https://doi.org/10.1140/epjp/s13360-024-05924-3
Regular Article
Deep learning neural network designed large dimensional neutron focusing metalens
1
, 20418, Keelung, Taiwan
2
Department of Mechanical Engineer (AOS), FH Aachen - University of Applied Sciences, Jülich, Germany
Received:
10
August
2024
Accepted:
11
December
2024
Published online:
8
January
2025
Neutron focusing lenses are very important for various applications in fundamental science, industry, or even medicine. Conventional Fresnel zone plates (FZP) were successfully used as neutron focusing lenses. However, several difficulties are arisen when larger dimension (at the order of 10 cm) focal lenses are demanded for some applications. Here, we proposed a neuron network metalens design method based on D2NN algorithm. In this work, several neutron power focusing metalenses were designed for neutron energies from cold to epithermal, with size lengths from a few mm to 140 mm, and a focal length range from 60 mm to 13.5 m. Based on our simulations, clear focusing patterns were observed and were consistent with previous experiments. The estimation value of power focusing efficiency could be as high as 20%.
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjp/s13360-024-05924-3.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
