@article{LiuContiBhogarajuetal.2020, author = {Liu, E and Conti, Fosca and Bhogaraju, Sri Krishna and Signorini, Raffaella and Pedron, Danilo and Wunderle, Bernhard and Elger, Gordon}, title = {Thermomechanical stress in GaN-LEDs soldered onto Cu substrates studied using finite element method and Raman spectroscopy}, volume = {51}, journal = {Journal of Raman Spectroscopy}, number = {10}, publisher = {Wiley}, address = {Chichester}, issn = {1097-4555}, doi = {https://doi.org/10.1002/jrs.5947}, pages = {2083 -- 2094}, year = {2020}, abstract = {Local thermomechanical stress can cause failures in semiconductor packages during long-time operation under harsh environmental conditions. This study helps to explain the packaging-induced stress in blue GaN-LEDs soldered onto copper substrates using AuSn alloy as lead-free interconnect material. Based on the finite element method, a virtual prototype is developed to simulate the thermomechanical behavior and stress in the LED and in the complete LED/AuSn/Cu assembly considering plastic and viscoplastic strain. The investigations were performed by varying the temperature between -50°C and 180°C. To validate the model, the simulation results are compared to experimental data collected with Raman spectroscopy. Studies of the urn:x-wiley:03770486:media:jrs5947:jrs5947-math-0003 phonon mode of GaN semiconductor are elaborated to understand the induced thermomechanical stress. The model enables evaluation of the stress in the interfaces of the assembly, which otherwise cannot be accessed by measurements. It serves to predict how assemblies would perform, before committing resources to build a physical prototype.}, language = {en} } @article{LiuBhogarajuWunderleetal.2022, author = {Liu, E and Bhogaraju, Sri Krishna and Wunderle, Bernhard and Elger, Gordon}, title = {Investigation of stress relaxation in SAC305 with micro-Raman spectroscopy}, volume = {2022}, pages = {114664}, journal = {Microelectronics Reliability}, number = {138}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0026-2714}, doi = {https://doi.org/10.1016/j.microrel.2022.114664}, year = {2022}, language = {en} } @article{KettelgerdesSarmientoErdoganetal.2024, author = {Kettelgerdes, Marcel and Sarmiento, Nicolas and Erdogan, H{\"u}seyin and Wunderle, Bernhard and Elger, Gordon}, title = {Precise Adverse Weather Characterization by Deep-Learning-Based Noise Processing in Automotive LiDAR Sensors}, volume = {16}, pages = {2407}, journal = {Remote Sensing}, number = {13}, publisher = {MDPI}, address = {Basel}, issn = {2072-4292}, doi = {https://doi.org/10.3390/rs16132407}, year = {2024}, abstract = {With current advances in automated driving, optical sensors like cameras and LiDARs are playing an increasingly important role in modern driver assistance systems. However, these sensors face challenges from adverse weather effects like fog and precipitation, which significantly degrade the sensor performance due to scattering effects in its optical path. Consequently, major efforts are being made to understand, model, and mitigate these effects. In this work, the reverse research question is investigated, demonstrating that these measurement effects can be exploited to predict occurring weather conditions by using state-of-the-art deep learning mechanisms. In order to do so, a variety of models have been developed and trained on a recorded multiseason dataset and benchmarked with respect to performance, model size, and required computational resources, showing that especially modern vision transformers achieve remarkable results in distinguishing up to 15 precipitation classes with an accuracy of 84.41\% and predicting the corresponding precipitation rate with a mean absolute error of less than 0.47 mm/h, solely based on measurement noise. Therefore, this research may contribute to a cost-effective solution for characterizing precipitation with a commercial Flash LiDAR sensor, which can be implemented as a lightweight vehicle software feature to issue advanced driver warnings, adapt driving dynamics, or serve as a data quality measure for adaptive data preprocessing and fusion.}, 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} }