@inproceedings{ZippeliusStroblSchmidetal.2022, author = {Zippelius, Andreas and Strobl, Tobias and Schmid, Maximilian and Hermann, Joseph and Hoffmann, Alwin and Elger, Gordon}, title = {Predicting thermal resistance of solder joints based on Scanning Acoustic Microscopy using Artificial Neural Networks}, booktitle = {2022 IEEE 9th Electronics System-Integration Technology Conference (ESTC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-8947-8}, doi = {https://doi.org/10.1109/ESTC55720.2022.9939465}, pages = {566 -- 575}, year = {2022}, language = {en} } @article{ZippeliusHanssSchmidetal.2022, author = {Zippelius, Andreas and Hanss, Alexander and Schmid, Maximilian and P{\´e}rez-Vel{\´a}zquez, Judith and Elger, Gordon}, title = {Reliability analysis and condition monitoring of SAC+ solder joints under high thermomechanical stress conditions using neuronal networks}, volume = {2022}, pages = {114461}, journal = {Microelectronics Reliability}, number = {129}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0026-2714}, doi = {https://doi.org/10.1016/j.microrel.2021.114461}, year = {2022}, abstract = {The thermo-mechanical fatigue of different SAC+ solders is investigated using transient thermal analysis (TTA) and predicted using artificial neural networks (ANN). TTA measures the thermal impedance and allows detection of solder cracks and delamination of material interfaces. LEDs soldered to printed circuit boards using seven different solders were aged within passive air-to-air temperature shock tests with TTA measurements every 50 cycles with the increase of the thermal resistance as failure criterium. A SnAgCuSb solder showed the best performance improvement over the SAC305 reference under the test conditions. In addition to standard evaluation by the cumulative failure-curve and Weibull plot, new approaches for reliability assessment are investigated to assess the reliability of the solder joint of the individual LEDs. A hybrid approach to predict failures in the solder joints of the individual LEDs during accelerated stress testing is set-up which processes the TTA data using artificial neural networks with memory, specifically LSTM, where the memory allows full use of the measurement history. Two ANN approaches, regression and classification, are used. Both approaches are shown to be quite accurate. The greater information gained from the regression approach requires more processing using external knowledge of the problem requirements, whereas the categorical approach can be more directly implemented. The results demonstrate the advantages of integrated approaches for assessment of the remaining useful life of solder joints.}, language = {en} } @article{SchmidZippeliusHanssetal.2023, author = {Schmid, Maximilian and Zippelius, Andreas and Hanß, Alexander and B{\"o}ckhorst, Stephan and Elger, Gordon}, title = {Investigations on High-Power LEDs and Solder Interconnects in Automotive Application: Part II - Reliability}, volume = {23}, journal = {IEEE Transactions on Device and Materials Reliability}, number = {3}, publisher = {IEEE}, address = {New York}, issn = {1558-2574}, doi = {https://doi.org/10.1109/TDMR.2023.3300355}, pages = {419 -- 429}, year = {2023}, language = {en} } @article{SchmidZippeliusHanssetal.2022, author = {Schmid, Maximilian and Zippelius, Andreas and Hanss, Alexander and B{\"o}ckhorst, Stephan and Elger, Gordon}, title = {Investigations on High-Power LEDs and Solder Interconnects in Automotive Application: Part I - Initial Characterization}, volume = {22}, journal = {IEEE Transactions on Device and Materials Reliability}, number = {2}, publisher = {IEEE}, address = {New York}, issn = {1530-4388}, doi = {https://doi.org/10.1109/TDMR.2022.3152590}, pages = {175 -- 186}, year = {2022}, abstract = {Thermo-mechanical reliability is one major issue in solid-state lighting. Mismatches in the coefficients of thermal expansion (CTE) between high-power LED packages and substrates paired with temperature changes induce mechanical stress. This leads to a thermal degradation of LED modules by crack formation in the solder interconnect and/or delamination in the substrate, which in turn increases junction temperature and thus decreases light output and reduces lifetime. To investigate degradation and understand influence of LED package design and solder material, a reliability study with a total of 1800 samples - segmented in nine LED types and five solder pastes - is performed. First of all, in this paper a state-of-the-art review of high-power LED packages is performed by analyzing and categorizing the packaging technologies. Second, the quality inspection after assembly is realized by transient thermal analysis (TTA), scanning acoustic microscopy (SAM) and X-ray. For TTA, a new method is introduced to separate the thermal resistance of the LED package from solder interconnect and substrate by applying the transient dual interface method (TDI) on samples with different solder interconnect void ratios. Further measurement effort is not required. The datasheet values for thermal resistance are verified and the different LED package types are benchmarked. The void ratio of the solder interconnects is determined by X-ray inspection combined with an algorithm to suppress disruptive internal LED package structures. TTA and TDI revealed that initial thermal performance is independent of solder paste type and that voiding is more critical to smaller LED packages. In addition, lower silver proportion in the paste is found to increase voiding. SAM is less sensitive for initial void detection than X-ray, but it's applied to monitor crack propagation while aging in combination with TTA. The results of the reliability study, i.e., the crack growth under temperature shock test for the different SAC solders, will be presented in a second independent paper.