@article{KleinerLechermannKomsiyskaetal.2021, author = {Kleiner, Jan and Lechermann, Lorenz and Komsiyska, Lidiya and Elger, Gordon and Endisch, Christian}, title = {Thermal behavior of intelligent automotive lithium-ion batteries}, volume = {2021}, pages = {102686}, journal = {Journal of energy storage}, subtitle = {operating strategies for adaptive thermal balancing by reconfiguration}, number = {40}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2352-1538}, doi = {https://doi.org/10.1016/j.est.2021.102686}, year = {2021}, 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{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}, volume = {15}, pages = {24340}, journal = {Scientific Reports}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {https://doi.org/10.1038/s41598-025-06902-0}, year = {2025}, abstract = {Crack propagation in solder joints remains a major challenge impacting the thermo-mechanical reliability of electronic devices, underscoring the importance of optimizing package and solder pad designs. Traditional Finite Element Analysis (FEA) techniques for predicting solder joint lifespan often rely on manual post-processing to identify high-risk regions for plastic strain accumulation. However, this manual process can fail to detect complex and subtle failure mechanisms and purely based on averaging the creep strain and correlating it to lifetime values collected from experiments using Coffin Manson equation. To address these limitations, this study presents an Artificial Intelligence (AI) framework designed for automated 3D FEA post-processing of surface-mounted devices (SMDs) assembled to Printed Circuit Board (PCB). This framework integrates 3D Convolutional Neural Networks (CNNs) and PointNet architectures to automatically extract complex spatial features from 3D FEA data. These learned features are then linked to experimentally measured solder joint lifetimes through fully connected neural network layers, allowing the model to capture complex and nonlinear failure behaviours. The research specifically targets crack development in solder joints of ceramic-based high-power LED packages used in automotive lighting systems. This dataset included variations in two-pad and three-pad configurations, as well as thin and thick film metallized ceramic substrates. Results from the study demonstrate that the PointNet model outperforms the 3D CNN, achieving a high correlation with experimental data (R2 = 99.91\%). This AI-driven, automated feature extraction approach significantly improves the accuracy and provide the more reliable models for solder joint lifetime predictions, offering a substantial improvement over traditional method.}, 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} } @inproceedings{LiuMohdSteinbergeretal.2024, author = {Liu, E and Mohd, Zubair Akhtar and Steinberger, Fabian and Wunderle, Bernhard and Elger, Gordon}, title = {Using µ-RAMAN Spectroscopy to Inspect Sintered Interconnects}, booktitle = {2024 IEEE 10th Electronics System-Integration Technology Conference (ESTC), Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-9036-0}, doi = {https://doi.org10.1109/ESTC60143.2024.10712149}, 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} } @article{MeessGernerHeinetal.2024, author = {Meess, Henri and Gerner, Jeremias and Hein, Daniel and Schmidtner, Stefanie and Elger, Gordon and Bogenberger, Klaus}, title = {First steps towards real-world traffic signal control optimisation by reinforcement learning}, volume = {18}, journal = {Journal of Simulation}, number = {6}, publisher = {Taylor \& Francis}, address = {London}, issn = {1747-7778}, doi = {https://doi.org/10.1080/17477778.2024.2364715}, pages = {957 -- 972}, year = {2024}, abstract = {Enhancing traffic signal optimisation has the potential to improve urban traffic flow without the need for expensive infrastructure modifications. While reinforcement learning (RL) techniques have demonstrated their effectiveness in simulations, their real-world implementation is still a challenge. Real-world systems need to be developed that guarantee a deployable action definition for real traffic systems while prioritising safety constraints and robust policies. This paper introduces a method to overcome this challenge by introducing a novel action definition that optimises parameter-level control programmes designed by traffic engineers. The complete proposed framework consists of a traffic situation estimation, a feature extractor, and a system that enables training on estimates of real-world traffic situations. Further multimodal optimisation, scalability, and continuous training after deployment could be achieved. The first simulative tests using this action definition show an average improvement of more than 20\% in traffic flow compared to the baseline - the corresponding pre-optimised real-world control.