原子力显微镜的机器学习支持的自主操作
Seongseok Kang1, Junhong Park1, Manhee Lee1
1Department of Physics, Chungbuk National University, Seowon-Gu, Cheongju 28644, South Korea.
The Review of scientific instruments
|December 18, 2023
概括
这项研究引入了使用机器学习的自主原子力显微镜,以获得可重现的结果. 该系统自动化了初始化,成像和分析,减少了科学测量的操作员变化.
科学领域:
- 材料科学 材料科学 材料科学
- 纳米技术纳米技术
- 自动化工程自动化工程
背景情况:
- 科学仪器操作通常需要专门的技能,导致结果的变化.
- 自主操作可以提高科学测量的可重现性和可靠性.
研究的目的:
- 使用机器学习演示原子力显微镜 (AFM) 的自主操作.
- 为了减少AFM测量的操作员对操作员的变化.
主要方法:
- 使用基于区域的卷积神经网络实现基于机器学习的对象检测技术.
- 使用两个摄像头进行对象识别,自我校准和仪器对齐.
- 开发用于AFM初始化,表面成像和图像分析的自动化系统.
主要成果:
- 原子力显微镜的成功自主初始化,表面成像和图像分析.
- 机器学习算法的证明能力,以执行自我校准和对齐.
- 在操作人员的最小干预下取得可重复和可靠的结果.
结论:
- 机器学习使原子力显微镜能够自主运行.
- 开发的方法可以通用到其他扫描探头显微镜和科学仪器.
- 自主系统通过提高数据的一致性,为科学研究提供了显著的好处.
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