Abstract
Focused ion beam (FIB) tomography is a destructive technique used to collect three-dimensional (3D) structural information at a resolution of a few nanometers. For FIB tomography, a material sample is degraded by layer-wise milling. After each layer, the current surface is imaged by a scanning electron microscope (SEM), providing a consecutive series of cross-sections of the three-dimensional material sample. Especially for nanoporous materials, the reconstruction of the 3D microstructure of the material, from the information collected during FIB tomography, is impaired by the so-called shine-through effect. This effect prevents a unique mapping between voxel intensity values and material phase (e.g., solid or void). It often substantially reduces the accuracy of conventional methods for image segmentation. Here we demonstrate how machine learning can be used to tackle this problem. A bottleneck in doing so is the availability of sufficient training data. To overcome this problem, we present a novel approach to generate synthetic training data in the form of FIB-SEM images generated by Monte Carlo simulations. Based on this approach, we compare the performance of different machine learning architectures for segmenting FIB tomography data of nanoporous materials. We demonstrate that two-dimensional (2D) convolutional neural network (CNN) architectures processing a group of adjacent slices as input data as well as 3D CNN perform best and can enhance the segmentation performance significantly.
Highlights
Nanoporous materials bear great potential in microtechnology, chemical engineering, biomedical engineering, energy technology and electronics and communication technology
Deep learning can play an important role in the segmentation of Focused ion beam (FIB) tomography data
We demonstrated that the lack of training data can be overcome by generating virtual microstructures and simulating them using the MCXray method, providing ample synthetic FIB tomography training data
Summary
Nanoporous materials bear great potential in microtechnology, chemical engineering, biomedical engineering, energy technology and electronics and communication technology. Accurate 3D reconstruction of nanoporous structures remains a challenge because of the so-called shine-through effect in FIB tomography data (Prill et al, 2013) Due to this effect, the Reconstruction of Nanoporous Structures intensity of pixels in the SEM images generally depends on the material at the respective position in the plane currently imaged and on structures in deeper layers. The Reconstruction of Nanoporous Structures intensity of pixels in the SEM images generally depends on the material at the respective position in the plane currently imaged and on structures in deeper layers This effect occurs because these structures may shine through the nanopores up to the surface currently imaged by SEM, in case of back-scattered electron (BSE) imaging even in infiltrated nanoporous materials. This ambiguity makes segmentation of FIB tomography data of nanoporous materials highly non-trivial (Figure 1A)
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