TY - JOUR A1 - Liu, E A1 - Bhogaraju, Sri Krishna A1 - Wunderle, Bernhard A1 - Elger, Gordon T1 - Investigation of stress relaxation in SAC305 with micro-Raman spectroscopy JF - Microelectronics Reliability UR - https://doi.org/10.1016/j.microrel.2022.114664 Y1 - 2022 UR - https://doi.org/10.1016/j.microrel.2022.114664 SN - 0026-2714 VL - 2022 IS - 138 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Mohan, Nihesh A1 - Bhogaraju, Sri Krishna A1 - Lysien, Mateusz A1 - Schneider, Ludovic A1 - Granek, Filip A1 - Lux, Kerstin A1 - Elger, Gordon T1 - Drop feature optimization for fine trace inkjet printing T2 - 2021 23rd European Microelectronics and Packaging Conference & Exhibition (EMPC): Technical Papers UR - https://doi.org/10.23919/EMPC53418.2021.9585004 KW - inkjet printing KW - Ag nanoparticles KW - fine trace KW - surface interactions KW - drop feature KW - ink properties KW - sintering Y1 - 2021 UR - https://doi.org/10.23919/EMPC53418.2021.9585004 SN - 978-0-9568086-7-7 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Kettelgerdes, Marcel A1 - Elger, Gordon T1 - In-Field Measurement and Methodology for Modeling and Validation of Precipitation Effects on Solid-State LiDAR Sensors JF - IEEE Journal of Radio Frequency Identification UR - https://doi.org/10.1109/JRFID.2023.3234999 KW - LiDAR KW - adverse weather KW - sensor model KW - automotive KW - simulation KW - virtual validation KW - ROS KW - ADAS Y1 - 2023 UR - https://doi.org/10.1109/JRFID.2023.3234999 SN - 2469-7281 VL - 7 SP - 192 EP - 202 PB - Institute of Electrical and Electronics Engineers (IEEE) CY - New York ER - TY - CHAP A1 - Bhogaraju, Sri Krishna A1 - Schmid, Maximilian A1 - Kotadia, Hiren R. A1 - Conti, Fosca A1 - Elger, Gordon T1 - Highly reliable die-attach bonding with etched brass flakes T2 - 2021 23rd European Microelectronics and Packaging Conference & Exhibition (EMPC): Technical Papers UR - https://doi.org/10.23919/EMPC53418.2021.9584967 KW - sintering KW - etched brass KW - flakes KW - reliability KW - LED KW - die-attach KW - in-situ copper oxide reduction KW - transient thermal analysis Y1 - 2021 UR - https://doi.org/10.23919/EMPC53418.2021.9584967 SN - 978-0-9568086-7-7 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Steinberger, Fabian A1 - Bhogaraju, Sri Krishna A1 - Elger, Gordon T1 - Correlation between the characteristics of printed sinter paste and the quality of sintered interconnects through non-destructive analysis techniques T2 - NordPac 2023 Annual Microelectronics and Packaging Conference and Exhibition: Reviewed Papers UR - https://doi.org/10.23919/NordPac58023.2023.10186250 KW - profilometry KW - sinter paste KW - interconnect KW - SAM Y1 - 2023 UR - https://doi.org/10.23919/NordPac58023.2023.10186250 SN - 978-91-89821-06-4 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Kettelgerdes, Marcel A1 - Elger, Gordon T1 - Modeling Methodology and In-field Measurement Setup to Develop Empiric Weather Models for Solid-State LiDAR Sensors T2 - 2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI) UR - https://doi.org/10.1109/DTPI55838.2022.9998918 KW - LiDAR KW - adverse weather KW - sensor model KW - auto-motive KW - simulation KW - virtual validation KW - ROS KW - ADAS Y1 - 2022 UR - https://doi.org/10.1109/DTPI55838.2022.9998918 SN - 978-1-6654-9227-0 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Tavakolibasti, M. A1 - Meszmer, P. A1 - Kettelgerdes, Marcel A1 - Böttger, Gunnar A1 - Elger, Gordon A1 - Erdogan, Hüseyin A1 - Seshaditya, A. A1 - Wunderle, Bernhard T1 - Structural-thermal-optical-performance (STOP) analysis of a lens stack for realization of a digital twin of an automotive LiDAR 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/EuroSimE54907.2022.9758897 