Deep-learning-based visual inspection of facets and p-sides for efficient quality control of diode lasers

Christof Zink; Michael Ekterai; Dominik Martin; William Clemens; Angela Maennel; Konrad Mundinger; Lorenz Richter; Paul Crump; Andrea Knigge

doi:10.1117/12.2648691

14 March 2023 Deep-learning-based visual inspection of facets and p-sides for efficient quality control of diode lasers

Christof Zink, Michael Ekterai, Dominik Martin, William Clemens, Angela Maennel, Konrad Mundinger, Lorenz Richter, Paul Crump, Andrea Knigge

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Proceedings Volume 12403, High-Power Diode Laser Technology XXI; 124030E (2023) https://doi.org/10.1117/12.2648691
Event: SPIE LASE, 2023, San Francisco, California, United States

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.

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Christof Zink, Michael Ekterai, Dominik Martin, William Clemens, Angela Maennel, Konrad Mundinger, Lorenz Richter, Paul Crump, and Andrea Knigge "Deep-learning-based visual inspection of facets and p-sides for efficient quality control of diode lasers", Proc. SPIE 12403, High-Power Diode Laser Technology XXI, 124030E (14 March 2023); https://doi.org/10.1117/12.2648691

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KEYWORDS

Semiconductor lasers

Semiconducting wafers

Education and training

Defect detection

Data modeling

Object detection

Image processing

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