TY - JOUR A1 - Kleiner, Jan A1 - Komsiyska, Lidiya A1 - Elger, Gordon A1 - Endisch, Christian T1 - Thermal modelling of a prismatic lithium-ion cell in a battery electric vehicle environment BT - influences of the experimental validation setup JF - Energies N2 - In electric vehicles with lithium-ion battery systems, the temperature of the battery cells has a great impact on performance, safety, and lifetime. Therefore, developing thermal models of lithium-ion batteries to predict and investigate the temperature development and its impact is crucial. Commonly, models are validated with experimental data to ensure correct model behaviour. However, influences of experimental setups or comprehensive validation concepts are often not considered, especially for the use case of prismatic cells in a battery electric vehicle. In this work, a 3D electro–thermal model is developed and experimentally validated to predict the cell’s temperature behaviour for a single prismatic cell under battery electric vehicle (BEV) boundary conditions. One focus is on the development of a single cell’s experimental setup and the investigation of the commonly neglected influences of an experimental setup on the cell’s thermal behaviour. Furthermore, a detailed validation is performed for the laboratory BEV scenario for spatially resolved temperatures and heat generation. For validation, static and dynamic loads are considered as well as the detected experimental influences. The validated model is used to predict the temperature within the cell in the BEV application for constant current and Worldwide harmonized Light vehicles Test Procedure (WLTP) load profile. UR - https://doi.org/10.3390/en13010062 KW - Lithium-Ionen-Batterie KW - Elektrofahrzeug Y1 - 2019 UR - https://doi.org/10.3390/en13010062 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-7532 SN - 1996-1073 VL - 13 IS - 1 PB - MDPI CY - Basel ER - TY - JOUR A1 - Liu, E A1 - Conti, Fosca A1 - Bhogaraju, Sri Krishna A1 - Signorini, Raffaella A1 - Pedron, Danilo A1 - Wunderle, Bernhard A1 - Elger, Gordon T1 - Thermomechanical stress in GaN-LEDs soldered onto Cu substrates studied using finite element method and Raman spectroscopy JF - Journal of Raman Spectroscopy N2 - 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. UR - https://doi.org/10.1002/jrs.5947 KW - finite element method KW - gallium nitride (GaN) KW - LED KW - semiconductor KW - thermomechanical stress Y1 - 2020 UR - https://doi.org/10.1002/jrs.5947 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9438 SN - 1097-4555 VL - 51 IS - 10 SP - 2083 EP - 2094 PB - Wiley CY - Chichester ER - TY - CHAP A1 - Agrawal, Shiva A1 - Elger, Gordon T1 - Concept of infrastructure based environment perception for IN2Lab test field for automated driving T2 - 2021 IEEE International Smart Cities Conference (ISC2) UR - https://doi.org/10.1109/ISC253183.2021.9562894 KW - RSU KW - Radar KW - Lidar KW - Camera KW - Intelligent Infrastructure KW - Autonomous Driving KW - Sensor fusion Y1 - 2021 UR - https://doi.org/10.1109/ISC253183.2021.9562894 SN - 978-1-6654-4919-9 SN - 2687-8860 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Liu, E A1 - Conti, Fosca A1 - Bhogaraju, Sri Krishna A1 - Elger, Gordon T1 - Simulations and experiments to analyze stress phenomena in soldered and sintered interconnections between silicon nitride chips and copper substrates T2 - 2021 22nd International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE) UR - https://doi.org/10.1109/EuroSimE52062.2021.9410869 KW - Performance evaluation KW - Analytical models KW - Compressive stress KW - Thermomechanical processes KW - Data models KW - Microelectronics KW - Finite element analysis Y1 - 2021 UR - https://doi.org/10.1109/EuroSimE52062.2021.9410869 SN - 978-1-6654-1373-2 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Schmid, Maximilian A1 - Bhogaraju, Sri Krishna A1 - Liu, E A1 - Elger, Gordon T1 - Comparison of Nondestructive Testing Methods for Solder, Sinter, and Adhesive Interconnects in Power and Opto-Electronics JF - Applied Sciences N2 - Reliability is one of the major requirements for power and opto-electronic devices across all segments. High operation temperature and/or high thermomechanical stress cause defects and degradation of materials and interconnects, which may lead to malfunctions with costly or even life-threatening consequences. To avoid or at least reduce failures, nondestructive testing (NDT) methods are common within development and production of power and opto-electronics. Currently, the dominating NDT methods are X-ray, scanning acoustic microscopy (SAM), and transient thermal analysis (TTA). However, they have different strengths and weaknesses with respect to materials and mechanical designs. This paper compares these NDT methods for different interconnect technologies, i.e., reflow soldering, adhesive, and sintered interconnection. While X-ray provided adequate results for soldered interfaces, inspection of adhesives and sintered interconnects was not possible. With SAM, evaluation of adhesives and sintered interconnects was also feasible, but quality depended strongly on the sample under test. TTA enabled sufficiently detailed results for all the interconnect applications. Automated TTA equipment, as the in-house developed tester used within this investigation, enabled measurement times compatible with SAM and X-ray. In the investigations, all methods revealed their pros and cons, and their selection has to depend on the sample under tests and the required analysis depth and data details. In the paper, guidelines are formulated for an appropriate decision on the NDT method depending on sample and requirements. UR - https://doi.org/10.3390/app10238516 KW - reliability KW - nondestructive testing KW - power electronics KW - X-ray KW - scanning acoustic microscopy KW - transient thermal analysis KW - TTA KW - sintering KW - LED KW - MOSFET Y1 - 2020 UR - https://doi.org/10.3390/app10238516 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-12518 SN - 2076-3417 VL - 10 IS - 23 PB - MDPI CY - Basel ER - TY - INPR A1 - Han, Longfei A1 - Xu, Qiuyu A1 - Kefferpütz, Klaus A1 - Elger, Gordon A1 - Beyerer, Jürgen T1 - Applying Extended Object Tracking for Self-Localization of Roadside Radar Sensors N2 - Intelligent Transportation Systems (ITS) can benefit from roadside 4D mmWave radar sensors for large-scale traffic monitoring due to their weatherproof functionality, long sensing range and low manufacturing cost. However, the localization method using external measurement devices has limitations in urban environments. Furthermore, if the sensor mount exhibits changes due to environmental influences, they cannot be corrected when the measurement is performed only during the installation. In this paper, we propose self-localization of roadside radar data using Extended Object Tracking (EOT). The method analyses both the tracked trajectories of the vehicles observed by the sensor and the aerial laser scan of city streets, assigns labels of driving behaviors such as "straight ahead", "left turn", "right turn" to trajectory sections and road segments, and performs Semantic Iterative Closest Points (SICP) algorithm to register the point cloud. The method exploits the result from a down stream task -- object tracking -- for localization. We demonstrate high accuracy in the sub-meter range along with very low orientation error. The method also shows good data efficiency. The evaluation is done in both simulation and real-world tests. UR - https://doi.org/10.48550/arXiv.2407.03084 Y1 - 2024 UR - https://doi.org/10.48550/arXiv.2407.03084 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58361 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Meess, Henri A1 - Gerner, Jeremias A1 - Hein, Daniel A1 - Schmidtner, Stefanie A1 - Elger, Gordon T1 - Reinforcement Learning for Traffic Signal Control Optimization: A Concept for Real-World Implementation T2 - AAMAS '22: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems UR - https://dl.acm.org/doi/10.5555/3535850.3536081 KW - Multi-Agent Reinforcement Learning in real-world KW - MARL KW - traffic optimization KW - multimodal traffic KW - DRL Y1 - 2022 UR - https://dl.acm.org/doi/10.5555/3535850.3536081 SN - 978-1-4503-9213-6 SP - 1699 EP - 1701 PB - International Foundation for Autonomous Agents and Multiagent Systems CY - Richland ER - TY - CHAP A1 - Agrawal, Shiva A1 - Song, Rui A1 - Doycheva, Kristina A1 - Knoll, Alois A1 - Elger, Gordon ED - Klein, Cornel ED - Jarke, Matthias ED - Ploeg, Jeroen ED - Helfert, Markus ED - Berns, Karsten ED - Gusikhin, Oleg T1 - Intelligent Roadside Infrastructure for Connected Mobility T2 - Smart