Learning-based Defect Recognition for Quasi-Periodic Microscope Images
- URL: http://arxiv.org/abs/2007.01309v2
- Date: Sun, 9 Aug 2020 11:14:57 GMT
- Title: Learning-based Defect Recognition for Quasi-Periodic Microscope Images
- Authors: Nik Dennler, Antonio Foncubierta-Rodriguez, Titus Neupert, Marilyne
Sousa
- Abstract summary: We propose a semi-supervised machine learning method that assists in the detection of lattice defects from atomic resolution microscope images.
It involves a convolutional neural network that classifies image patches as defective or non-defective, a graph-based that chooses one non-defective patch as a model, and finally an automatically generated convolutional filter bank.
The algorithm is tested on III-V/Si crystalline materials and successfully evaluated against different metrics, showing promising results even for extremely small training data sets.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Controlling crystalline material defects is crucial, as they affect
properties of the material that may be detrimental or beneficial for the final
performance of a device. Defect analysis on the sub-nanometer scale is enabled
by high-resolution (scanning) transmission electron microscopy [HR(S)TEM],
where the identification of defects is currently carried out based on human
expertise. However, the process is tedious, highly time consuming and, in some
cases, yields ambiguous results. Here we propose a semi-supervised machine
learning method that assists in the detection of lattice defects from atomic
resolution microscope images. It involves a convolutional neural network that
classifies image patches as defective or non-defective, a graph-based heuristic
that chooses one non-defective patch as a model, and finally an automatically
generated convolutional filter bank, which highlights symmetry breaking such as
stacking faults, twin defects and grain boundaries. Additionally, we suggest a
variance filter to segment amorphous regions and beam defects. The algorithm is
tested on III-V/Si crystalline materials and successfully evaluated against
different metrics, showing promising results even for extremely small training
data sets. By combining the data-driven classification generality, robustness
and speed of deep learning with the effectiveness of image filters in
segmenting faulty symmetry arrangements, we provide a valuable open-source tool
to the microscopist community that can streamline future HR(S)TEM analyses of
crystalline materials.
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