Ion beam analysis (IBA) is an established tool for material characterization, providing precise information on elemental composition, depth profiles, and structural information in the region near the surface of materials. However, traditional data processing methods can be slow and computationally intensive, limiting the efficiency and speed of the analysis. This article explores the current landscape of applying machine learning algorithms (MLAs) in the field of IBA, demonstrating the immense potential to optimize and accelerate processes. We present how ML has been employed to extract valuable insights from large datasets, automate repetitive tasks, and enhance the interpretability of results, with practical examples of applications in various IBA techniques, such as RBS, PIXE, and others. Finally, perspectives on using MLA to approach open problems in IBA are also discussed.
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March 2025
Review Article|
February 19 2025
Applications of machine learning in ion beam analysis of materials Available to Purchase
Tiago Fiorini da Silva
Tiago Fiorini da Silva
a)
(Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing)
Instituto de Física da Universidade de São Paulo Rua do matão
, trav. R 187, São Paulo 05508-090, Brazil
a)Author to whom correspondence should be addressed: [email protected]
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Tiago Fiorini da Silva
a)
Instituto de Física da Universidade de São Paulo Rua do matão
, trav. R 187, São Paulo 05508-090, Brazil
a)Author to whom correspondence should be addressed: [email protected]
J. Vac. Sci. Technol. A 43, 020803 (2025)
Article history
Received:
December 06 2024
Accepted:
January 29 2025
Citation
Tiago Fiorini da Silva; Applications of machine learning in ion beam analysis of materials. J. Vac. Sci. Technol. A 1 March 2025; 43 (2): 020803. https://doi.org/10.1116/6.0004277
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