Cities, Green Technologies, and Intelligent Transport Systems: 11th International Conference, SMARTGREENS 2022 and 8th International Conference, VEHITS 2022: Revised Selected Papers UR - https://doi.org/10.1007/978-3-031-37470-8_6 Y1 - 2023 UR - https://doi.org/10.1007/978-3-031-37470-8_6 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-41761 SN - 978-3-031-37470-8 SN - 1865-0937 SP - 134 EP - 157 PB - Springer CY - Cham ER - TY - CHAP A1 - Agrawal, Shiva A1 - Bhanderi, Savankumar A1 - Amanagi, Sumit A1 - Doycheva, Kristina A1 - Elger, Gordon ED - Vinel, Alexey ED - Ploeg, Jeroen ED - Berns, Karsten ED - Gusikhin, Oleg T1 - Instance Segmentation and Detection of Children to Safeguard Vulnerable Traffic User by Infrastructure T2 - Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems N2 - Cameras mounted on intelligent roadside infrastructure units and vehicles can detect humans on the road using state-of-the-art perception algorithms, but these algorithms are presently not trained to distinguish between human and adult. However, this is a crucial requirement from a safety perspective because a child may not follow all the traffic rules, particularly while crossing the road. Moreover, a child may stop or may start playing on the road. In such situations, the separation of a child from an adult is necessary. The work in this paper targets to solve this problem by applying a transfer-learning-based neural network approach to classify child and adult separately in camera images. The described work is comprised of image data collection, data annotation, transfer learning-based model development, and evaluation. For the work, Mask-RCNN (region-based convolutional neural network) with different backbone architectures and two different baselines are investigated and the perception precision of the architectures after transfer-learning is compared. The results reveal that the best performing trained model is able to detect and classify children and adults separately in different road scenarios with segmentation mask AP (average precision) of 85% and bounding box AP of 92%. UR - https://doi.org/10.5220/0011825400003479 KW - Child and Adult Detection KW - Classification KW - Intelligent Roadside Infrastructure KW - Image Segmentation KW - Mask-RCNN KW - Traffic Flow Optimization KW - Transfer Learning Y1 - 2023 UR - https://doi.org/10.5220/0011825400003479 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-41785 SN - 978-989-758-652-1 SN - 2184-495X SP - 206 EP - 214 PB - SciTePress CY - Setúbal ER - TY - JOUR A1 - Agrawal, Shiva A1 - Bhanderi, Savankumar A1 - Elger, Gordon T1 - Infra-3DRC-FusionNet: Deep Fusion of Roadside Mounted RGB Mono Camera and Three-Dimensional Automotive Radar for Traffic User Detection JF - Sensors N2 - Mono RGB cameras and automotive radar sensors provide a complementary information set that makes them excellent candidates for sensor data fusion to obtain robust traffic user detection. This has been widely used in the vehicle domain and recently introduced in roadside-mounted smart infrastructure-based road user detection. However, the performance of the most commonly used late fusion methods often degrades when the camera fails to detect road users in adverse environmental conditions. The solution is to fuse the data using deep neural networks at the early stage of the fusion pipeline to use the complete data provided by both sensors. Research has been carried out in this area, but is limited to vehicle-based sensor setups. Hence, this work proposes a novel deep neural network to jointly fuse RGB mono-camera images and 3D automotive radar point cloud data to obtain enhanced traffic user detection for the roadside-mounted smart infrastructure setup. Projected radar points are first used to generate anchors in image regions with a high likelihood of road users, including areas not visible to the camera. These anchors guide the prediction of 2D bounding boxes, object categories, and confidence scores. Valid detections are then used to segment radar points by instance, and the results are post-processed to produce final road user detections in the ground plane. The trained model is evaluated for different light and weather conditions using ground truth data from a lidar sensor. It provides a precision of 92%, recall of 78%, and F1-score of 85%. The proposed deep fusion methodology has 33%, 6%, and 21% absolute improvement in precision, recall, and F1-score, respectively, compared to object-level spatial fusion output. UR - https://doi.org/10.3390/s25113422 Y1 - 2025 UR - https://doi.org/10.3390/s25113422 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59421 SN - 1424-8220 VL - 25 IS - 11 PB - MDPI CY - Basel ER - TY - JOUR A1 - Da Rosa Zanatta, Mateus A1 - Carvalho Lustosa da Costa, Joao Paulo A1 - Antreich, Felix A1 - Haardt, Martin A1 - Elger, Gordon A1 - Lopes de Mendonça, Fábio Lúcio A1 - de Sousa Junior, Rafael Timóteo T1 - Tensor-based framework with model order selection and high accuracy factor decomposition for time-delay estimation in dynamic multipath scenarios JF - IEEE Access N2 - Global Navigation Satellite Systems (GNSS) are crucial for applications that demand very accurate positioning. Tensor-based time-delay estimation methods, such as CPD-GEVD, DoA/KRF, and SECSI, combined with the GPS3 L1C signal, are capable of, significantly, mitigating the positioning degradation caused by multipath components. However, even though these schemes require an estimated model order, they assume that the number of multipath components is constant. In GNSS applications, the number of multipath components is time-varying in dynamic scenarios. Thus, in this paper, we propose a tensor-based framework with model order selection and high accuracy factor decomposition for time-delay estimation in dynamic multipath scenarios. Our proposed approach exploits the estimates of the model order for each slice by grouping the data tensor slices into sub-tensors to provide high accuracy factor decomposition. We further enhance the proposed approach by incorporating the tensor-based Multiple Denoising (MuDe). UR - https://doi.org/10.1109/ACCESS.2020.3024597 KW - Global navigation satellite systems (GNSS) KW - global positioning system (GPS) KW - GPS3 KW - time-delay estimation (TDE) KW - multipath components KW - model order selection (MOS) Y1 - 2020 UR - https://doi.org/10.1109/ACCESS.2020.3024597 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9528 SN - 2169-3536 VL - 8 SP - 174931 EP - 174942 PB - IEEE CY - New York ER - TY - JOUR A1 - Mohan, Nihesh A1 - Ahuir-Torres, Juan Ignacio A1 - Kotadia, Hiren R. A1 - Elger, Gordon T1 - Laser sintering of Cu particle-free inks for high-performance printed electronics JF - npj Flexible Electronics N2 - This study investigates laser sintering of Cu particle-free ink (Cu formate tetrahydrate—amino-2-propanol complex) as an alternative to conventional sintering in an oven (under inert/reducing atmosphere). Utilizing benefits of high-speed localized heating using laser, substrate damage can be prevented for low-melting substrates such as Polyethylene Terephthalate (PET). Firstly, a suitable sintering process window is achieved based on energy density for two different flexible polymeric susbtrates: Polyimide and PET using different laser parameters (laser power, scan rate and spot diameter). Subsequently, characterization of laser sintered traces are also made using different laser optic profiles (Gaussian and top hat). Different methodologies for fabrication of metallized Cu layer were also demonstrated. A very low bulk resistivity of 3.24 µΩcm (1.87 times of bulk Cu) was achieved on trace thickness of 0.85 ± 0.15 µm exhibiting good adherence to polymeric substrates. A promising fabrication process of low-cost and reliable flexible printed electronic devices is demonstrated. UR - https://doi.org/10.1038/s41528-025-00389-5 Y1 - 2025 UR - https://doi.org/10.1038/s41528-025-00389-5 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58266 SN - 2397-4621 VL - 9 IS - 1 PB - Springer CY - London ER - TY - JOUR A1 - Theissler, Andreas A1 - Pérez-Velázquez, Judith A1 - Kettelgerdes, Marcel A1 - Elger, Gordon T1 - Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry JF - Reliability Engineering & System Safety N2 - Recent developments in maintenance modelling fuelled by data-based approaches such as machine learning (ML), have enabled a broad range of applications. In the automotive industry, ensuring the functional safety over the product life cycle while limiting maintenance costs has become a major challenge. One crucial approach to achieve this, is predictive maintenance (PdM). Since modern vehicles come with