}, language = {en} } @unpublished{KettelgerdesHillmannHirmeretal.2023, author = {Kettelgerdes, Marcel and Hillmann, Tjorven and Hirmer, Thomas and Erdogan, H{\"u}seyin and Wunderle, Bernhard and Elger, Gordon}, title = {Accelerated Real-Life (ARL) Testing and Characterization of Automotive LiDAR Sensors to facilitate the Development and Validation of Enhanced Sensor Models}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2312.04229}, year = {2023}, abstract = {In the realm of automated driving simulation and sensor modeling, the need for highly accurate sensor models is paramount for ensuring the reliability and safety of advanced driving assistance systems (ADAS). Hence, numerous works focus on the development of high-fidelity models of ADAS sensors, such as camera, Radar as well as modern LiDAR systems to simulate the sensor behavior in different driving scenarios, even under varying environmental conditions, considering for example adverse weather effects. However, aging effects of sensors, leading to suboptimal system performance, are mostly overlooked by current simulation techniques. This paper introduces a cutting-edge Hardware-in-the-Loop (HiL) test bench designed for the automated, accelerated aging and characterization of Automotive LiDAR sensors. The primary objective of this research is to address the aging effects of LiDAR sensors over the product life cycle, specifically focusing on aspects such as laser beam profile deterioration, output power reduction and intrinsic parameter drift, which are mostly neglected in current sensor models. By that, this proceeding research is intended to path the way, not only towards identifying and modeling respective degradation effects, but also to suggest quantitative model validation metrics.}, language = {en} } @inproceedings{SchmidMombergKettelgerdesetal.2023, author = {Schmid, Maximilian and Momberg, Marcel and Kettelgerdes, Marcel and Elger, Gordon}, title = {Transient thermal analysis for VCSEL Diodes}, booktitle = {2023 29th International Workshop on Thermal Investigations of ICs and Systems (THERMINIC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-1862-3}, doi = {https://doi.org/10.1109/THERMINIC60375.2023.10325906}, year = {2023}, 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} } @article{SchwanMohanSchmidetal.2025, author = {Schwan, Hannes and Mohan, Nihesh and Schmid, Maximilian and Saha, Rocky Kumar and Klassen, Holger and M{\"u}ller, Klaus and Elger, Gordon}, title = {Sintering for High Power Optoelectronic Devices}, volume = {16}, pages = {1164}, journal = {Micromachines}, number = {10}, publisher = {MDPI}, address = {Basel}, issn = {2072-666X}, doi = {https://doi.org/10.3390/mi16101164}, year = {2025}, abstract = {Residual-free eutectic Au80Sn20 soldering is still the dominant assembly technology for optoelectronic devices such as high-power lasers, LEDs, and photodiodes. Due to the high cost of gold, alternatives are desirable. This paper investigates the thermal performance of copper-based sintering for optoelectronic submodules on first and second level to obtain thermally efficient thin bondlines. Sintered interconnects obtained by a new particle-free copper ink, based on complexed copper salt, are compared with copper flake and silver nanoparticle sintered interconnects and benchmarked against AuSn solder interconnects. The copper ink is dispensed and predried at 130 °C to facilitate in situ generation of Cu nanoparticles by thermal decomposition of the metal salt before sintering. Submounts are then sintered at 275 °C for 15 min under nitrogen with 30 MPa pressure, forming uniform 2-5 µm copper layers achieving shear strengths above 31 MPa. Unpackaged LEDs are bonded on first level using the copper ink but applying only 10 MPa to avoid damaging the semiconductor dies. Thermal performance is evaluated via transient thermal analysis. Results show that copper ink interfaces approach the performance of thin AuSn joints and match silver interconnects at second level. However, at first level, AuSn and sintered interconnects of commercial silver and copper pastes remained superior due to the relative inhomogeneous thickness of the thin Cu copper layer after predrying, requiring higher bonding pressure to equalize surface inhomogeneities.}, language = {en} } @inproceedings{SchwanSchmidElger2024, author = {Schwan, Hannes and Schmid, Maximilian and Elger, Gordon}, title = {Layer Resolved Thermal Impedance Measurement with Laser Stimulated Transient Thermal Analysis of Semiconductor Modules}, booktitle = {THERMINIC 2024: Proceedings of 2024, 30th International Workshop on Thermal Investigations of ICs and Systems}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-8782-7}, doi = {https://doi.org/10.1109/THERMINIC62015.2024.10732233}, year = {2024}, language = {en} } @inproceedings{SchwanPforrElger2022, author = {Schwan, Hannes and Pforr, Johannes and Elger, Gordon}, title = {ZVS Class E2 Wireless Power Transfer System with Self-Resonant Transmission Coils for the Biomedical Application}, pages = {107}, booktitle = {PCIM Europe 2022: International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management}, editor = {Mesago Messe Frankfurt GmbH,}, publisher = {VDE Verlag}, address = {Berlin}, isbn = {978-3-8007-5822-7}, doi = {https://doi.org/10.30420/565822107}, year = {2022}, language = {en} } @inproceedings{SchwanSchmidElger2022, author = {Schwan, Hannes and Schmid, Maximilian and Elger, Gordon}, title = {Laser