Machine learning of fake micrographs for automated analysis of crystal growth process

Abstract

Material informatics is being applied to crystal engineering, which is a core technology in electronics. Micrographs particularly provide important insights; however, they have not benefited significantly from material informatics because of the efforts required to acquire huge numbers of data. Herein, we propose a fast and automated analysis technique for micrographs showing the crystallization process of semiconductor thin films. We automatically generated fake micrographs and trained the crystal domain recognition capability on 10 different machine learning models. Experimentally obtained micrographs were analyzed using the developed model, which correctly determined the domain size and nuclei density. The activation energies required for growth and nucleation were determined from the lateral growth velocity and nucleation frequency, the variations of which were smaller than those measured by humans. Therefore, the proposed analysis framework not only reduces the time required to derive the crystal growth properties, but also enables a high accuracy without human subjectivity.

Keywords

• image recognition
• microscopy
• semiconductors
• thin-film growth