an enormous amount of operating data, ML is an ideal candidate for PdM. While PdM and ML for automotive systems have both been covered in numerous review papers, there is no current survey on ML-based PdM for automotive systems. The number of publications in this field is increasing — underlining the need for such a survey. Consequently, we survey and categorize papers and analyse them from an application and ML perspective. Following that, we identify open challenges and discuss possible research directions. We conclude that (a) publicly available data would lead to a boost in research activities, (b) the majority of papers rely on supervised methods requiring labelled data, (c) combining multiple data sources can improve accuracies, (d) the use of deep learning methods will further increase but requires efficient and interpretable methods and the availability of large amounts of (labelled) data. UR - https://doi.org/10.1016/j.ress.2021.107864 KW - predictive maintenance KW - artificial intelligence KW - machine learning KW - deep learning KW - vehicle KW - automotive KW - reliability KW - lifetime prediction KW - condition monitoring Y1 - 2021 UR - https://doi.org/10.1016/j.ress.2021.107864 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9673 SN - 0951-8320 VL - 2021 IS - 215 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Agrawal, Shiva A1 - Bhanderi, Savankumar A1 - Elger, Gordon T1 - Semi-Automatic Annotation of 3D Radar and Camera for Smart Infrastructure-Based Perception JF - IEEE Access N2 - Environment perception using camera, radar, and/or lidar sensors has significantly improved in the last few years because of deep learning-based methods. However, a large group of these methods fall into the category of supervised learning, which requires a considerable amount of annotated data. Due to uncertainties in multi-sensor data, automating the data labeling process is extremely challenging; hence, it is performed manually to a large extent. Even though full automation of such a process is difficult, semiautomation can be a significant step to ease this process. However, the available work in this regard is still very limited; hence, in this paper, a novel semi-automatic annotation methodology is developed for labeling RGB camera images and 3D automotive radar point cloud data using a smart infrastructure-based sensor setup. This paper also describes a new method for 3D radar background subtraction to remove clutter and a new object category, GROUP, for radar-based object detection for closely located vulnerable road users. To validate the work, a dataset named INFRA-3DRC is created using this methodology, where 75% of the labels are automatically generated. In addition, a radar cluster classifier and an image classifier are developed, trained, and tested on this dataset, achieving accuracy of 98.26% and 94.86%, respectively. The dataset and Python scripts are available at https://fraunhoferivi.github.io/INFRA-3DRC-Dataset/. UR - https://doi.org/10.1109/ACCESS.2024.3373310 Y1 - 2024 UR - https://doi.org/10.1109/ACCESS.2024.3373310 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-46180 SN - 2169-3536 VL - 12 SP - 34325 EP - 34341 PB - IEEE CY - New York ER - TY - JOUR A1 - Mohan, Nihesh A1 - Steinberger, Fabian A1 - Wächter, Sonja A1 - Erdogan, Hüseyin A1 - Elger, Gordon T1 - Additive Manufacturing for Automotive Radar Sensors Using Copper Inks and Pastes JF - Applied Sciences N2 - Radar sensors are critical for obstacle detection and navigation, especially for automated driving. Using the use-case “printing of heating coils on the inside of the front housing (primary radome)” needed for de-icing in winter, it is demonstrated that additive manufacturing (AM) can provide economic and functional benefits for manufacturing of the sensors. AM will allow significant cost reduction by eliminating parts and simplifying the manufacturing process. Different AM technologies for the coils were investigated, first, by applying the conductive traces by fused deposition modeling (FDM), and, second, by printing copper particle-free inks and pastes. The metal layers were electrically and mechanically characterized using a profilometer to measure the trace dimension and a four-point probe to measure the resistance. It was revealed that low-cost conductive filaments with low resistivity and current carrying capacity are commercially still not available. The best option sourced was a copper–polyester-based filament with 6000 µΩcm after printing. Therefore, low-cost particle-free copper inks and commercial copper flake paste were selected to print the heating coil. The Cu particle-free inks were amine-based Cu (II) formate complexes, where the Cu exists in an ionic form. Using contactless printing processes such as ink-jet printing or pneumatic dispensing, the traces could be deposited onto the low-melting temperature (225 °C) polymeric radome structure. After printing, the material needed to be sintered to form the conductive copper traces. To avoid damaging the polymer radome during sintering, two different processes were investigated: low-temperature (<150 °C) sintering in an oven for 30 min or fast laser sintering. The sintered Cu layers achieved the following specific electric resistivities when slowly sintered in the oven: paste 4 µΩcm and ink 8.8 µΩcm. Using laser sintering, the ink achieved 3.2 µΩcm because the locally high temperature provides better sintering. Also, the adhesion was significantly increased to (5 B). Therefore, laser sintering is the preferred technology. In addition, it allows fast processing directly after printing. Commercial equipment is available where printing and laser sintering is integrated. The potential of low-cost copper material and the integration in additive manufacturing of electronic systems using radar sensors as an example are demonstrated in this paper. UR - https://doi.org/10.3390/app15052676 Y1 - 2025 UR - https://doi.org/10.3390/app15052676 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58065 SN - 2076-3417 VL - 15 IS - 5 PB - MDPI CY - Basel ER - TY - CHAP A1 - Kettelgerdes, Marcel A1 - Böhm, Lena A1 - Elger, Gordon T1 - Correlating Intrinsic Parameters and Sharpness for Condition Monitoring of Automotive Imaging Sensors T2 - 2021 5th International Conference on System Reliability and Safety (ICSRS) UR - https://doi.org/10.1109/ICSRS53853.2021.9660665 Y1 - 2021 UR - https://doi.org/10.1109/ICSRS53853.2021.9660665 SN - 978-1-6654-0049-7 SP - 298 EP - 306 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Tavakolibasti, M. A1 - Meszmer, P. A1 - Böttger, Gunnar A1 - Kettelgerdes, Marcel A1 - Elger, Gordon A1 - Erdogan, Hüseyin A1 - Seshaditya, A. A1 - Wunderle, Bernhard T1 - Thermo-mechanical-optical coupling within a digital twin development for automotive LiDAR T2 - 2021 22nd International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE) UR - https://doi.org/10.1109/EuroSimE52062.2021.9410831 KW - Couplings KW - Laser radar KW - Digital twin KW - Thermomechanical processes KW - Adaptive optics KW - Optical coupling KW - Real-time systems Y1 - 2021 UR - https://doi.org/10.1109/EuroSimE52062.2021.9410831 SN - 978-1-6654-1373-2 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Pascucci, Jacopo A1 - Conti, Fosca A1 - Bhogaraju, Sri Krishna A1 - Signorini, Raffaella A1 - Liu, E A1 - Pedron, Danilo A1 - Elger, Gordon ED - Cheben, Pavel ED - Čtyroký, Jiří ED - Molina-Fernández, Iñigo T1 - Micro-Raman to detect stress phenomena in Si-chips bonded onto Cu substrates T2 - Integrated Optics: Design, Devices, Systems and Applications VI UR - https://doi.org/10.1117/12.2576414 Y1 - 2021 UR - https://doi.org/10.1117/12.2576414 SN - 978-1-5106-4385-7 N1 - Poster + Paper PB - SPIE CY - Bellingham ER - TY - CHAP A1 - Bhogaraju, Sri Krishna A1 - Schmid, Maximilian A1 - Hufnagel, Elias A1 - Conti, Fosca A1 - Kotadia, Hiren R. A1 - Elger, Gordon T1 - Low temperature and low pressure die-attach bonding of high power light emitting diodes with self reducing copper complex paste T2 - IEEE 71st Electronic Components and Technology Conference ECTC 2021, Proceedings UR - https://doi.org/10.1109/ECTC32696.2021.00094 KW - low temperature sintering KW - rapid sintering KW - Cu(II) formate KW - reducing binder KW - low pressure sintering KW - high bond strength Y1 - 2021 UR - https://doi.org/10.1109/ECTC32696.2021.00094 SN - 978-1-6654-4097-4 SP - 526 EP - 531 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Schmid, Maximilian A1 - Bhogaraju, Sri Krishna A1 - Elger, Gordon T1 - Characterization of copper sintered interconnects by transient thermal analysis T2 - 2021 International Conference on Electronics Packaging (ICEP 2021) UR - https://doi.org/10.23919/ICEP51988.2021.9451966 KW - copper sintering KW - transient thermal analysis (TTA) KW - thermal impedance (Zth) KW - non-destructive testing (NDT) KW - scanning acoustic microscopy (SAM) Y1 - 2021 UR - https://doi.org/10.23919/ICEP51988.2021.9451966 SN - 978-4-9911-9111-4 SP - 71 EP - 72 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Kleiner, Jan A1 - Heider, Alexander A1 - Hanzl, Christian A1 - Komsiyska, Lidiya A1 - Elger, Gordon A1 - Endisch, Christian T1 - Thermal behavior of an intelligent li-ion cell under vehicle conditions T2 - Proceedings: IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society UR - https://doi.org/10.1109/IECON43393.2020.9254336 KW - Li-ion KW - electro-thermal model KW - intelligent battery KW - reconfigurable system KW - smart cell Y1 - 2020 UR - https://doi.org/10.1109/IECON43393.2020.9254336 SN - 978-1-7281-5414-5 SN - 2577-1647 SP - 2081 EP - 2086 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Conti, Fosca A1 - Liu, E A1 - Bhogaraju, Sri Krishna A1 - Wunderle, Bernhard A1 - Elger, Gordon T1 - Finite Element simulations and Raman measurements to investigate thermomechanical stress in GaN-LEDs T2 - 2020 IEEE 8th Electronics System-Integration Technology Conference (ESTC) UR - https://doi.org/10.1109/ESTC48849.2020.9229843 KW - finite element method KW - gallium nitride (GaN) KW - LED KW - Raman spectroscopy KW - semiconductor KW - thermomechanical stress Y1 - 2020 UR - https://doi.org/10.1109/ESTC48849.2020.9229843 SN - 978-1-7281-6293-5 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Bhogaraju, Sri Krishna A1 - Conti, Fosca A1 - Elger, Gordon T1 - Copper die bonding using copper formate based pastes with α-terpineol, amino-2-propanol and hexylamine as binders T2 - 2020 IEEE 8th Electronics System-Integration Technology Conference (ESTC) UR - https://doi.org/10.1109/ESTC48849.2020.9229801 KW - copper inks KW - sintering KW - low temperature KW - copper nano particles KW - copper formate KW - binder KW - terpineol KW - hexylamine KW - amino-2-propanol Y1 - 2020 UR - https://doi.org/10.1109/ESTC48849.2020.9229801 SN - 978-1-7281-6293-5 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Keim, Simon A1 - Tetzlaff, Ulrich A1 - Elger, Gordon T1 - The influence of different-sized Ni micro- and nanopowders on the processing and microstructural properties of Sn-Ag-Cu-solder with low Ag content T2 - Proceedings: IEEE 70th Electronic Components and Technology Conference, ECTC 2020 UR - https://doi.org/10.1109/ECTC32862.2020.00312 KW - Composites KW - Soldering KW - Porosity KW - Microstructure KW - Intermetallics KW - Phase interfaces Y1 - 2020 UR - https://doi.org/10.1109/ECTC32862.2020.00312 SN - 978-1-7281-6180-8 SN - 2377-5726 SP - 2005 EP - 2012 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Conti, Fosca A1 - Liu, E A1 - Bhogaraju, Sri Krishna A1 - Lenz, Christoph A1 - Seitz, Roland A1 - Elger, Gordon T1 - Stress evaluations of silicon nitride chips bonded onto copper substrates via SAC soldering, AuSn soldering, and copper sintering T2 - 2020 26th International Workshop on Thermal Investigations of ICs and Systems (THERMINIC) UR - https://doi.org/10.1109/THERMINIC49743.2020.9420523 KW - Creep KW - Thermomechanical processes KW - Silicon nitride KW - Copper KW - Plastics KW - Microstructure KW - Soldering Y1 - 2021 UR - https://doi.org/10.1109/THERMINIC49743.2020.9420523 SN - 978-1-7281-7643-7 SP - 204 EP - 208 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Schmid, Maximilian A1 - Bhogaraju, Sri Krishna A1 - Riedel, Andreas A1 - Elger, Gordon T1 - Development of an in-line capable transient thermal analysis equipment for a power module with five half bridges T2 - 2020 26th International Workshop on Thermal Investigations of ICs and Systems (THERMINIC) UR - https://doi.org/10.1109/THERMINIC49743.2020.9420493 KW - MOSFET KW - Power measurement KW - Pulse measurements KW - Current measurement KW - Multichip modules KW - Time measurement KW - Thermal analysis Y1 - 2021 UR - https://doi.org/10.1109/THERMINIC49743.2020.9420493 SN - 978-1-7281-7643-7 SP - 268 EP - 273 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Liu, E A1 - Schmid, Maximilian A1 - Bhogaraju, Sri Krishna A1 - Elger, Gordon T1 - Advanced location resolved transient thermal analysis T2 - 2020 26th International Workshop on Thermal Investigations of ICs and Systems (THERMINIC) UR - https://doi.org/10.1109/THERMINIC49743.2020.9420538 KW - Temperature measurement KW - Semiconductor device measurement KW - Thermal resistance KW - Thermal conductivity KW - Dielectric measurement KW - Thermal analysis KW - Dielectrics Y1 - 2021 UR - https://doi.org/10.1109/THERMINIC49743.2020.9420538 SN - 978-1-7281-7643-7 SP - 191 EP - 196 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Mokhtari, Omid A1 - Conti, Fosca A1 - Saccon, Rodolfo A1 - Bhogaraju, Sri Krishna A1 - Elger, Gordon T1 - Formic acid and formate salts for chemical vapor deposition of copper on glass substrates at atmospheric pressure JF - New Journal of Chemistry UR - https://doi.org/10.1039/D1NJ02476K Y1 - 2021 UR - https://doi.org/10.1039/D1NJ02476K SN - 1369-9261 VL - 45 IS - 43 SP - 20133 EP - 20139 PB - RSC CY - London ER - TY - JOUR A1 - Bhogaraju, Sri Krishna A1 - Kotadia, Hiren R. A1 - Conti, Fosca A1 - Mauser, Armin A1 - Rubenbauer, Thomas A1 - Bruetting, Robert A1 - Schneider-Ramelow, Martin A1 - Elger, Gordon T1 - Die-attach bonding with etched micro brass metal pigment flakes for high-power electronics packaging JF - ACS Applied Electronic Materials UR - https://doi.org/10.1021/acsaelm.1c00721 KW - sintering KW - brass flakes KW - nanostructured materials KW - self-reducing KW - etching KW - surface modifications Y1 - 2021 UR - https://doi.org/10.1021/acsaelm.1c00721 SN - 2637-6113 VL - 3 IS - 10 SP - 4587 EP - 4603 PB - ACS Publications CY - Washington ER - TY - JOUR A1 - Kleiner, Jan A1 - Singh, Randeep A1 - Schmid, Michael A1 - Komsiyska, Lidiya A1 - Elger, Gordon A1 - Endisch, Christian T1 - Influence of heat pipe assisted terminal cooling on the thermal behavior of a large prismatic lithium-ion cell during fast charging in electric vehicles JF - Applied Thermal Engineering UR - https://doi.org/10.1016/j.applthermaleng.2020.116328 KW - Lithium-ion battery KW - Battery modeling KW - Electro-thermal model KW - Fast charging KW - Terminal cooling KW - Heat pipe Y1 - 2020 UR - https://doi.org/10.1016/j.applthermaleng.2020.116328 SN - 1359-4311 VL - 2021 IS - 188 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Schmid, Maximilian A1 - Bhogaraju, Sri Krishna A1 - Hanss, Alexander A1 - Elger, Gordon T1 - A new noise-suppression algorithm for transient thermal analysis in semiconductors over pulse superposition JF - IEEE Transactions on Instrumentation and Measurement UR - https://doi.org/10.1109/TIM.2020.3011818 KW - LED KW - linear time-invariant (LTI) system KW - MOSFET KW - noise suppression KW - reliability KW - semiconductor KW - signal processing KW - thermal impedance (Zth) KW - transient thermal analysis (TTA) Y1 - 2021 UR - https://doi.org/10.1109/TIM.2020.3011818 SN - 0018-9456 VL - 70 PB - IEEE CY - New York ER - TY - JOUR A1 - Bhogaraju, Sri Krishna A1 - Mokhtari, Omid A1 - Conti, Fosca A1 - Elger, Gordon T1 - Die-attach bonding for high temperature applications using thermal decomposition of copper(II) formate with polyethylene glycol JF - Scripta Materialia UR - https://doi.org/10.1016/j.scriptamat.2020.02.045 Y1 - 2020 UR - https://doi.org/10.1016/j.scriptamat.2020.02.045 SN - 1359-6462 VL - 2020 IS - 182 SP - 74 EP - 80 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Bhogaraju, Sri Krishna A1 - Conti, Fosca A1 - Kotadia, Hiren R. A1 - Keim, Simon A1 - Tetzlaff, Ulrich A1 - Elger, Gordon T1 - Novel approach to copper sintering using surface enhanced brass micro flakes for microelectronics packaging JF - Journal of Alloys and Compounds UR - https://doi.org/10.1016/j.jallcom.2020.156043 KW - Metals and alloys KW - Microstructure KW - Surfaces and interfaces KW - Sintering Y1 - 2020 UR - https://doi.org/10.1016/j.jallcom.2020.156043 SN - 1873-4669 VL - 2020 IS - 844 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Mokhtari, Omid A1 - Conti, Fosca A1 - Bhogaraju, Sri Krishna A1 - Meier, Markus A1 - Schweigart, Helmut A1 - Tetzlaff, Ulrich A1 - Elger, Gordon T1 - Characterization of tin-oxides and tin-formate crystals obtained from SnAgCu solder alloy under formic acid vapor JF - New Journal of Chemistry UR - https://doi.org/10.1039/C9NJ02135C Y1 - 2019 UR - https://doi.org/10.1039/C9NJ02135C SN - 1369-9261 VL - 43 IS - 26 SP - 10227 EP - 10231 PB - RSC CY - London ER - TY - CHAP A1 - Kleiner, Jan A1 - Komsiyska, Lidiya A1 - Elger, Gordon A1 - Endisch, Christian T1 - Modelling of 3D temperature behavior of prismatic lithium-ion cell with focus on experimental validation under battery electric vehicle conditions T2 - 2019 25th International Workshop on Thermal Investigations of ICs and Systems (THERMINIC) UR - https://doi.org/10.1109/THERMINIC.2019.8923604 Y1 - 2019 UR - https://doi.org/10.1109/THERMINIC.2019.8923604 SN - 978-1-7281-2078-2 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Zippelius, Andreas A1 - Hanss, Alexander A1 - Liu, E A1 - Schmid, Maximilian A1 - Pérez-Velázquez, Judith A1 - Elger, Gordon T1 - Comparing prediction methods for LED failure measured with Transient Thermal Analysis 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/EuroSimE48426.2020.9152657 KW - Temperature measurement KW - Artificial neural networks KW - Light emitting diodes KW - Aging KW - Current measurement KW - Training KW - Heating systems Y1 - 2020 UR - https://doi.org/10.1109/EuroSimE48426.2020.9152657 SN - 978-1-7281-6049-8 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Bhogaraju, Sri Krishna A1 - Conti, Fosca A1 - Schmid, Maximilian A1 - Meier, Markus A1 - Schweigart, Helmut A1 - Elger, Gordon T1 - Development of sinter paste with surface modified copper alloy particles for die-attach bonding T2 - ETG-Fachbericht 161, CIPS 2020, 11th International Conference on Integrated Power Electronics Systems Y1 - 2020 UR - https://www.vde-verlag.de/proceedings-de/455225100.html SN - 978-3-8007-5226-3 SN - 978-3-8007-5225-6 N1 - Auch veröffentlicht auf IEEE: https://ieeexplore.ieee.org/document/9097746 SP - 582 EP - 587 PB - VDE Verlag CY - Berlin ER - TY - CHAP A1 - Hanss, Alexander A1 - Liu, E A1 - Abdullah, Muhammad Rizwan A1 - Elger, Gordon T1 - Failure Identification in LED packages by Transient Thermal Analysis and Calibrated FE Models T2 - 2019 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE) UR - https://doi.org/10.1109/EuroSimE.2019.8724558 KW - Conferences KW - Microelectronics KW - Micromechanical devices KW - Light emitting diodes KW - Aluminum nitride KW - III-V semiconductor materials Y1 - 2019 UR - https://doi.org/10.1109/EuroSimE.2019.8724558 SN - 978-1-5386-8040-7 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Elger, Gordon A1 - Liu, E A1 - Conti, Fosca A1 - Signorini, Raffaella A1 - Brugnolotto, Enrico A1 - Bhogaraju, Sri Krishna T1 - Modelling Thermo-Mechanical Stress in GaN-LEDs Soldered on Copper Substrate with Simulations Validated by Raman Experiments T2 - 2019 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE) UR - https://doi.org/10.1109/EuroSimE.2019.8724533 KW - Conferences KW - Microelectronics KW - Micromechanical devices Y1 - 2019 UR - https://doi.org/10.1109/EuroSimE.2019.8724533 SN - 978-1-5386-8040-7 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Elger, Gordon A1 - Mokhtari, Omid A1 - Bhogaraju, Sri Krishna A1 - Conti, Fosca A1 - Meier, Markus A1 - Schweigart, Helmut T1 - Analyse der Reaktionsprodukte von Metall-Formiaten im rückstandfreien Lötprozess T2 - GMM-Fb. 94: EBL 2020 – Elektronische Baugruppen und Leiterplatten UR - https://www.vde-verlag.de/proceedings-de/455185053.html Y1 - 2020 UR - https://www.vde-verlag.de/proceedings-de/455185053.html SN - 978-3-8007-5185-3 SN - 978-3-8007-5186-0 PB - VDE Verlag CY - Berlin ER - TY - CHAP A1 - Bhogaraju, Sri Krishna A1 - Mokhtari, Omid A1 - Pascucci, Jacopo A1 - Conti, Fosca A1 - Kotadia, Hiren R. A1 - Elger, Gordon T1 - A multi-pronged approach to low-pressure Cu sintering using surface-modified particles, substrate and chip metallization T2 - International Symposium on Microelectronics UR - https://doi.org/10.4071/2380-4505-2019.1.000387 Y1 - 2019 UR - https://doi.org/10.4071/2380-4505-2019.1.000387 SN - 2380-4505 SN - 1085-8024 VL - 2019 IS - 1 SP - 387 EP - 392 PB - IMAPS CY - Pittsburgh ER - TY - CHAP A1 - Bhogaraju, Sri Krishna A1 - Mokhtari, Omid A1 - Pascucci, Jacopo A1 - Conti, Fosca A1 - Elger, Gordon T1 - Improved sinterability of particles to substrates by surface modifications on substrate metallization T2 - Additional Conferences (Device Packaging, HiTEC, HiTEN, and CICMT) UR - https://doi.org/10.4071/2380-4491.2019.HiTen.000066 Y1 - 2019 UR - https://doi.org/10.4071/2380-4491.2019.HiTen.000066 SN - 2380-4491 VL - 2019 IS - HiTen SP - 66 EP - 70 PB - IMAPS CY - Pittsburgh (PA) ER - TY - CHAP A1 - Signorini, Raffaella A1 - Conti, Fosca A1 - Brugnolotto, Enrico A1 - Pedron, Danilo A1 - Liu, E A1 - Bhogaraju, Sri Krishna A1 - Elger, Gordon ED - Cheben, Pavel ED - Čtyroký, Jiří ED - Molina-Fernández, Iñigo T1 - Thermomechanical local stress in assembled GaN LEDs investigated by Raman optical spectroscopy T2 - Integrated Optics: Design, Devices, Systems, and Applications V UR - https://doi.org/10.1117/12.2520992 Y1 - 2019 UR - https://doi.org/10.1117/12.2520992 SN - 978-1-5106-2729-1 N1 - Presentation + Paper PB - SPIE CY - Bellingham ER - TY - CHAP A1 - Schmid, Maximilian A1 - Hanss, Alexander A1 - Bhogaraju, Sri Krishna A1 - Elger, Gordon T1 - Time saving averaging algorithm for transient thermal analyses over deterministic pulse superposition T2 - 2019 25th International Workshop on Thermal Investigations of ICs and Systems (THERMINIC) UR - https://doi.org/10.1109/THERMINIC.2019.8923548 Y1 - 2019 UR - https://doi.org/10.1109/THERMINIC.2019.8923548 SN - 978-1-7281-2078-2 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Brugnolotto, Enrico A1 - Bhogaraju, Sri Krishna A1 - Liu, E A1 - Conti, Fosca A1 - Pedron, Danilo A1 - Signorini, Raffaella A1 - Elger, Gordon T1 - Investigation of thermomechanical local stress induced in assembled GaN LEDs T2 - 2019 25th International Workshop on Thermal Investigations of ICs and Systems (THERMINIC) UR - https://doi.org/10.1109/THERMINIC.2019.8923629 Y1 - 2019 UR - https://doi.org/10.1109/THERMINIC.2019.8923629 SN - 978-1-7281-2078-2 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Schmid, Maximilian A1 - Bhogaraju, Sri Krishna A1 - Elger, Gordon T1 - Automatic transient thermal impedance tester for quality inspection of soldered and sintered power electronic devices on panel and tile level T2 - 2019 IEEE 69th Electronic Components and Technology Conference (ECTC) UR - https://doi.org/10.1109/ECTC.2019.00320 KW - reliability KW - thermal KW - silver sintering KW - MOSFET KW - measurment equipment Y1 - 2019 UR - https://doi.org/10.1109/ECTC.2019.00320 SN - 978-1-7281-1499-6 SP - 2324 EP - 2330 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Senel, Numan A1 - Udupa, Shrivatsa A1 - Elger, Gordon T1 - Sensor data preprocessing in road-side sensor units T2 - FISITA World Congress 2021 Y1 - 2021 UR - https://www.fisita.com/library/f2021-acm-120 N1 - Paper + Video PB - FISITA CY - Bishops Stortford ER - TY - CHAP A1 - Streck, Egor A1 - Schmok, Peter A1 - Schneider, Klaus A1 - Erdogan, Hüseyin A1 - Elger, Gordon T1 - Safeguarding future autonomous traffic by infrastructure based on multi radar sensor systems T2 - FISITA World Congress 2021 Y1 - 2021 UR - https://www.fisita.com/library/f2021-acm-121 N1 - Paper + Video PB - FISITA CY - Bishops Stortford ER - TY - CHAP A1 - Mohan, Nihesh A1 - Langer, Stefan A1 - Elger, Gordon T1 - Fluxfree solder paste and process for standard SMD components T2 - 2022 IEEE 9th Electronics System-Integration Technology Conference (ESTC) UR - https://doi.org/10.1109/ESTC55720.2022.9939528 KW - Fluxfree KW - Solder process KW - Surface mount devices Y1 - 2022 UR - https://doi.org/10.1109/ESTC55720.2022.9939528 SN - 978-1-6654-8947-8 SP - 163 EP - 168 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Meess, Henri A1 - Gerner, Jeremias A1 - Hein, Daniel A1 - Schmidtner, Stefanie A1 - Elger, Gordon ED - Kieu, Minh Le ED - Dam, Koen H. van ED - Thompson, Jason ED - Malleson, Nick ED - Heppenstall, Alison ED - Ge, Jiaqi T1 - Real World Traffic Optimization by Reinforcement Learning: A Concept T2 - International Workshop on Agent-Based Modelling of Urban Systems (ABMUS) Proceedings: 2022 UR - https://doi.org/10.6084/m9.figshare.19733800.v1 Y1 - 2022 UR - https://doi.org/10.6084/m9.figshare.19733800.v1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-29290 SP - 49 EP - 54 PB - figshare CY - [s. l.] ER - TY - CHAP A1 - Schmid, Maximilian A1 - Hermann, Joseph A1 - Liu, E A1 - Elger, Gordon T1 - Correlation of Scanning Acoustic Microscopy and Transient Thermal Analysis to Identify Crack Growth in Solder Joints T2 - Proceedings of the Twenty First InterSociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems: ITherm 2022 UR - https://doi.org/10.1109/iTherm54085.2022.9899664 KW - reliability KW - transient thermal analysis (TTA) KW - scanning acoustic microscopy (SAM) KW - finite element simulation KW - finite