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Item type:Publication, Access status: with files , Enhancing 3D reconstruction accuracy of FIB tomography data using multi-voltage images and multimodal machine learningFIB-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.Publicationtype: Journal Article76 127 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Role of slice thickness quantification in the 3D reconstruction of FIB tomography data of nanoporous materials(Elsevier, 2024-02); ; ; ; In focused ion beam (FIB) tomography, a combination of FIB with a scanning electron microscope (SEM) is used for collecting a series of planar images of the microstructure of nanoporous materials. These planar images serve as the basis for reconstructing the three-dimensional microstructure through segmentation algorithms. However, the assumption of a constant distance between consecutively imaged sections is generally invalid due to random variations in the FIB milling process. This variation complicates the accurate reconstruction of the three-dimensional microstructure. Using synthetic FIB tomography data, we present an algorithm that repositions slices according to their actual thickness and interpolates the results using machine learning-based methods. We applied our algorithm to real datasets, comparing two standard approaches of microstructure reconstruction: on-the-fly via image processing and ruler-based via sample structuring. Our findings indicate that the ruler-based method, combined with our novel slice repositioning and interpolation algorithm, exhibits superior performance in reconstructing the microstructure.Publicationtype: Journal ArticleScopus© Citations 2 63 123 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Slice thickness optimization for the focused ion beam-scanning electron microscopy 3D tomography of hierarchical nanoporous goldThe 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.Publicationtype: Journal ArticleScopus© Citations 6 106 207