TY - CHAP A1 - Pomplun, Jan A1 - Wenzel, Hans A1 - Burger, Sven A1 - Zschiedrich, Lin A1 - Rozova, Maria A1 - Schmidt, Frank A1 - Crump, Paul A1 - Ekhteraei, Hossein A1 - Schultz, Christoph M. A1 - Erbert, Götz T1 - Thermo-optical simulation of high-power diode lasers T2 - Proc. SPIE Y1 - 2012 U6 - https://doi.org/10.1117/12.909330 VL - 8255 SP - 825510 ER - TY - CHAP A1 - Wenzel, Hans A1 - Crump, Paul A1 - Ekhteraei, Hossein A1 - Schultz, Christoph M. A1 - Pomplun, Jan A1 - Burger, Sven A1 - Zschiedrich, Lin A1 - Schmidt, Frank A1 - Erbert, Götz T1 - Theoretical and experimental analysis of the lateral modes of high-power broad-area lasers T2 - Numerical Simulation of Optoelectronic Devices Y1 - 2011 U6 - https://doi.org/10.1109/NUSOD.2011.6041183 SP - 143 EP - 144 ER - TY - CHAP A1 - Zink, Christof A1 - Ekterai, Michael A1 - Martin, Dominik A1 - Clemens, William A1 - Maennel, Angela A1 - Mundinger, Konrad A1 - Richter, Lorenz A1 - Crump, Paul A1 - Knigge, Andrea T1 - Deep-learning-based visual inspection of facets and p-sides for efficient quality control of diode lasers T2 - High-Power Diode Laser Technology XXI N2 - The optical inspection of the surfaces of diode lasers, especially the p-sides and facets, is an essential part of the quality control in the laser fabrication procedure. With reliable, fast, and flexible optical inspection processes, it is possible to identify and eliminate defects, accelerate device selection, reduce production costs, and shorten the cycle time for product development. Due to a vast range of rapidly changing designs, structures, and coatings, however, it is impossible to realize a practical inspection with conventional software. In this work, we therefore suggest a deep learning based defect detection algorithm that builds on a Faster Regional Convolutional Neural Network (Faster R-CNN) as a core component. While for related, more general object detection problems, the application of such models is straightforward, it turns out that our task exhibits some additional challenges. On the one hand, a sophisticated pre- and postprocessing of the data has to be deployed to make the application of the deep learning model feasible. On the other hand, we find that creating labeled training data is not a trivial task in our scenario, and one has to be extra careful with model evaluation. We can demonstrate in multiple empirical assessments that our algorithm can detect defects in diode lasers accurately and reliably in most cases. We analyze the results of our production-ready pipeline in detail, discuss its limitations and provide some proposals for further improvements. Y1 - 2023 U6 - https://doi.org/10.1117/12.2648691 VL - 12403 SP - 94 EP - 112 PB - SPIE ER -