element optimization KW - LED KW - solder KW - crack Y1 - 2022 UR - https://doi.org/10.1109/iTherm54085.2022.9899664 SN - 978-1-6654-8503-6 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Zippelius, Andreas A1 - Strobl, Tobias A1 - Schmid, Maximilian A1 - Hermann, Joseph A1 - Hoffmann, Alwin A1 - Elger, Gordon T1 - Predicting thermal resistance of solder joints based on Scanning Acoustic Microscopy using Artificial Neural Networks T2 - 2022 IEEE 9th Electronics System-Integration Technology Conference (ESTC) UR - https://doi.org/10.1109/ESTC55720.2022.9939465 KW - Solder Joints KW - LED KW - non-destructive testing KW - Machine Learning KW - Scanning Acoustic Microscopy (SAM) KW - Transient Thermal Analysis (TTA), Convolutional Neural Network (CNN) Y1 - 2022 UR - https://doi.org/10.1109/ESTC55720.2022.9939465 SN - 978-1-6654-8947-8 SP - 566 EP - 575 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Schmid, Maximilian A1 - Hermann, Joseph A1 - Bhogaraju, Sri Krishna A1 - Elger, Gordon T1 - Reliability of SAC Solders under Low and High Stress Conditions T2 - 2022 IEEE 9th Electronics System-Integration Technology Conference (ESTC) UR - https://doi.org/10.1109/ESTC55720.2022.9939394 KW - reliability KW - solder joint cracking KW - SAC solder KW - transient thermal analysis KW - SAM Y1 - 2022 UR - https://doi.org/10.1109/ESTC55720.2022.9939394 SN - 978-1-6654-8947-8 SP - 553 EP - 559 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Liu, E A1 - Bhogaraju, Sri Krishna A1 - Lux, Kerstin A1 - Elger, Gordon A1 - Mou, Rokeya Mumtahana T1 - Investigation Of Stress Generated By Interconnection Processes With Micro-Raman Spectroscopy (μRS) T2 - Proceedings IEEE 72nd Electronic Components and Technology Conference: ECTC 2022 UR - https://doi.org/10.1109/ECTC51906.2022.00123 KW - component KW - μ-Raman Spectroscopy KW - Au80Sn20 KW - Cu sintering KW - finite element simultion Y1 - 2022 UR - https://doi.org/10.1109/ECTC51906.2022.00123 SN - 978-1-6654-7943-1 SP - 739 EP - 745 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Saccon, Rodolfo A1 - Benin, Alice A1 - Bhogaraju, Sri Krishna A1 - Elger, Gordon T1 - Effect of binders on the performance of copper sintering pastes T2 - 2022 International Conference on Electronics Packaging (ICEP 2022) UR - https://doi.org/10.23919/ICEP55381.2022.9795555 KW - copper sintering KW - binders KW - nanoparticles KW - reduction KW - alcohols, polyols Y1 - 2022 UR - https://doi.org/10.23919/ICEP55381.2022.9795555 SN - 978-4-9911911-3-8 SP - 71 EP - 72 PB - IEEE CY - Piscataway (NJ) ER - TY - CHAP A1 - Kleiner, Jan A1 - Heider, Alexander A1 - Komsiyska, Lidiya A1 - Elger, Gordon A1 - Endisch, Christian T1 - Experimental Study on the Thermal Interactions in Novel Intelligent Lithium-Ion Modules for Electric Vehicles T2 - Proceedings of the Twentieth InterSociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems: ITherm 2021 UR - https://doi.org/10.1109/ITherm51669.2021.9503299 KW - Intelligent battery KW - Lithium-ion KW - Reconfiguration KW - Cooling KW - Thermal management KW - Parallel connection Y1 - 2021 UR - https://doi.org/10.1109/ITherm51669.2021.9503299 SN - 978-1-7281-8539-2 SN - 2694-2135 SP - 556 EP - 562 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Senel, Numan A1 - Elger, Gordon A1 - Festag, Andreas T1 - Sensor Time Synchronization in Smart Road Infrastructure T2 - FISITA Web Congress 2020 Y1 - 2020 UR - https://www.fisita.com/library/f2020-acm-083 PB - FISITA CY - Bishops Stortford ER - TY - JOUR A1 - Zippelius, Andreas A1 - Hanss, Alexander A1 - Schmid, Maximilian A1 - Pérez-Velázquez, Judith A1 - Elger, Gordon T1 - Reliability analysis and condition monitoring of SAC+ solder joints under high thermomechanical stress conditions using neuronal networks JF - Microelectronics Reliability N2 - 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. UR - https://doi.org/10.1016/j.microrel.2021.114461 KW - Artificial neural networks KW - LSTM KW - Prediction KW - Reliability KW - Solder joints KW - Transient thermal analysis Y1 - 2022 UR - https://doi.org/10.1016/j.microrel.2021.114461 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-19719 SN - 0026-2714 VL - 2022 IS - 129 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Hanss, Alexander A1 - Schmid, Maximilian A1 - Bhogaraju, Sri Krishna A1 - Conti, Fosca A1 - Elger, Gordon T1 - Reliability of sintered and soldered high power chip size packages and flip chip LEDs T2 - ECTC - The 2018 IEEE 68th Electronic Components and Technology Conference: Proceedings UR - https://doi.org/10.1109/ECTC.2018.00312 Y1 - 2018 UR - https://doi.org/10.1109/ECTC.2018.00312 SN - 978-1-5386-5000-4 SP - 2080 EP - 2088 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Bhogaraju, Sri Krishna A1 - Mokhtari, Omid A1 - Pascucci, Jacopo A1 - Hanss, Alexander A1 - Schmid, Maximilian A1 - Conti, Fosca A1 - Elger, Gordon T1 - Hybrid Cu particle paste with surface-modified particles for high temperature electronics packaging T2 - Proceedings 22nd European Microelectronics and Packaging Conference, EMPC UR - https://doi.org/10.23919/EMPC44848.2019.8951887 Y1 - 2020 UR - https://doi.org/10.23919/EMPC44848.2019.8951887 SN - 978-1-7281-6291-1 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Hanss, Alexander A1 - Schmid, Maximilian A1 - Liu, E A1 - Elger, Gordon T1 - Transient thermal analysis as measurement method for IC package structural integrity JF - Chinese Physics B UR - https://doi.org/10.1088/1674-1056/24/6/068105 Y1 - 2015 UR - https://doi.org/10.1088/1674-1056/24/6/068105 SN - 2058-3834 SN - 1674-1056 VL - 24 IS - 6 PB - IOP Publishing CY - Bristol ER - TY - JOUR A1 - Hanss, Alexander A1 - Elger, Gordon T1 - Residual free solder process for fluxless solder pastes JF - Soldering & Surface Mount Technology UR - https://doi.org/10.1108/SSMT-10-2017-0030 KW - Reflow soldering KW - LED KW - Fluxless KW - Solder process Y1 - 2018 UR - https://doi.org/10.1108/SSMT-10-2017-0030 SN - 0954-0911 VL - 30 IS - 2 SP - 118 EP - 128 PB - Emerald CY - Bingley ER - TY - CHAP A1 - Hanss, Alexander A1 - Schmid, Maximilian A1 - Bhogaraju, Sri Krishna A1 - Conti, Fosca A1 - Elger, Gordon T1 - Process development and reliability of sintered high power chip size packages and flip chip LEDs T2 - 2018 International Conference on Electronics Packaging and iMAPS All Asia Conference (ICEP-IAAC) UR - https://doi.org/10.23919/ICEP.2018.8374351 KW - LED KW - Reliability KW - Sintering KW - Transient Thermal Testing Y1 - 2018 UR - https://doi.org/10.23919/ICEP.2018.8374351 SN - 978-4-9902-1885-0 SP - 479 EP - 484 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Bhogaraju, Sri Krishna A1 - Hanss, Alexander A1 - Schmid, Maximilian A1 - Elger, Gordon A1 - Conti, Fosca T1 - Evaluation of silver and copper sintering of first level interconnects for high power LEDs T2 - 2018 7th Electronic System-Integration Technology Conference (ESTC) UR - https://doi.org/10.1109/ESTC.2018.8546499 KW - transient thermal analysis KW - pressureless silver sintering KW - pressureless copper sintering KW - bonding atmosphere KW - particle size KW - binder chemistry Y1 - 2018 UR - https://doi.org/10.1109/ESTC.2018.8546499 SN - 978-1-5386-6814-6 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Schmid, Maximilian A1 - Elger, Gordon T1 - Measurement of the transient thermal impedance of MOSFETs over the sensitivity of the threshold voltage T2 - EPE'18 ECCE Europe KW - Reliability KW - MOSFET KW - Thermal stress KW - Thermal design KW - Measurement Y1 - 2018 UR - https://ieeexplore.ieee.org/document/8515480 SN - 978-9-0758-1528-3 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Elger, Gordon A1 - Hanss, Alexander A1 - Schmid, Maximilian T1 - Transient Thermal Analysis as In-Situ Method in Accelerated Stress Tests to Access Package Integrity of LEDs T2 - 2018 24rd International Workshop on Thermal Investigations of ICs and Systems (THERMINIC) UR - https://doi.org/10.1109/THERMINIC.2018.8593278 KW - Temperature measurement KW - Conferences KW - Integrated circuits KW - Light emitting diodes KW - Transient analysis KW - Temperature dependence KW - Thermal analysis Y1 - 2018 UR - https://doi.org/10.1109/THERMINIC.2018.8593278 SN - 978-1-5386-6759-0 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Conti, Fosca A1 - Hanss, Alexander A1 - Mokhtari, Omid A1 - Bhogaraju, Sri Krishna A1 - Elger, Gordon T1 - Formation of tin-based crystals from a SnAgCu alloy under formic acid vapor JF - New Journal of Chemistry UR - https://doi.org/10.1039/C8NJ04173C Y1 - 2018 UR - https://doi.org/10.1039/C8NJ04173C SN - 1369-9261 VL - 42 IS - 23 SP - 19232 EP - 19236 PB - RSC CY - London ER - TY - CHAP A1 - Hanss, Alexander A1 - Schmid, Maximilian