Stimulated Transient Thermal Analysis of Semiconductors}, booktitle = {THERMINIC 2022: Proceedings 2022}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-9229-4}, doi = {https://doi.org/10.1109/THERMINIC57263.2022.9950672}, year = {2022}, language = {en} } @article{PandeyMohdVeettiletal.2026, author = {Pandey, Amit and Mohd, Zubair Akhtar and Veettil, Nandana Kappuva and Wunderle, Bernhard and Elger, Gordon}, title = {Quantitative Kernel estimation from traffic signs using slanted edge spatial frequency response as a sharpness metric}, volume = {16}, pages = {7387}, journal = {Scientific Reports}, number = {1}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {https://doi.org/10.1038/s41598-026-40556-w}, year = {2026}, abstract = {Sharpness is a critical optical property of automotive cameras, measured by the spatial frequency response (SFR) within the end-of-line (EOL) test after manufacturing. This work presents a method to estimate the blurring kernel of an automotive camera, which could be the first step toward state monitoring of automotive cameras. To achieve this, Principal Component Analysis (PCA) was performed, using synthetic kernels generated by Zemax. The PCA model was built with approximately 1300 base kernels representing spatially variant point spread functions (PSFs). This model generates kernel samples during the estimation process. Synthetic images were created by convolving the synthetic kernels with reference traffic sign images and compared with real-life data captured by an automotive camera. These synthetic data were utilized for algorithm development, and later on, validation was performed on real-life data. The algorithm extracts two pixels regions of interest (ROIs) containing slanted edges from the blurred image and crops matching ROIs from a reference sharp image. Each candidate kernel was used to blur the reference ROIs, and the resulting SFR was compared with the blurred ROIs' SFR. Differential evolution optimization minimizes the SFR difference, selecting the kernel that best matches the observed blur. The final kernel was evaluated against the true kernel for accuracy. The structural similarity index measure (SSIM) between the original and estimated blurred ROIs ranges from 0.808 to 0.945. For true vs. estimated kernels, SSIM varies from 0.92 to 0.98. Pearson correlation coefficients range from 0.84 to 0.99, Cosine similarity from 0.86 to 0.99, and mean squared error (MSE) from to . Validation on real-life camera images showed that the SSIM between the estimated and blurred ROI was >0.82, showing promising accuracy in kernel estimation, which could be used towards in-field monitoring of camera sharpness degradation.}, language = {en} } @article{PandeyKuehnWeisetal.2025, author = {Pandey, Amit and K{\"u}hn, Stephan and Weis, Alexander and Wunderle, Bernhard and Elger, Gordon}, title = {Evaluating optical performance degradation of automotive cameras under accelerated aging}, volume = {2026}, pages = {109396}, journal = {Optics and Lasers in Engineering}, number = {196}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1873-0302}, doi = {https://doi.org/10.1016/j.optlaseng.2025.109396}, year = {2025}, abstract = {Automotive cameras are subject to environmental stress, which degrades performance by reducing image sharpness. To qualify for automotive use and to ensure that the cameras maintain sharpness according to the hard requirements of end-of-line testing, cameras have to undergo standardized accelerated aging tests. These tests are performed to demonstrate reliability and functional safety over lifetime. Few studies have been published that demonstrate how aging contributes to the degradation of optical performance. This study addresses this gap by combining accelerated thermal aging with sharpness tracking to investigate degradation over time. To quantify sharpness degradation, six series-production cameras were subjected to accelerated thermal aging between -40◦𝐶 and +85◦𝐶. Each camera underwent 2000 aging cycles, equivalent to 80\% of their lifetime based on the Coffin-Manson model of the LV124 standard. Sharpness was measured by calculating the Spatial Frequency Response (SFR) from images captured of a double-cross reticle projected by a virtual object generator with three illumination wavelengths (625nm, 520nm, and 470nm). The change in sharpness was evaluated with SFR50 and SFR at 60 line pairs per millimeter (SFR@60). During the first 250 cycles, a wear-in effect was observed, where sharpness increased before leveling off, as seen previously. The results also indicated a slow decline in sharpness showing long-term stability. Analysis indicated that before aging, the best focal plane was located closer to the focal position of the red wavelength, which lies furthest from the objective. By the end of the aging process, the best focal plane had shifted toward the focal position of the blue wavelength, which is located closer to the objective. This suggests a forward movement of the image sensor due to aging. Even after 2000 cycles, all cameras maintained an SFR@60 above 0.5. A Random Forest regression model was trained to predict the age based on the SFR curves, achieving a mean absolute error of 126 cycles and a 𝑅2 score of 0.96.