A machine learning approach to self-consistent RBS data analysis and combined uncertainty evaluation-Reference-Cited by-同舟云学术

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A machine learning approach to self-consistent RBS data analysis and combined uncertainty evaluation

Published:2024-06 Issue: Volume:551 Page:165354
ISSN:0168-583X
Container-title:Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms
language:en
Short-container-title:Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms

Author:

Magchiels Goele^ORCID,Mtshali C.B.^ORCID,Kotsedi L.,Segola I.K.^ORCID,Vantomme A.^ORCID

Funder

National Research Foundation

European Commission

National Research Foundation of Korea

Research Foundation Flanders

Publisher

Elsevier BV

Reference31 articles.

1. J. W. Butler, O.F.R. Rfo, “Criteria for Validity Scatter,” vol. 45, pp. 160–165, 1990.

2. HZDR, “The RBS-Hedgehog at the HZDR Ion Beam Center,” 2024. https://hzdr-innovation.de/en/products/hissib/.

3. Ensemble RBS: Probing the compositional profile of 3D microscale structures;Claessens;Surfaces and Interfaces,2022

4. External RBS analysis setup at University of Göttingen: RBS analysis for liquid samples;Saito;Surf. Interface Anal.,2018

5. Unambiguous automatic evaluation of multiple Ion Beam Analysis data with Simulated Annealing;Barradas;Nucl. Instruments Methods Phys. Res. Sect. B Beam Interact. with Mater. Atoms,1999

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