A1 - Elger, Gordon T1 - Combined Accelerated Stress Test with In-Situ Thermal Impedance Monitoring to Access LED Reliability T2 - Proceedings 2018 20th International Conference on Electronic Materials and Packaging (EMAP) UR - https://doi.org/10.1109/EMAP.2018.8660833 KW - Temperature measurement KW - Light emitting diodes KW - Temperature KW - Electronic packaging thermal management KW - Temperature sensors KW - Transient analysis KW - Sensitivity Y1 - 2019 UR - https://doi.org/10.1109/EMAP.2018.8660833 SN - 978-1-5386-5642-6 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Elger, Gordon A1 - Biberger, M. A1 - Meier, M. A1 - Schweigart, Helmut A1 - Schneider, Klaus A1 - Erdogan, Hüseyin T1 - Technische Sauberkeit von Radarbaugruppen T2 - Elektronische Baugruppen und Leiterplatten EBL 2018: Multifunktionale Aufbau- und Verbindungstechnik – Beherrschung der Vielfalt Y1 - 2018 UR - https://www.dvs-media.eu/de/buecher/dvs-berichte/3644/elektronische-baugruppen-und-leiterplatten-ebl-2018 SN - 978-3-96144-026-9 SP - 339 EP - 349 PB - DVS Media GmbH CY - Düsseldorf ER - TY - CHAP A1 - Bhogaraju, Sri Krishna A1 - Schmid, Maximilian A1 - Liu, E A1 - Saccon, Rodolfo A1 - Elger, Gordon A1 - Klassen, Holger A1 - Müller, Klaus A1 - Pirzer, Georg T1 - Low cost copper based sintered interconnect material for optoelectronics packaging T2 - 2022 IEEE 72nd Electronic Components and Technology Conference (ECTC) UR - https://doi.org/10.1109/ECTC51906.2022.00270 KW - Cu sintering KW - flakes KW - PEG600 KW - reliability KW - encapsulation KW - oxidation KW - transient thermal analysis KW - Scanning acoustic microscopy KW - μ-Raman spectroscopy Y1 - 2022 UR - https://doi.org/10.1109/ECTC51906.2022.00270 SN - 978-1-6654-7943-1 SP - 1720 EP - 1725 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Signorini, Raffaella A1 - Pedron, Danilo A1 - Conti, Fosca A1 - Hanss, Alexander A1 - Bhogaraju, Sri Krishna A1 - Elger, Gordon T1 - Thermomechanical Stress in GaN LED Soldered on Copper Substrate Evaluated by Raman Measurements and Computer Modelling T2 - 2018 24rd International Workshop on Thermal Investigations of ICs and Systems (THERMINIC) UR - https://doi.org/10.1109/THERMINIC.2018.8593304 KW - Stress KW - Light emitting diodes KW - Gallium nitride KW - Phonons KW - Substrates KW - Temperature measurement KW - Silicon Y1 - 2018 UR - https://doi.org/10.1109/THERMINIC.2018.8593304 SN - 978-1-5386-6759-0 PB - IEEE CY - Piscataway ER - 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 - TY - JOUR A1 - Kettelgerdes, Marcel A1 - Sarmiento, Nicolas A1 - Erdogan, Hüseyin A1 - Wunderle, Bernhard A1 - Elger, Gordon T1 - Precise Adverse Weather Characterization by Deep-Learning-Based Noise Processing in Automotive LiDAR Sensors JF - Remote Sensing N2 - 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. UR - https://doi.org/10.3390/rs16132407 Y1 - 2024 UR - https://doi.org/10.3390/rs16132407 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-49599 SN - 2072-4292 VL - 16 IS - 13 PB - MDPI CY - Basel ER - TY - JOUR A1 - Mohan, Nihesh A1 - Ahuir-Torres, Juan Ignacio A1 - Bhogaraju, Sri Krishna A1 - Webler, Ralf A1 - Kotadia, Hiren R. A1 - Erdogan, Hüseyin A1 - Elger, Gordon T1 - Decomposition mechanism and morphological evolution of in situ realized Cu nanoparticles in Cu complex inks JF - New Journal of Chemistry UR - https://doi.org/10.1039/D3NJ05185D Y1 - 2024 UR - https://doi.org/10.1039/D3NJ05185D SN - 1369-9261 VL - 48 IS - 15 SP - 6796 EP - 6808 PB - RSC CY - London ER - TY - CHAP A1 - Mohan, Nihesh A1 - Ahuir-Torres, Juan Ignacio A1 - Bhogaraju, Sri Krishna A1 - Kotadia, Hiren R. A1 - Elger, Gordon T1 - Rapid Sintering of Inkjet Printed Cu Complex Inks Using Laser in Air T2 - 2023 24th European Microelectronics and Packaging Conference & Exhibition (EMPC) UR - https://doi.org/10.23919/EMPC55870.2023.10418323 Y1 - 2024 UR - https://doi.org/10.23919/EMPC55870.2023.10418323 SN - 978-0-9568086-9-1 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Bhogaraju, Sri Krishna A1 - Ugolini, Francesco A1 - Belponer, Federico A1 - Greci, Alessio A1 - Elger, Gordon T1 - Reliability of Copper Sintered Interconnects Under Extreme Thermal Shock Conditions T2 - 2023 24th European Microelectronics and Packaging Conference & Exhibition (EMPC) UR - https://doi.org/10.23919/EMPC55870.2023.10418348 Y1 - 2024 UR - https://doi.org/10.23919/EMPC55870.2023.10418348 SN - 978-0-9568086-9-1 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Bhogaraju, Sri Krishna A1 - Mohan, Nihesh A1 - Steinberger, Fabian A1 - Erdogan, Hüseyin A1 - Hadrava, Philipp A1 - Elger, Gordon T1 - Novel Low Temperature and Low Pressure Sintering of ADAS Radar Sensor Antenna Stack T2 - 2023 24th European Microelectronics and Packaging Conference & Exhibition (EMPC) UR - https://doi.org/10.23919/EMPC55870.2023.10418277 Y1 - 2024 UR - https://doi.org/10.23919/EMPC55870.2023.10418277 SN - 978-0-9568086-9-1 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Ahuir-Torres, Juan Ignacio A1 - Bhogaraju, Sri Krishna A1 - West, Geoff A1 - Elger, Gordon A1 - Kotadia, Hiren R. T1 - Understanding Cu Sintering and Its Role on Corrosion Behaviour for High-Temperature Microelectronic Application T2 - 2023 24th European Microelectronics and Packaging Conference & Exhibition (EMPC) UR - https://doi.org/10.23919/EMPC55870.2023.10418365 Y1 - 2024 UR - https://doi.org/10.23919/EMPC55870.2023.10418365 SN - 978-0-9568086-9-1 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Elger, Gordon A1 - Bhogaraju, Sri Krishna A1 - Schneider-Ramelow, Martin T1 - Hybrid Cu sinter paste for low temperature bonding of bare semiconductors JF - Materials Letters N2 - A novel hybrid copper paste was developed for low temperature sintering of bare semiconductors. Cu(II) formate (Cu(for)) is complexed in amino-2-propanol (A2P) and added to a paste of etched brass micro flakes. A two-step sintering process is applied: The paste is printed and dried at 120 °C under formic acid (FA) enriched N2 atmosphere (FAN2) for 5 min. Afterwards, bare semiconductors are placed and sintered at 250 °C for 5 min applying a bonding pressure of 20 MPa/10 MPa. By the thermal decomposition of the Cu(for) atomic Cu is released and forms in-situ Cu-nanoparticles. An interconnect is realized with shear strength >100 MPa. UR - https://doi.org/10.1016/j.matlet.2024.136973 Y1 - 2024 UR - https://doi.org/10.1016/j.matlet.2024.136973 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-49138 SN - 1873-4979 VL - 2024 IS - 372 PB - Elsevier CY - New York ER - TY - CHAP A1 - Han, Longfei A1 - Kefferpütz, Klaus A1 - Elger, Gordon A1 - Beyerer, Jürgen T1 - FlexSense: Flexible Infrastructure Sensors for Traffic Perception T2 - 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC57777.2023.10422616 Y1 - 2024 UR - https://doi.org/10.1109/ITSC57777.2023.10422616 SN - 979-8-3503-9946-2 SP - 3810 EP - 3816 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Han, Longfei A1 - Xu, Qiuyu A1 - Kefferpütz, Klaus A1 - Lu, Ying A1 - Elger, Gordon A1 - Beyerer, Jürgen T1 - Scalable Radar-based Roadside Perception: Self-localization and Occupancy Heat Map for Traffic Analysis T2 - 2024 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/iv55156.2024.10588397 Y1 - 2024 UR - https://doi.org/10.1109/iv55156.2024.10588397 SN - 979-8-3503-4881-1 SP - 1651 EP - 1657 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Olcay, Ertug A1 - Meeß, Henri A1 - Elger, Gordon T1 - Dynamic Obstacle Avoidance for UAVs using MPC and GP-Based Motion Forecast T2 - 2024 European Control Conference (ECC) UR - https://doi.org/10.23919/ECC64448.2024.10591083 Y1 - 2024 UR - https://doi.org/10.23919/ECC64448.2024.10591083 SN - 978-3-9071-4410-7 SP - 1024 EP - 1031 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Paret, Paul A1 - Bhogaraju, Sri Krishna A1 - Busse, Dirk A1 - Dahlbüdding, Alexander A1 - Elger, Gordon A1 - Narumanchi, Sreekant T1 - Thermomechanical Degradation of Sintered Copper under High-Temperature Thermal Shock T2 - Proceedings: IEEE 74th Electronic Components and Technology Conference, ECTC 2024 UR - https://doi.org/10.1109/ECTC51529.2024.00196 Y1 - 2024 UR - https://doi.org/10.1109/ECTC51529.2024.00196 SN - 979-8-3503-7598-5 SP - 1219 EP - 1224 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Steinberger, Fabian A1 - Mohan, Nihesh A1 - Rämer, Olaf A1 - Elger, Gordon T1 - Low temperature die-attach bonding using copper particle free inks T2 - 2024 IEEE 10th Electronics System-Integration Technology Conference (ESTC), Proceedings UR - https://doi.org/10.1109/ESTC60143.2024.10712150 Y1 - 2024 UR - https://doi.org/10.1109/ESTC60143.2024.10712150 SN - 979-8-3503-9036-0 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Agrawal, Shiva