@inproceedings{PomplunWenzelBurgeretal.2012, author = {Pomplun, Jan and Wenzel, Hans and Burger, Sven and Zschiedrich, Lin and Rozova, Maria and Schmidt, Frank and Crump, Paul and Ekhteraei, Hossein and Schultz, Christoph M. and Erbert, G{\"o}tz}, title = {Thermo-optical simulation of high-power diode lasers}, series = {Proc. SPIE}, volume = {8255}, booktitle = {Proc. SPIE}, doi = {10.1117/12.909330}, pages = {825510}, year = {2012}, language = {en} } @inproceedings{WenzelCrumpEkhteraeietal., author = {Wenzel, Hans and Crump, Paul and Ekhteraei, Hossein and Schultz, Christoph M. and Pomplun, Jan and Burger, Sven and Zschiedrich, Lin and Schmidt, Frank and Erbert, G{\"o}tz}, title = {Theoretical and experimental analysis of the lateral modes of high-power broad-area lasers}, series = {Numerical Simulation of Optoelectronic Devices}, booktitle = {Numerical Simulation of Optoelectronic Devices}, doi = {10.1109/NUSOD.2011.6041183}, pages = {143 -- 144}, language = {en} } @inproceedings{ZinkEkteraiMartinetal., author = {Zink, Christof and Ekterai, Michael and Martin, Dominik and Clemens, William and Maennel, Angela and Mundinger, Konrad and Richter, Lorenz and Crump, Paul and Knigge, Andrea}, title = {Deep-learning-based visual inspection of facets and p-sides for efficient quality control of diode lasers}, series = {High-Power Diode Laser Technology XXI}, volume = {12403}, booktitle = {High-Power Diode Laser Technology XXI}, publisher = {SPIE}, doi = {10.1117/12.2648691}, pages = {94 -- 112}, abstract = {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.}, language = {en} }