Journal
MATERIALS CHARACTERIZATION
Volume 146, Issue -, Pages 307-318Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.matchar.2018.02.040
Keywords
FIM; Image processing; Machine learning; Atomic resolution; Data mining
Categories
Funding
- IMPRS SurMat
- European Research Council (ERC) under the EU's Horizon 2020 Research and Innovation Programme [639211]
Ask authors/readers for more resources
Field ion microscopy (FIM) allows to image individual surface atoms by exploiting the effect of an intense electric field. Widespread use of atomic resolution imaging by FIM has been hampered by a lack of efficient image processing/data extraction tools. Recent advances in imaging and data mining techniques have renewed the interest in using FIM in conjunction with automated detection of atoms and lattice defects for materials characterization. After a brief overview of existing routines, we review the use of machine learning (ML) approaches for data extraction with the aim to catalyze new data-driven insights into high electrical field physics. Apart from exploring various supervised and unsupervised ML algorithms in this context, we also employ advanced image processing routines for data extraction from large sets of FIM images. The outcomes and limitations of such routines are discussed, and we conclude with the possible application of energy minimization schemes to the extracted point clouds as a way of improving the spatial resolution of FIM.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available