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  4. Enhancing 3D reconstruction accuracy of FIB tomography data using multi-voltage images and multimodal machine learning
 
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Enhancing 3D reconstruction accuracy of FIB tomography data using multi-voltage images and multimodal machine learning

Citation Link: https://doi.org/10.15480/882.9482
Publikationstyp
Journal Article
Date Issued
2024-12-01
Sprache
English
Author(s)
Sardhara, Trushal 
Kontinuums- und Werkstoffmechanik M-15  
Shkurmanov, Alexander  
Betriebseinheit Elektronenmikroskopie M-26  
Li, Yong  orcid-logo
Werkstoffphysik und -technologie M-22  
Riedel, Lukas 
Werkstoffphysik und -technologie M-22  
Shi, Shan  
Integrated metallic Nanomaterialssystems M-EXK4  
Cyron, Christian J.  
Kontinuums- und Werkstoffmechanik M-15  
Aydin, Roland 
Machine Learning in Virtual Materials Design M-EXK5  
Ritter, Martin  orcid-logo
Betriebseinheit Elektronenmikroskopie M-26  
TORE-DOI
10.15480/882.9482
TORE-URI
https://hdl.handle.net/11420/47167
Journal
Nanomanufacturing and metrology  
Volume
7
Issue
1
Article Number
4
Citation
Nanomanufacturing and Metrology 7 (1): 4 (2024)
Publisher DOI
10.1007/s41871-024-00223-y
Scopus ID
2-s2.0-85186115935
Publisher
Springer Singapore
Peer Reviewed
true
Is Supplemented By
10.15480/882.8927
FIB-SEM tomography is a powerful technique that integrates a focused ion beam (FIB) and a scanning electron microscope (SEM) to capture high-resolution imaging data of nanostructures. This approach involves collecting in-plane SEM images and using FIB to remove material layers for imaging subsequent planes, thereby producing image stacks. However, these image stacks in FIB-SEM tomography are subject to the shine-through effect, which makes structures visible from the posterior regions of the current plane. This artifact introduces an ambiguity between image intensity and structures in the current plane, making conventional segmentation methods such as thresholding or the k-means algorithm insufficient. In this study, we propose a multimodal machine learning approach that combines intensity information obtained at different electron beam accelerating voltages to improve the three-dimensional (3D) reconstruction of nanostructures. By treating the increased shine-through effect at higher accelerating voltages as a form of additional information, the proposed method significantly improves segmentation accuracy and leads to more precise 3D reconstructions for real FIB tomography data.
Subjects
3D reconstruction
FIB tomography
FIB-SEM
Multi-voltage images
Multimodal machine learning
Overdeterministic systems
MLE@TUHH
DDC Class
670: Manufacturing
Funding(s)
SFB 986: Teilprojekt C01 - Multiskalige photonische Materialien mit anpassbarer Absorption und thermischer Emission  
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by/4.0/
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