A1 - Song, Rui A1 - Kohli, Akhil A1 - Korb, Andreas A1 - Andre, Maximilian A1 - Holzinger, Erik A1 - Elger, Gordon ED - Ploeg, Jeroen ED - Helfert, Markus ED - Berns, Karsten ED - Gusikhin, Oleg T1 - Concept of Smart Infrastructure for Connected Vehicle Assist and Traffic Flow Optimization T2 - Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems N2 - The smart infrastructure units can play a vital role to develop smart cities of the future and in assisting automated vehicles on the road by providing extended perception and timely warnings to avoid accidents. This paper focuses on the development of such an infrastructure unit, that is specifically designed for a pedestrian crossing junction. It can control traffic lights at the junction by real-time environment perception through its sensors and can optimize the flow of vehicles and passing vulnerable road users (VRUs). Moreover, it can assist on-road vehicles by providing real-time information and critical warnings via a v2x module. This paper further describes different use-cases of the work, all major hardware components involved in the development of smart infrastructure unit, referred to as an edge, different sensor fusion approaches using the camera, radar, and lidar mounted on the edge for environment perception, various modes of communication including v2x, system design UR - https://doi.org/10.5220/0011068800003191 KW - Road Side Unit (RSU) KW - Smart Infrastructure KW - Radar KW - LiDAR KW - Camera KW - Perception KW - Sensor Data Fusion KW - Vehicle-to-everything (V2X) KW - Communication KW - Simulation KW - ITS Y1 - 2022 UR - https://doi.org/10.5220/0011068800003191 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-30926 SN - 978-989-758-573-9 SN - 2184-495X SP - 360 EP - 367 PB - SciTePress CY - Setúbal ER - TY - CHAP A1 - Streck, Egor A1 - Herschel, Reinhold A1 - Wallrath, Patrick A1 - Sunderam, M. A1 - Elger, Gordon ED - Prasad, Venkatesha ED - Pesch, Dirk ED - Ansari, Nirwan ED - Benavente-Peces, César T1 - Comparison of Two Different Radar Concepts for Pedestrian Protection on Bus Stops T2 - Proceedings of the 11th International Conference on Sensor Networks N2 - This paper presents the joint work from the “HORIS” project, with a focus on pedestrian detection at bus-stops by radar sensors mounted in the infrastructure to support future autonomous driving and protecting pedestrians in critical situations. Two sensor systems are investigated and evaluated. The first based on single radar sensor phase-sensitive raw data analysis and the second based on sensor data fusion of cluster data with two radar sensors using neural networks to predict the position of pedestrians. UR - https://doi.org/10.5220/0010777100003118 KW - Sensor Data Fusion KW - Radar Sensor KW - Multiple-Sensor Systems KW - Machine Learning Y1 - 2022 UR - https://doi.org/10.5220/0010777100003118 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-30953 SN - 978-989-758-551-7 SN - 2184-4380 SP - 89 EP - 96 PB - SciTePress CY - Setúbal ER - TY - CHAP A1 - Conti, Fosca A1 - Lux, Kerstin A1 - Bhogaraju, Sri Krishna A1 - Liu, E A1 - Lenz, Christoph A1 - Seitz, Roland A1 - Elger, Gordon ED - Baldini, Francesco ED - Homola, Jiri ED - Lieberman, Robert A. T1 - Raman spectroscopy to investigate gallium nitride light emitting diodes after assembling onto copper substrates T2 - Optical Sensors 2021 UR - https://doi.org/10.1117/12.2591947 Y1 - 2021 UR - https://doi.org/10.1117/12.2591947 SN - 978-1-5106-4379-6 N1 - Poster + Paper PB - SPIE CY - Bellingham ER - TY - JOUR A1 - Bhanderi, Savankumar A1 - Agrawal, Shiva A1 - Elger, Gordon T1 - Deep segmentation of 3+1D radar point cloud for real-time roadside traffic user detection JF - Scientific Reports N2 - Smart cities rely on intelligent infrastructure to enhance road safety, optimize traffic flow, and enable vehicle-to-infrastructure (V2I) communication. A key component of such infrastructure is an efficient and real-time perception system that accurately detects diverse traffic participants. Among various sensing modalities, automotive radar is one of the best choices due to its robust performance in adverse weather and low-light conditions. However, due to low spatial resolution, traditional clustering-based approaches for radar object detection often struggle with vulnerable road user detection and nearby object separation. Hence, this paper proposes a deep learning-based D radar point cloud clustering methodology tailored for smart infrastructure-based perception applications. This approach first performs semantic segmentation of the radar point cloud, followed by instance segmentation to generate well-formed clusters with class labels using a deep neural network. It also detects single-point objects that conventional methods often miss. The described approach is developed and experimented using a smart infrastructure-based sensor setup and it performs segmentation of the point cloud in real-time. Experimental results demonstrate 95.35% F1-macro score for semantic segmentation and 91.03% mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5 for instance segmentation. Further, the complete pipeline operates at 43.61 frames per second with a memory requirement of less than 0.7 MB on the edge device (Nvidia Jetson AGX Orin). UR - https://doi.org/10.1038/s41598-025-23019-6 Y1 - 2025 UR - https://doi.org/10.1038/s41598-025-23019-6 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63458 SN - 2045-2322 VL - 15 PB - Springer Nature CY - London ER - TY - INPR A1 - Bhanderi, Savankumar A1 - Agrawal, Shiva A1 - Elger, Gordon T1 - Deep Segmentation of 3+1D Radar Point Cloud for Real-Time Roadside Traffic User Detection T2 - Research Square N2 - Smart cities rely on intelligent infrastructure to enhance road safety, optimize traffic flow, and enable vehicle-to-infrastructure (V2I) communication. A key component of such infrastructure is an efficient and real-time perception system that accurately detects diverse traffic participants. Among various sensing modalities, automotive radar is one of the best choices due to its robust performance in adverse weather and low-light conditions. However, due to low spatial resolution, traditional clustering-based approaches for radar object detection often struggle with vulnerable road user detection and nearby object separation. Hence, this paper proposes a deep learning-based 3+1D radar point cloud clustering methodology tailored for smart infrastructure-based perception applications. This approach first performs semantic segmentation of the radar point cloud, followed by instance segmentation to generate well-formed clusters with class labels using a deep neural network. It also detects single-point objects that conventional methods often miss. The described approach is developed and experimented using a smart infrastructure-based sensor setup and it performs segmentation of the point cloud in real-time. Experimental results demonstrate 95.35% F1-macro score for semantic segmentation and 91.03% mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5 for instance segmentation. Further, the complete pipeline operates at 43.61 frames per second with a memory requirement of less than 0.7 MB on the edge device (Nvidia Jetson AGX Orin). UR - https://doi.org/10.21203/rs.3.rs-7222130/v1 Y1 - 2025 UR - https://doi.org/10.21203/rs.3.rs-7222130/v1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61996 SN - 2693-5015 PB - Research Square CY - Durham ER - TY - JOUR A1 - Kleiner, Jan A1 - Heider, Alexander A1 - Komsiyska, Lidiya A1 - Elger, Gordon A1 - Endisch, Christian T1 - Thermal behavior of intelligent automotive lithium-ion batteries: Experimental study with switchable cells and reconfigurable modules JF - Journal of Energy Storage UR - https://doi.org/10.1016/j.est.2021.103274 KW - lithium-ion KW - intelligent battery KW - switchable cell KW - reconfiguration KW - smart cell KW - thermal management Y1 - 2021 UR - https://doi.org/10.1016/j.est.2021.103274 SN - 2352-1538 VL - 2021 IS - 44, Part A PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Kleiner, Jan A1 - Lechermann, Lorenz A1 - Komsiyska, Lidiya A1 - Elger, Gordon A1 - Endisch, Christian T1 - Thermal behavior of intelligent automotive lithium-ion batteries BT - operating strategies for adaptive thermal balancing by reconfiguration JF - Journal of energy storage UR - https://doi.org/10.1016/j.est.2021.102686 KW - intelligent battery KW - thermal management KW - balancing KW - cell-to-cell variations KW - inhomogeneities KW - thermal modeling Y1 - 2021 UR - https://doi.org/10.1016/j.est.2021.102686 SN - 2352-1538 VL - 2021 IS - 40 PB - Elsevier CY - Amsterdam ER -