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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
Riedel, Lukas
TORE-DOI
Journal
Volume
7
Issue
1
Article Number
4
Citation
Nanomanufacturing and Metrology 7 (1): 4 (2024-12)
Publisher DOI
Scopus ID
Publisher
Springer Singapore
Peer Reviewed
true
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
Publication version
publishedVersion
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Name
s41871-024-00223-y-1.pdf
Type
Main Article
Size
3.28 MB
Format
Adobe PDF