Three-dimensional structural comparison of tantalum glancing angle deposition thin films by FIB-SEM

Ott, T.; Roldán, Diego Gerardo; Redenbach, Claudia; Schladitz, Katja; Godehardt, Michael; Höhn, Sören

doi:10.5194/jsss-8-305-2019

Cited by 10 publications

(14 citation statements)

References 36 publications

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“…(2020) and Ott et al . (2019). Lacking a proper ground truth, we use this reconstruction as reference.…”

Section: Resultsmentioning

confidence: 99%

“…Left: stack of backscattered electron images. Centre: reference reconstruction -secondary electron and backscattered electron signals combined using reconstruction by dilation as described by Prill et al (2020); Ott et al (2019). Right: reconstruction result of the trained convolutional neural network.…”

Section: Real Data Setsmentioning

confidence: 99%

“…Both secondary electron and backscattered electron signals are available. Thus reconstruction of the 3D solid structure can rely on morphological combination of the two signals by reconstruction by dilation as described by Prill et al (2020) and Ott et al (2019). Lacking a proper ground truth, we use this reconstruction as reference.…”

Section: Real Data Setsmentioning

confidence: 99%

“…Thus, the stack does not represent the true 3D structure. Reconstructing the proper spatial structure is in general not yet resolved despite of several algorithms developed particularly for this purpose (Prill et al ., 2013, 2020; Salzer et al ., 2014, 2015; Holzer et al ., 2016; Penner & Holzer, 2018; Ott et al ., 2019). All these solutions are tailored to specific structures and SEM modes and therefore not generally applicable.…”

Section: Introductionmentioning

confidence: 99%

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Reconstruction of highly porous structures from FIB‐SEM using a deep neural network trained on synthetic images

Fend

Moghiseh

Redenbach

et al. 2020

Journal of Microscopy

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Combining scanning electron microscopy with serial slicing by a focused ion beam yields spatial image data of materials structures at the nanometer scale. However, the depth of field of the scanning electron microscopic images causes unwanted effects when highly porous structures are imaged. Proper spatial reconstruction of such porous structures from the stack of microscopic images is a tough and in general yet unsolved segmentation problem. Recently, machine learning methods have proven to yield solutions to a variety of image segmentation problems. However, their use is hindered by the need of large amounts of annotated data in the training phase. Here, we therefore replace annotated real image data by simulated image stacks of synthetic structures-realizations of stochastic germ-grain models and random packings. This strategy yields the annotations for free, but shifts the effort to choosing appropriate stochastic geometry models and generating sufficiently realistic scanning electron microscopic images.

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“…(2020) and Ott et al . (2019). Lacking a proper ground truth, we use this reconstruction as reference.…”

Section: Resultsmentioning

confidence: 99%

Section: Real Data Setsmentioning

confidence: 99%

Section: Real Data Setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reconstruction of highly porous structures from FIB‐SEM using a deep neural network trained on synthetic images

Fend

Moghiseh

Redenbach

et al. 2020

Journal of Microscopy

View full text Add to dashboard Cite

show abstract

“…A second class of mitigation techniques applies image processing tools to remove striped content from the micrograph; for example, in [5,6], image content with low horizontal frequency is removed. Because these methods are designed to remove stripes, they have also been applied to remove 'curtaining' artifacts that arise from variations in an ion beam's milling rate [7,8]. Though undesired, curtaining arises from accurate representation of the sample.…”

Section: Introductionmentioning

confidence: 99%

Robustness of Time-Resolved Measurement to Unknown and Variable Beam Current in Particle Beam Microscopy

Watkins

Seidel

Peng

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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Variations in the intensity of the incident beam can cause significant inaccuracies in microscopes that use focused beams of electrons or ions. Existing mitigation methods depend on the artifacts having characteristic spatial structures explained by the raster scan pattern and temporal correlation of the beam current variations. We show that recently introduced time-resolved measurement methods create robustness to beam current variations that improve significantly upon existing methods while not depending on separability of artifact structure from underlying image content. These advantages are illustrated through Monte Carlo simulations representative of both helium ion microscopy (higher secondary electron yield) and scanning electron microscopy (lower secondary electron yield). Notably, this demonstrates that when the beam current variation is appreciable, time-resolved measurements provide a novel benefit in particle beam microscopy that extends to low secondary electron yields.

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Image Based Methodologies, Workflows, and Calculation Approaches for Tortuosity

Holzer,

Marmet,

Fingerle

et al. 2023

Springer Series in Materials Science

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In this chapter, modern methodologies for characterization of tortuosity are thoroughly reviewed. Thereby, 3D microstructure data is considered as the most relevant basis for characterization of all three tortuosity categories, i.e., direct geometric, indirect physics-based and mixed tortuosities. The workflows for tortuosity characterization consists of the following methodological steps, which are discussed in great detail: (a) 3D imaging (X-ray tomography, FIB-SEM tomography and serial sectioning, Electron tomography and atom probe tomography), (b) qualitative image processing (3D reconstruction, filtering, segmentation) and (c) quantitative image processing (e.g., morphological analysis for determination of direct geometric tortuosity). (d) Numerical simulations are used for the estimation of effective transport properties and associated indirect physics-based tortuosities. Mixed tortuosities are determined by geometrical analysis of flow fields from numerical transport simulation. (e) Microstructure simulation by means of stochastic geometry or discrete element modeling enables the efficient creation of numerous virtual 3D microstructure models, which can be used for parametric studies of micro–macro relationships (e.g., in context with digital materials design or with digital rock physics). For each of these methodologies, the underlying principles as well as the current trends in technical evolution and associated applications are reviewed. In addition, a list with 75 software packages is presented, and the corresponding options for image processing, numerical simulation and stochastic modeling are discussed. Overall, the information provided in this chapter shall help the reader to find suitable methodologies and tools that are necessary for efficient and reliable characterization of specific tortuosity types.

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Three-dimensional structural comparison of tantalum glancing angle deposition thin films by FIB-SEM

Cited by 10 publications

References 36 publications

Reconstruction of highly porous structures from FIB‐SEM using a deep neural network trained on synthetic images

Reconstruction of highly porous structures from FIB‐SEM using a deep neural network trained on synthetic images

Robustness of Time-Resolved Measurement to Unknown and Variable Beam Current in Particle Beam Microscopy

Image Based Methodologies, Workflows, and Calculation Approaches for Tortuosity

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