Ishiyama Takamitsu, Suemasu Takashi, Toko Kaoru
Institute of Applied Physics, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8573, Japan.
Sci Rep. 2024 Aug 21;14(1):19449. doi: 10.1038/s41598-024-70530-3.
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.
微观评估是材料研究中最有效的方法之一。高质量图像对于使用人工智能分析微观图像至关重要。为了克服这一挑战，我们在本研究中提出了对“假显微照片”进行机器学习的方法。为了验证该方法的有效性，我们选择分析锗薄膜晶体生长过程的光学显微镜图像，锗是一种难以在晶体态和非晶态之间获得对比度的材料。通过学习模拟晶体生长过程自动生成的假显微照片，机器学习模型现在可以将低分辨率真实显微照片识别为晶体或非晶态。比较三种类型的机器学习模型后发现，ResUNet ++表现出较高的准确率，超过了90%。本研究中开发的用于低分辨率图像自动快速分析的技术在材料研究中具有广泛的帮助。
