Fachbericht 161, CIPS 2020, 11th International Conference on Integrated Power Electronics Systems}, publisher = {VDE Verlag}, address = {Berlin}, isbn = {978-3-8007-5226-3}, url = {https://www.vde-verlag.de/proceedings-de/455225100.html}, pages = {582 -- 587}, year = {2020}, language = {en} } @inproceedings{HanssSchmidElger2019, author = {Hanss, Alexander and Schmid, Maximilian and Elger, Gordon}, title = {Combined Accelerated Stress Test with In-Situ Thermal Impedance Monitoring to Access LED Reliability}, booktitle = {Proceedings 2018 20th International Conference on Electronic Materials and Packaging (EMAP)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5386-5642-6}, doi = {https://doi.org/10.1109/EMAP.2018.8660833}, year = {2019}, language = {en} } @unpublished{MohdSchmidElger2025, author = {Mohd, Zubair Akhtar and Schmid, Maximilian and Elger, Gordon}, title = {AI-Driven Point Cloud Framework for Predicting Solder Joint Reliability using 3D FEA Data}, titleParent = {Research Square}, publisher = {Research Square}, address = {Durham}, doi = {https://doi.org/10.21203/rs.3.rs-6173485/v1}, year = {2025}, abstract = {Crack propagation in solder joints remains a critical challenge affecting the thermo-mechanical reliability of electronic devices, emphasizing the need for optimized package and solder pad designs. Traditional Finite Element Analysis (FEA) methods for predicting solder joint lifespan rely heavily on manual post-processing, where high-risk regions for plastic strain accumulation are identified. However, these approaches often overlook intricate failure mechanisms, as they primarily average creep strain and correlate it with experimental lifetime data using the Coffin-Manson equation, limiting their predictive accuracy. To overcome these limitations, this study introduces a novel AI-driven framework that automates 3D FEA post-processing for surface-mounted devices (SMDs) connected to printed circuit boards (PCBs). Unlike traditional methods, this framework leverages deep learning architectures—specifically, 3D Convolutional Neural Networks (CNNs) and PointNet—to extract complex spatial features directly from 3D FEA data, eliminating the need for manual interpretation. These learned features are then mapped to experimentally measured solder joint lifetimes through fully connected neural network layers, allowing the model to capture nonlinear failure behaviours that conventional methods fail to recognize. The research focuses on crack propagation in ceramic-based high-power LED packages used in automotive lighting systems, incorporating variations in two-pad and three-pad configurations, as well as thin and thick film metallized ceramic substrates with validated FEA models. Comparative analysis shows that PointNet significantly outperforms 3D CNNs, achieving an exceptionally high correlation with experimental data (R² = 99.99\%). This AI-driven automated feature extraction and lifetime prediction approach marks a major advancement over traditional FEA-based methods, offering superior accuracy, reliability, and scalability for predicting solder joint reliability in microelectronics.}, language = {en} }