}, language = {en} } @article{MohdSchmidZippeliusetal.2024, author = {Mohd, Zubair Akhtar and Schmid, Maximilian and Zippelius, Andreas and Elger, Gordon}, title = {Solder joint lifetime model using AI framework operating on FEA data}, volume = {2025}, pages = {109032}, journal = {Engineering Failure Analysis}, number = {167, Part B}, publisher = {Elsevier}, address = {Oxford}, issn = {1350-6307}, doi = {https://doi.org/10.1016/j.engfailanal.2024.109032}, year = {2024}, abstract = {The thermo-mechanical reliability of electronic systems is often limited by the crack growth within the solder joints. Addressing this issue requires careful consideration of the design of the package and solder pads. Finite Element Analysis (FEA) is widely used to predict crack growth and to model their lifetime. Traditionally, FEA post-processing methods rely on human expertise to select appropriate regions for evaluating plastic and creep strain at critical locations and correlating these values with experimental data using the Coffin-Manson equation, which predicts fatigue lifetime based on cyclic plastic strain. This study introduces a novel method for FEA post-processing of surface-mounted devices (SMD) on printed circuit boards (PCB) using artificial intelligence. The method transforms the FEA data into a 2D grid map of creep strain values and employs a Convolutional Neural Network (CNN) for automatic feature extraction. Afterwards, a fully connected layer correlates the extracted features with the experimental measured solder joint lifetime, effectively capturing nonlinear relationships. The study focuses on the development of the concept of crack formation in the solder interconnects of ceramic based high-power LED packages used in the automotive industry for headlights. The validated FEA model is based on an extensive data set of 1800 LED packages including seven different ceramic-based LED packages and five different solders. The design of the ceramic LED package covers two-pad and three-pad footprint for soldering and thin film and thick film metallized ceramic carriers. Results show a strong agreement (R2 Score is 99.867 \%) between simulations and experimental data for ceramic LED packages. This automatic feature extraction from FEA data sets a new benchmark for improving solder reliability predictions, and it has proved to be better than established methods for lifetime prediction of solder joints.}, language = {en} } @inproceedings{MohdKreinerSchmidetal.2024, author = {Mohd, Zubair Akhtar and Kreiner, Christian and Schmid, Maximilian and Zippelius, Andreas and Tetzlaff, Ulrich and Elger, Gordon}, title = {Fully Connected Neural Network (FCNN) Based Validation Framework for FEA Post Processing to Improve SAC Solder Reliability Analysis}, booktitle = {2024 IEEE 10th Electronics System-Integration Technology Conference (ESTC), Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-9036-0}, doi = {https://doi.org/10.1109/ESTC60143.2024.10712023}, year = {2024}, language = {en} } @inproceedings{MohdSchmidZippeliusetal.2024, author = {Mohd, Zubair Akhtar and Schmid, Maximilian and Zippelius, Andreas and Elger, Gordon}, title = {LEDs Lifetime Prediction Modeling: Thermomechanical Simulation for SAC305 and SAC105}, booktitle = {2024 25th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-9363-7}, doi = {https://doi.org/10.1109/EuroSimE60745.2024.10491530}, year = {2024}, language = {en} } @article{ZippeliusMohdSchmidetal.2025, author = {Zippelius, Andreas and Mohd, Zubair Akhtar and Schmid, Maximilian and Elger, Gordon}, title = {Comparison of different Input data for the prediction of LED solder joints using Artificial Neural Networks}, journal = {IEEE Transactions on Device and Materials Reliability}, publisher = {IEEE}, address = {New York}, issn = {1558-2574}, doi = {https://doi.org/10.1109/TDMR.2025.3633876}, year = {2025}, abstract = {Scarcity of raw data is a major issue for applying data driven methods to reliability prediction, so making the best use of what is available is critical. This paper studies how different aspects of measurement data can be used best. Specifically, the reliability of the solder joint of LED packages is predicted based on Transient Thermal Analysis and Scanning Acoustic Microscopy data from a large measurement campaign. The impact of using full temporal information vs measurements at individual datapoints is investigated as well as the benefit of including the SAM data, and different ways of presenting the TTA information, either as a full curve or as expert-selected features. The impact of formatting categorical information of solder and LED package type as one-hot encoding or using embeddings is considered. Finally, the performance for Pass/Fail predictions of the best identified model with a model architecture developed on a similar dataset is compared. We identified the most relevant sources of information for predicting the behavior and the best format for the data, which helps guide the choice for future model architectures.}, language = {en} } @inproceedings{ZippeliusHufnagelShahetal.2025, author = {Zippelius, Andreas and Hufnagel, Elias and Shah, Jainam and Schmid, Maximilian and Elger, Gordon}, title = {Reliability of High-Power LEDs Under Varying Thermal Aging Conditions}, booktitle = {2025 31st International Workshop on Thermal Investigations of ICs and Systems (THERMINIC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3315-9486-2}, doi = {https://doi.org/10.1109/THERMINIC65879.2025.11216869}, year = {2025}, language = {en} } @inproceedings{KuehnPandeyZippeliusetal.2020, author = {K{\"u}hn, Stephan and Pandey, Amit and Zippelius, Andreas and Schneider, Klaus and Erdogan, H{\"u}seyin and Elger, Gordon}, title = {Analysis of package design of optic modules for automotive cameras to realize reliable image sharpness}, booktitle = {2020 IEEE 8th Electronics System-Integration Technology Conference (ESTC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-6293-5}, doi = {https://doi.org/10.1109/ESTC48849.2020.9229786}, year = {2020}, language = {en} }