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Semantic segmentation in crystal growth process using fake micrograph machine learning
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DOI：10.1038/s41598-024-70530-3.png)
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Abstract
En 中文
Microscopic evaluation is one of the most effective methods in materials research. High-quality images are essential to analyze microscopic images using artificial intelligence. To overcome this challenge, we propose the machine learning of fake micrographs in this study. To verify the effectiveness of this method, we chose to analyze the optical microscopic images of the crystal growth process of a Ge thin film, which is a material in which it is difficult to obtain a contrast between the crystal and amorphous states. By learning the automatically generated fake micrographs that mimic the crystal growth process, the machine learning model can now identify the low-resolution real micrographs as crystalline or amorphous. Comparing the three types of machine learning models, it was found that ResUNet ++ exhibited high accuracy, exceeding 90%. The technology developed in this study for the automatic and rapid analysis of low-resolution images is widely helpful in material research.
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