Presentation
6 June 2023 Analysis of ablation imprints accelerated by machine learning (Conference Presentation)
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Abstract
A proper spatial characterization of a laser beam profile is indisputably important for any laser-mater experiment as well as for protection of beamline optical elements. Method of ablation and desorption imprints provides thorough beam profile analysis applicable to a broad range of photon energies. This method, however, often requires up to thousands of shots which must be then manually analyzed. Here we present method based on deep learning image segmentation model which is able to substitute human element currently indispensable in this time-consuming ex situ post processing. It is a part of AbloCAM project – an universal device for semi-automatic beam profile analysis.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Vojtěch Vozda, Jaromír Chalupský, Jan Hering, Jan Kybic, Tomáš Burian, Katerina Frantálová, Věra Hájková, Šimon Jelínek, Libor Juha, Barbara Keitel, Zuzana Kuglerová, Marion Kuhlmann, Bohdan Petryshak, Mabel Ruiz-Lopez, Ludek Vyšín, Thomas A. Wodzinski, and Elke Plönjes "Analysis of ablation imprints accelerated by machine learning (Conference Presentation)", Proc. SPIE 12578, Optics Damage and Materials Processing by EUV/X-ray Radiation (XDam8), 125780K (6 June 2023); https://doi.org/10.1117/12.2670421
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KEYWORDS
Laser ablation

Machine learning

Beam analyzers

Image processing

Deep learning

Image segmentation

Optical components

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