}, language = {en} } @unpublished{PandeyMohdVeettiletal.2025, author = {Pandey, Amit and Mohd, Zubair Akhtar and Veettil, Nandana Kappuva and Wunderle, Bernhard and Elger, Gordon}, title = {Quantitative Kernel Estimation from Traffic Signs using Slanted Edge Spatial Frequency Response as a Sharpness Metric}, titleParent = {Research Square}, publisher = {Research Square}, address = {Durham}, doi = {https://doi.org/10.21203/rs.3.rs-6725582/v1}, year = {2025}, abstract = {The sharpness is a critical optical property of automotive cameras, measured by the Spatial Frequency Response (SFR) within the end of line (EOL) test after manufacturing. This work presents a method to estimate the blurring kernel of automotive camera for state monitoring. To achieve this, Principal Component Analysis (PCA) is performed, using synthetic kernels generated by Zemax. The PCA model is built with approximately 1300 base kernels representing spatially variant point spread functions (PSFs). This model generates kernel samples during the estimation process. Synthetic images are created by convolving the synthetic kernels with reference traffic sign images and compared with real-life data captured by an automotive camera. These synthetic data are utilized for algorithm development, and later on validation is performed on real-life data. The algorithm extracts two 45 x 45 pixels regions of interest (ROIs) containing slanted edges from the blurred image and crops matching ROIs from a reference sharp image. Each candidate kernel blurs the reference ROIs, and the resulting Spatial Frequency Response (SFR) is compared with the blurred ROIs' SFR. Differential evolution optimization minimizes the SFR difference, selecting the kernel that best matches the observed blur. The final kernel is evaluated against the true kernel for accuracy. Structural similarity index measure (SSIM) between the original and estimated blurred ROIs ranges from 0.808 to 0.945. For true vs. estimated kernels, SSIM varies from 0.92 to 0.98. Pearson correlation coefficients range from 0.84 to 0.99, Cosine similarity from 0.86 to 0.98, and mean squared error (MSE) from 1.1 x 10-5 to 8.3 x 10-5. Validation on real-life camera images shows that the SSIM between estimated ROI is 0.82 indicating a sufficient level of accuracy in kernel estimation to detect potential degradation of the camera.}, language = {en} } @inproceedings{KettelgerdesMezmerHaeussleretal.2023, author = {Kettelgerdes, Marcel and Mezmer, Peter and Haeussler, Michael J. and B{\"o}ttger, Gunnar and Tavakolibasti, Majid and Pandey, Amit and Erdogan, H{\"u}seyin and Elger, Gordon and Schacht, Ralph and Wunderle, Bernhard}, title = {Realization, multi-field coupled simulation and characterization of a thermo-mechanically robust LiDAR front end on a copper coated glass substrate}, booktitle = {Proceedings: IEEE 73rd Electronic Components and Technology Conference, ECTC 2023}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-3498-2}, issn = {2377-5726}, doi = {https://doi.org/10.1109/ECTC51909.2023.00131}, pages = {753 -- 760}, year = {2023}, language = {en} } @inproceedings{KettelgerdesPandeyUnruhetal.2024, author = {Kettelgerdes, Marcel and Pandey, Amit and Unruh, Denis and Erdogan, H{\"u}seyin and Wunderle, Bernhard and Elger, Gordon}, title = {Automotive LiDAR Based Precipitation State Estimation Using Physics Informed Spatio-Temporal 3D Convolutional Neural Networks (PIST-CNN)}, booktitle = {2023 29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-2562-1}, doi = {https://doi.org/10.1109/M2VIP58386.2023.10413394}, year = {2024}, language = {en} } @inproceedings{PandeyUnruhKettelgerdesetal.2023, author = {Pandey, Amit and Unruh, Denis and Kettelgerdes, Marcel and Wunderle, Bernhard and Elger, Gordon}, title = {Evaluation of thermally-induced change in sharpness of automotive cameras by coupled thermo-mechanical and optical simulation}, pages = {123270U}, booktitle = {SPIE Future Sensing Technologies 2023}, editor = {Matoba, Osamu and Shaw, Joseph A. and Valenta, Christopher R.}, publisher = {SPIE}, address = {Bellingham}, isbn = {978-1-5106-5723-6}, doi = {https://doi.org/10.1117/12.2665475}, year = {2023}, language = {en} } @inproceedings{PandeyKuehnErdoganetal.2020, author = {Pandey, Amit and K{\"u}hn, Stephan and Erdogan, H{\"u}seyin and Schneider, Klaus and Elger, Gordon}, title = {Finite Element Analysis: A Tool for Investigation of Sharpness Changes in Automotive Cameras}, booktitle = {2020 21st International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-6049-8}, doi = {https://doi.org/10.1109/EuroSimE48426.2020.9152716}, year = {2020}, language = {en} } @inproceedings{PandeyBhogarajuLuxetal.2022, author = {Pandey, Amit and Bhogaraju, Sri Krishna and Lux, Kerstin and Elger, Gordon}, title = {Analysis of stress in spherically bent thinned image sensors}, booktitle = {2022 23rd International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {978-1-6654-5836-8}, doi = {https://doi.org/10.1109/EuroSimE54907.2022.9758847}, year = {2022}, 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} }