https://doi.org/10.1140/epjp/s13360-025-06221-3
Regular Article
Artificial intelligent focusing of a microbeam system based on reinforcement learning
1
Institute of Modern Physics, CAS, No.509, Nanchang Road, 730000, Lanzhou, Gansu, China
2
Lanzhou University, No.222, Tianshui Road, 730000, Lanzhou, Gansu, China
a
gh_du@impcas.ac.cn
b
lxj1899@impcas.ac.cn
Received:
30
October
2024
Accepted:
17
March
2025
Published online:
22
April
2025
Ion microbeam facility is a highly effective tool for precise sample irradiation, ion beam micro-modification, ion beam analysis, and other applications at micron and nanometer scale. However, achieving high-resolution beam spots requires meticulous adjustment of the microslit setting, beam transport and magnetic focusing field, which is even time-consuming for well-trained technicians. Nowadays, most of the beamline instruments and power supplies support remote control and automatic adjustment, which promotes the application of artificial intelligence to microbeam formation. In this work, we simulated the 50 MeV proton microbeam system with Oxford triplet lens configuration using a homemade ion optics package, which can generate data about any number of ions passing through quadrupole magnets. Then, an agent interacted with the system and generated large amounts of data. The data was used to train a deep Q-Network (DQN) model. Ultimately, we used the model to accomplish the intelligent focusing function on the simulated microbeam system. Comparative results show that the error between our model and the classic method is less than 0.3%.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025
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.
