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Publisher DOI: 10.1007/s41871-022-00134-w
Title: Slice thickness optimization for the focused ion beam-scanning electron microscopy 3D tomography of hierarchical nanoporous gold
Language: English
Authors: Shkurmanov, Alexander 
Krekeler, Tobias 
Ritter, Martin  
Keywords: Focused ion beam; Scanning electron microscopy; Tomography; Hierarchical nanoporous gold
Issue Date: 2022
Publisher: Springer Singapore
Source: Nanomanufacturing and Metrology 5 (2): 112–118 (2022)
Abstract (english): 
The combination of focused ion beam (FIB) with scanning electron microscopy (SEM), also known as FIB-SEM tomog raphy, has become a powerful 3D imaging technique at the nanometer scale. This method uses an ion beam to mill away a thin slice of material, which is then block-face imaged using an electron beam. With consecutive slicing along the z-axis and subsequent imaging, a volume of interest can be reconstructed from the images and further analyzed. Hierarchical nanoporous gold (HNPG) exhibits unique structural properties and has a ligament size of 15–110 nm and pore size of 5–20 nm. Accurate reconstruction of its image is crucial in determining its mechanical and other properties. Slice thickness is one of the most critical and uncertain parameters in FIB-SEM tomography. For HNPG, the slice thickness should be at least half as thin as the pore size and, in our approach, should not exceed 10 nm. Variations in slice thickness are caused by various microscope and sample parameters, e.g., converged ion milling beam shape, charging efects, beam drift, or sample surface roughness.
Determining and optimizing the actual slice thickness variation appear challenging. In this work, we examine the infuence of ion beam scan resolution and the dwell time on the mean and standard deviation of slice thickness. After optimizing the resolution and dwell time to achieve the target slice thickness and lowest possible standard deviation, we apply these parameters to analyze an actual HNPG sample. Our approach can determine the thickness of each slice along the z-axis and estimate the deviation of the milling process along the y-axis (slow imaging axis). For this function, we create a multi-ruler structure integrated with the HNPG sample.
DOI: 10.15480/882.4272
ISSN: 2520-8128
Journal: Nanomanufacturing and metrology 
Institute: Betriebseinheit Elektronenmikroskopie M-26 
Document Type: Article
Project: SFB 986: Teilprojekt B09 - Mikrostrukturbasierte Klassifizierung und mechanische Analyse nanoporöser Metalle durch maschinelles Lernen 
Projekt DEAL 
SFB 986: Zentralprojekt Z03 - Elektronenmikroskopie an multiskaligen Materialsystemen 
Funded by: Deutsche Forschungsgemeinschaft (DFG) 
More Funding information: This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Founda tion) - Project SFB 986 -Tailor-Made Multiscale Materials Systems, subproject B9 - Microstructure-based classifcation and mechanical analysis of nanoporous metals by machine learning.
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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