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Deep learning for laser beam imprinting

2023; Optica Publishing Group; Volume: 31; Issue: 12 Linguagem: Inglês

10.1364/oe.481776

1094-4087

J. Chalupský, Vojtěch Vozda, Julian Hering, Jan Kybic, T. Burian, Siarhei Dziarzhytski, Kateřina Frantálová, V. Hájková, S Jelinek, L. Juha, Barbara Keitel, Zuzana Kuglerová, M. Kuhlmann, B. Petryshak, Mabel Ruiz-Lopez, L. Vyšín, Thomas Wodzinski, Elke Plönjes,

Integrated Circuits and Semiconductor Failure Analysis

Methods of ablation imprints in solid targets are widely used to characterize focused X-ray laser beams due to a remarkable dynamic range and resolving power. A detailed description of intense beam profiles is especially important in high-energy-density physics aiming at nonlinear phenomena. Complex interaction experiments require an enormous number of imprints to be created under all desired conditions making the analysis demanding and requiring a huge amount of human work. Here, for the first time, we present ablation imprinting methods assisted by deep learning approaches. Employing a multi-layer convolutional neural network (U-Net) trained on thousands of manually annotated ablation imprints in poly(methyl methacrylate), we characterize a focused beam of beamline FL24/FLASH2 at the Free-electron laser in Hamburg. The performance of the neural network is subject to a thorough benchmark test and comparison with experienced human analysts. Methods presented in this Paper pave the way towards a virtual analyst automatically processing experimental data from start to end.