Group III nitrides are promising materials for light emitting diodes (LEDs). The occurrence of structural defects strongly affects the efficiency of these LEDs. We investigate the optical properties of basal plane stacking faults (BFSs), and the assignment of specific spectral features to distinct defect types by direct correlation of localized emission bands measured by cathodoluminescence in a scanning electron microscope with defects found in high resolution (scanning) transmission electron microscopy and electron beam induced current at identical sample spots. Thus, we are able to model the electronic structure of BSFs addressing I-1, I-2, and E type BSFs in GaN and AlGaN with low Al content. We find hints that BSFs in semipolar AlGaN layers cause local changes of the Al content, which strongly affects the usability of AlGaN as an electron blocking layer in nitride based LEDs.
This paper provides an approach for controlling the level of risk when operating highly automated transportation systems like cars, trains and similar. Such systems replace human perception and decision-making by employing highly sophisticated solutions based on electronics, IT, and AI. Such systems have demonstrated the potential for building highly automated vehicles, but, as of today, encounter challenges in correctly understanding the extremely complex open contexts, into which such vehicles could be deployed. Its key focus is on bounding the risks stemming from uncertainty in the perception of the environment.