TY - JOUR A1 - Mohd, Zubair Akhtar A1 - Schmid, Maximilian A1 - Elger, Gordon T1 - AI-driven point cloud framework for predicting solder joint reliability using 3D FEA data JF - Scientific Reports N2 - 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. UR - https://doi.org/10.1038/s41598-025-06902-0 Y1 - 2025 UR - https://doi.org/10.1038/s41598-025-06902-0 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-60961 SN - 2045-2322 VL - 15 PB - Springer Nature CY - London ER - TY - INPR A1 - Mohd, Zubair Akhtar A1 - Schmid, Maximilian A1 - Elger, Gordon T1 - AI-Driven Point Cloud Framework for Predicting Solder Joint Reliability using 3D FEA Data T2 - Research Square N2 - 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. UR - https://doi.org/10.21203/rs.3.rs-6173485/v1 Y1 - 2025 UR - https://doi.org/10.21203/rs.3.rs-6173485/v1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58308 SN - 2693-5015 PB - Research Square CY - Durham ER - TY - CHAP A1 - Liu, E A1 - Mohd, Zubair Akhtar A1 - Steinberger, Fabian A1 - Wunderle, Bernhard A1 - Elger, Gordon T1 - Using µ-RAMAN Spectroscopy to Inspect Sintered Interconnects T2 - 2024 IEEE 10th Electronics System-Integration Technology Conference (ESTC), Proceedings UR - https://doi.org10.1109/ESTC60143.2024.10712149 Y1 - 2024 UR - https://doi.org10.1109/ESTC60143.2024.10712149 SN - 979-8-3503-9036-0 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Zippelius, Andreas A1 - Mohd, Zubair Akhtar A1 - Schmid, Maximilian A1 - Elger, Gordon T1 - Comparison of different Input data for the prediction of LED solder joints using Artificial Neural Networks JF - IEEE Transactions on Device and Materials Reliability N2 - 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. UR - https://doi.org/10.1109/TDMR.2025.3633876 Y1 - 2025 UR - https://doi.org/10.1109/TDMR.2025.3633876 SN - 1558-2574 PB - IEEE CY - New York ER - TY - JOUR A1 - Meess, Henri A1 - Gerner, Jeremias A1 - Hein, Daniel A1 - Schmidtner, Stefanie A1 - Elger, Gordon A1 - Bogenberger, Klaus T1 - First steps towards real-world traffic signal control optimisation by reinforcement learning JF - Journal of Simulation N2 - 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. UR - https://doi.org/10.1080/17477778.2024.2364715 Y1 - 2024 UR - https://doi.org/10.1080/17477778.2024.2364715 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-48585 SN - 1747-7778 VL - 18 IS - 6 SP - 957 EP - 972 PB - Taylor & Francis CY - London ER - TY - INPR A1 - Kettelgerdes, Marcel A1 - Hillmann, Tjorven A1 - Hirmer, Thomas A1 - Erdogan, Hüseyin A1 - Wunderle, Bernhard A1 - Elger, Gordon T1 - Accelerated Real-Life (ARL) Testing and Characterization of Automotive LiDAR Sensors to facilitate the Development and Validation of Enhanced Sensor Models N2 - 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. UR - https://doi.org/10.48550/arXiv.2312.04229 Y1 - 2023 UR - https://doi.org/10.48550/arXiv.2312.04229 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59856 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Schmid, Maximilian A1 - Momberg, Marcel A1 - Kettelgerdes, Marcel A1 - Elger, Gordon T1 - Transient thermal analysis for VCSEL Diodes T2 - 2023 29th International Workshop on Thermal Investigations of ICs and Systems (THERMINIC) UR - https://doi.org/10.1109/THERMINIC60375.2023.10325906 Y1 - 2023 UR - https://doi.org/10.1109/THERMINIC60375.2023.10325906 SN - 979-8-3503-1862-3 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Zippelius, Andreas A1 - Hufnagel, Elias A1 - Shah, Jainam A1 - Schmid, Maximilian A1 - Elger, Gordon T1 - Reliability of High-Power LEDs Under Varying Thermal Aging Conditions T2 - 2025 31st International Workshop on Thermal