KW - thermo-mechanical simulation KW - optical simulation KW - digital twin KW - structural thermal optical performance analysis Y1 - 2022 UR - https://doi.org/10.1109/EuroSimE54907.2022.9758897 SN - 978-1-6654-5836-8 PB - IEEE CY - Piscataway, NJ ER - TY - JOUR A1 - Senel, Numan A1 - Kefferpütz, Klaus A1 - Doycheva, Kristina A1 - Elger, Gordon T1 - Multi-Sensor Data Fusion for Real-Time Multi-Object Tracking JF - Processes N2 - Sensor data fusion is essential for environmental perception within smart traffic applications. By using multiple sensors cooperatively, the accuracy and probability of the perception are increased, which is crucial for critical traffic scenarios or under bad weather conditions. In this paper, a modular real-time capable multi-sensor fusion framework is presented and tested to fuse data on the object list level from distributed automotive sensors (cameras, radar, and LiDAR). The modular multi-sensor fusion architecture receives an object list (untracked objects) from each sensor. The fusion framework combines classical data fusion algorithms, as it contains a coordinate transformation module, an object association module (Hungarian algorithm), an object tracking module (unscented Kalman filter), and a movement compensation module. Due to the modular design, the fusion framework is adaptable and does not rely on the number of sensors or their types. Moreover, the method continues to operate because of this adaptable design in case of an individual sensor failure. This is an essential feature for safety-critical applications. The architecture targets environmental perception in challenging time-critical applications. The developed fusion framework is tested using simulation and public domain experimental data. Using the developed framework, sensor fusion is obtained well below 10 milliseconds of computing time using an AMD Ryzen 7 5800H mobile processor and the Python programming language. Furthermore, the object-level multi-sensor approach enables the detection of changes in the extrinsic calibration of the sensors and potential sensor failures. A concept was developed to use the multi-sensor framework to identify sensor malfunctions. This feature will become extremely important in ensuring the functional safety of the sensors for autonomous driving. UR - https://doi.org/10.3390/pr11020501 KW - environmental perception KW - sensor fusion KW - autonomous vehicle KW - unscented Kalman filter KW - object tracking KW - roadside units Y1 - 2023 UR - https://doi.org/10.3390/pr11020501 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-31989 SN - 2227-9717 VL - 11 IS - 2 PB - MDPI CY - Basel ER - TY - CHAP A1 - Hermann, Joseph A1 - Schmid, Maximilian A1 - Elger, Gordon T1 - Crack Growth Prediction in High-Power LEDs from TTA, SAM and Simulated Data T2 - THERMINIC 2022: Proceedings 2022 UR - https://doi.org/10.1109/THERMINIC57263.2022.9950673 KW - reliability KW - transient thermal analysis (TTA) KW - scanning acoustic microscopy (SAM) KW - finite element simulation KW - LED KW - solder KW - crack Y1 - 2022 UR - https://doi.org/10.1109/THERMINIC57263.2022.9950673 SN - 978-1-6654-9229-4 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Agrawal, Shiva A1 - Bhanderi, Savankumar A1 - Doycheva, Kristina A1 - Elger, Gordon T1 - Static multi-target-based auto-calibration of RGB cameras, 3D Radar, and 3D Lidar sensors JF - IEEE Sensors Journal UR - https://doi.org/10.1109/JSEN.2023.3300957 KW - Autonomous vehicles KW - camera KW - feature extraction KW - intelligent roadside infrastructure KW - lidar KW - radar KW - sensor calibration Y1 - 2023 UR - https://doi.org/10.1109/JSEN.2023.3300957 SN - 1530-437X VL - 23 IS - 18 SP - 21493 EP - 21505 PB - IEEE CY - Piscataway ER -