减小偏差校正的发射率I:在不知偏差系数的情况下对电子显微镜进行偏差校正
Desheng Ma1, Steven E Zeltmann2, Chenyu Zhang1
1School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA.
Ultramicroscopy
|April 13, 2025
概括
我们介绍了一个深度学习模型,以最大限度地减少扫描传输电子显微镜 (STEM) 中的电子束发射率增长,以纠正扫描传输电子显微镜 (STEM) 的偏差. 这种方法简化和自动化复杂的过程,使原子级材料分析.
科学领域:
- 材料科学 材料科学 材料科学
- 物理 物理学 物理
- 计算机科学 计算机科学
背景情况:
- 精确的电子束对齐对于使用扫描传输电子显微镜 (STEM) 的原子级分析至关重要.
- 在STEM中纠正偏差,虽然已经很先进,但仍然是一个复杂和耗时的过程.
- 现有的方法往往缺乏实时调整的速度和自动化.
研究的目的:
- 从加速器物理的角度来看,将偏差校正重新定义为最小化光束发射率增长.
- 从Ronchigrams开发一个深度学习模型来预测发行量增长.
- 为了使STEM中更快,更自动,更准确的偏差校正.
主要方法:
- 训练了一个深度学习模型,从实验获得的Ronchigrams中预测光束发射率的增长.
- 通过模拟和实验,验证了模型在捕获异常系数的发射变量的准确性.
- 使用训练模型作为快速功能,对镜头参数进行全球优化.
主要成果:
- 深度学习模型根据偏差系数准确预测发行量增长.
- 该模型有效量化了来自Ronchigram数据的发射变化.
- 证明了该模型在优化镜头参数以纠正偏差方面的实用性.
结论:
- 偏差校正可以通过最大限度地减少光束发射量的增长来进行.
- 在Ronchigrams上训练的深度学习模型提供了一种快速而准确的方法来量化束属性.
- 这种方法促进了自动化和高效的偏差校正,提高了材料研究的STEM能力.
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