Investigations of ICs and Systems (THERMINIC) UR - https://doi.org/10.1109/THERMINIC65879.2025.11216869 Y1 - 2025 UR - https://doi.org/10.1109/THERMINIC65879.2025.11216869 SN - 979-8-3315-9486-2 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Schwan, Hannes A1 - Mohan, Nihesh A1 - Schmid, Maximilian A1 - Saha, Rocky Kumar A1 - Klassen, Holger A1 - Müller, Klaus A1 - Elger, Gordon T1 - Sintering for High Power Optoelectronic Devices JF - Micromachines N2 - 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. UR - https://doi.org/10.3390/mi16101164 Y1 - 2025 UR - https://doi.org/10.3390/mi16101164 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-64984 SN - 2072-666X VL - 16 IS - 10 PB - MDPI CY - Basel ER - TY - CHAP A1 - Schwan, Hannes A1 - Schmid, Maximilian A1 - Elger, Gordon T1 - Layer Resolved Thermal Impedance Measurement with Laser Stimulated Transient Thermal Analysis of Semiconductor Modules T2 - THERMINIC 2024: Proceedings of 2024, 30th International Workshop on Thermal Investigations of ICs and Systems UR - https://doi.org/10.1109/THERMINIC62015.2024.10732233 Y1 - 2024 UR - https://doi.org/10.1109/THERMINIC62015.2024.10732233 SN - 979-8-3503-8782-7 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Schwan, Hannes A1 - Pforr, Johannes A1 - Elger, Gordon ED - Mesago Messe Frankfurt GmbH, T1 - ZVS Class E2 Wireless Power Transfer System with Self-Resonant Transmission Coils for the Biomedical Application T2 - PCIM Europe 2022: International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management UR - https://doi.org/10.30420/565822107 Y1 - 2022 UR - https://doi.org/10.30420/565822107 SN - 978-3-8007-5822-7 PB - VDE Verlag CY - Berlin ER - TY - CHAP A1 - Schwan, Hannes A1 - Schmid, Maximilian A1 - Elger, Gordon T1 - Laser Stimulated Transient Thermal Analysis of Semiconductors T2 - THERMINIC 2022: Proceedings 2022 UR - https://doi.org/10.1109/THERMINIC57263.2022.9950672 KW - transient thermal analysis KW - laser KW - thermal resistance KW - reliability KW - LED Y1 - 2022 UR - https://doi.org/10.1109/THERMINIC57263.2022.9950672 SN - 978-1-6654-9229-4 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Pandey, Amit A1 - Mohd, Zubair Akhtar A1 - Veettil, Nandana Kappuva A1 - Wunderle, Bernhard A1 - Elger, Gordon T1 - Quantitative Kernel estimation from traffic signs using slanted edge spatial frequency response as a sharpness metric JF - Scientific Reports N2 - 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. UR - https://doi.org/10.1038/s41598-026-40556-w Y1 - 2026 UR - https://doi.org/10.1038/s41598-026-40556-w UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-66915 SN - 2045-2322 VL - 16 IS - 1 PB - Springer Nature CY - London ER - TY - JOUR A1 - Pandey, Amit A1 - Kühn, Stephan A1 - Weis, Alexander A1 - Wunderle, Bernhard A1 - Elger, Gordon T1 - Evaluating optical performance degradation of automotive cameras under accelerated aging JF - Optics and Lasers in Engineering N2 - 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. UR - https://doi.org/10.1016/j.optlaseng.2025.109396 Y1 - 2025 UR - https://doi.org/10.1016/j.optlaseng.2025.109396 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-64821 SN - 1873-0302 VL - 2026 IS - 196 PB - Elsevier CY - Amsterdam ER - TY - INPR A1 - Pandey, Amit A1 - Mohd, Zubair Akhtar A1 - Veettil, Nandana Kappuva A1 - Wunderle, Bernhard A1 - Elger, Gordon T1 - Quantitative Kernel Estimation from Traffic Signs using Slanted Edge Spatial Frequency Response as a Sharpness Metric T2 - Research Square N2 - 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. UR - https://doi.org/10.21203/rs.3.rs-6725582/v1 Y1 - 2025 UR - https://doi.org/10.21203/rs.3.rs-6725582/v1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-62419 SN - 2693-5015 PB - Research Square CY - Durham ER - TY - CHAP A1 - Kettelgerdes, Marcel A1 - Mezmer, Peter A1 - Haeussler, Michael J. A1 - Böttger, Gunnar A1 - Tavakolibasti, Majid A1 - Pandey, Amit A1 - Erdogan, Hüseyin A1 - Elger, Gordon A1 - Schacht, Ralph A1 - Wunderle, Bernhard T1 - Realization, multi-field coupled simulation and characterization of a thermo-mechanically robust LiDAR front end on a copper coated glass substrate T2 - Proceedings: IEEE 73rd Electronic Components and Technology Conference, ECTC 2023 UR - https://doi.org/10.1109/ECTC51909.2023.00131 KW - LiDAR KW - Reliability KW - Thermo-mechanical Simulation KW - Optical Simulation KW - VCSEL KW - Lifetime Testing KW - Glass Packaging Y1 - 2023 UR - https://doi.org/10.1109/ECTC51909.2023.00131 SN - 979-8-3503-3498-2 SN - 2377-5726 SP - 753 EP - 760 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Kettelgerdes, Marcel A1 - Pandey, Amit A1 - Unruh, Denis A1 - Erdogan, Hüseyin A1 - Wunderle, Bernhard A1 - Elger, Gordon T1 - Automotive LiDAR Based Precipitation State Estimation Using Physics Informed Spatio-Temporal 3D Convolutional Neural Networks (PIST-CNN) T2 - 2023 29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) UR - https://doi.org/10.1109/M2VIP58386.2023.10413394 Y1 - 2024 UR - https://doi.org/10.1109/M2VIP58386.2023.10413394 SN - 979-8-3503-2562-1 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Pandey, Amit A1 - Unruh, Denis A1 - Kettelgerdes, Marcel A1 - Wunderle, Bernhard A1 - Elger, Gordon ED - Matoba, Osamu ED - Shaw, Joseph A. ED - Valenta, Christopher R. T1 - Evaluation of thermally-induced change in sharpness of automotive cameras by coupled thermo-mechanical and optical simulation T2 - SPIE Future Sensing Technologies 2023 UR - https://doi.org/10.1117/12.2665475 Y1 - 2023 UR - https://doi.org/10.1117/12.2665475 SN - 978-1-5106-5723-6 SN - 978-1-5106-5722-9 PB - SPIE CY - Bellingham ER - TY - CHAP A1 - Pandey, Amit A1 - Kühn, Stephan A1 - Erdogan, Hüseyin A1 - Schneider, Klaus A1 - Elger, Gordon T1 - Finite Element Analysis: A Tool for Investigation of Sharpness Changes in Automotive Cameras T2 - 2020 21st International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE) UR - https://doi.org/10.1109/EuroSimE48426.2020.9152716 KW - Solid modeling KW - Creep KW - Load modeling KW - Lenses KW - Optical imaging KW - Optical sensors KW - Cameras Y1 - 2020 UR - https://doi.org/10.1109/EuroSimE48426.2020.9152716 SN - 978-1-7281-6049-8 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Pandey, Amit A1 - Bhogaraju, Sri Krishna A1 - Lux, Kerstin A1 - Elger, Gordon T1 - Analysis of stress in spherically bent thinned image sensors T2 - 2022 23rd International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE) UR - https://doi.org/10.1109/EuroSimE54907.2022.9758847 KW - image sensors KW - semiconductor device measurement KW - spectroscopy KW - compressive stress KW - bending KW - silicon KW - bonding Y1 - 2022 UR - https://doi.org/10.1109/EuroSimE54907.2022.9758847 SN - 978-1-6654-5836-8 PB - IEEE CY - Piscataway, NJ ER - TY - CHAP A1 - Kühn, Stephan A1 - Pandey, Amit A1 - Zippelius, Andreas A1 - Schneider, Klaus A1 - Erdogan, Hüseyin A1 - Elger, Gordon T1 - Analysis of package design of optic modules for automotive cameras to realize reliable image sharpness T2 - 2020 IEEE 8th Electronics System-Integration Technology Conference (ESTC) UR - https://doi.org/10.1109/ESTC48849.2020.9229786 KW - Condition Monitoring KW - Health Indicator KW - Change in Sharpness KW - Through Focus Y1 - 2020 UR - https://doi.org/10.1109/ESTC48849.2020.9229786 SN - 978-1-7281-6293-5 PB - IEEE CY - Piscataway ER -