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-p...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 imagesand using FIB to remove material layers for imaging subsequent planes,thereby producing image stacks.However,theseimage stacks in FIB-SEM tomography are subject to the shine-through effect,which makes structures visible from theposterior regions of the current plane.This artifact introduces an ambiguity between image intensity and structures in thecurrent plane,making conventional segmentation methods such as thresholding or the k-means algorithm insufficient.Inthis study,we propose a multimodal machine learning approach that combines intensity information obtained at differentelectron beam accelerating voltages to improve the three-dimensional(3D)reconstruction of nanostructures.By treatingthe increased shine-through effect at higher accelerating voltages as a form of additional information,the proposed methodsignificantly improves segmentation accuracy and leads to more precise 3D reconstructions for real FIB tomography data.展开更多
The combination of focused ion beam (FIB) with scanning electron microscopy (SEM), also known as FIB-SEM tomography, has become a powerful 3D imaging technique at the nanometer scale. This method uses an ion beam to m...The combination of focused ion beam (FIB) with scanning electron microscopy (SEM), also known as FIB-SEM tomography, 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 effects, beam drift, or sample surface roughness. Determining and optimizing the actual slice thickness variation appear challenging. In this work, we examine the influence 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.展开更多
基金funded by the Deutsche Forschungsgemein-schaft(DFG,German Research Foundation)-SFB 986-Project number 192346071.
文摘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 imagesand using FIB to remove material layers for imaging subsequent planes,thereby producing image stacks.However,theseimage stacks in FIB-SEM tomography are subject to the shine-through effect,which makes structures visible from theposterior regions of the current plane.This artifact introduces an ambiguity between image intensity and structures in thecurrent plane,making conventional segmentation methods such as thresholding or the k-means algorithm insufficient.Inthis study,we propose a multimodal machine learning approach that combines intensity information obtained at differentelectron beam accelerating voltages to improve the three-dimensional(3D)reconstruction of nanostructures.By treatingthe increased shine-through effect at higher accelerating voltages as a form of additional information,the proposed methodsignificantly improves segmentation accuracy and leads to more precise 3D reconstructions for real FIB tomography data.
基金funded by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)—Project SFB 986—Tailor-Made Multiscale Materials Systems,subproject B9—Microstructure-based classification and mechanical analysis of nanoporous metals by machine learningOpen Access funding enabled and organized by Projekt DEAL.
文摘The combination of focused ion beam (FIB) with scanning electron microscopy (SEM), also known as FIB-SEM tomography, 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 effects, beam drift, or sample surface roughness. Determining and optimizing the actual slice thickness variation appear challenging. In this work, we examine the influence 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.
