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使用卷积神经网络进行TEM对齐的原理:对冷凝器光圈对齐的案例研究
Loïc Grossetête1, Cécile Marcelot2, Christophe Gatel2
1CEMES-CNRS, 29 rue Jeanne Marvig, Toulouse, 31055, France; Fédération ENAC ISAE-SUPAERO ONERA, 7 Avenue Edouard Belin, Toulouse, 31055, France.
Ultramicroscopy
|October 16, 2024
概括
本研究介绍了一种使用卷积神经网络 (CNN) 来自动对齐传输电子显微镜 (TEM) 的人工智能 (AI) 方法. 人工智能系统准确地将冷凝器孔径定位在中心,减少了显微镜师的训练时间.
科学领域:
- 材料科学 材料科学 材料科学
- 仪器化 仪器化 仪器化
- 计算科学 计算科学
背景情况:
- 传输电子显微镜 (TEM) 需要精确的对齐以获得最佳性能.
- 手动对齐是耗时的,需要广泛的用户培训.
- 自动化对齐可以显著提高TEM技术的效率和可访问性.
研究的目的:
- 探索使用人工智能 (AI) 实现自动化TEM对齐的可行性.
- 开发和测试一种基于人工智能的方法,用于集中冷凝器孔径,这是一个关键的对齐步骤.
- 评估AI减少TEM操作所需的技能和时间的潜力.
主要方法:
- 一个卷积神经网络 (CNN) 被开发来预测孔径调整所需的转移.
- 一个简化的数字双胞胎被用于自动化数据采集,用于训练CNN.
- 评估了各种CNN模型,以确定最佳的性能设计.
主要成果:
- 开发的AI方法在集中冷凝器孔径方面实现了人类水平的性能.
- 该系统证明了能够在单个步骤中准确预测所需的x和y转移的能力.
- 人工智能方法证明与持续漂移校正和照明均性相兼容.
结论:
- 人工智能,特别是CNN,为自动化TEM对齐任务提供了可行的解决方案.
- 这种方法可以大大减少TEM用户的学习曲线和操作时间.
- 由人工智能驱动的对齐可适应各种对齐步骤和实验期间的持续校正.
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