arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2002.04716cond-mat.mtrl-sciphysics.app-ph
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H-Si(100)2x 1表面扫描隧道显微镜原子和缺陷识别的鲁棒多尺度多特征深度学习

Robust multi-scale multi-feature deep learning for atomic and defect identification in Scanning Tunneling Microscopy on H-Si(100) 2x1 surface

Maxim Ziatdinov, Udi Fuchs, James H. G. Owen, John N. Randall, Sergei V. Kalinin

摘要:

使用深度学习和扫描隧道显微镜(STM)分析氢钝化Si(100)表面上的原子缺陷的性质。开发了一个强大的深度学习框架,能够在存在未解决的污染物、阶跃边缘和噪声的情况下识别原子种类、缺陷。自动化的工作流程,结合几个网络的图像评估,原子发现和缺陷发现的基础上,开发执行分析在不同层次的描述,并部署在一个可操作的STM平台。这进一步扩展到使用均值漂移聚类算法对提取的缺陷进行无监督分类,该算法利用从神经网络的组合输出自动设计的特征。这种组合的方法允许识别局部和扩展的缺陷上的形貌不均匀的表面或真实的材料。我们的方法在本质上是通用的,并且可以应用于其他表面,用于构建量子材料中原子缺陷的综合库。

英文摘要:

The nature of the atomic defects on the hydrogen passivated Si (100) surface is analyzed using deep learning and scanning tunneling microscopy (STM). A robust deep learning framework capable of identifying atomic species, defects, in the presence of non-resolved contaminates, step edges, and noise is developed. The automated workflow, based on the combination of several networks for image assessment, atom-finding and defect finding, is developed to perform the analysis at different levels of description and is deployed on an operational STM platform. This is further extended to unsupervised classification of the extracted defects using the mean-shift clustering algorithm, which utilizes features automatically engineered from the combined output of neural networks. This combined approach allows the identification of localized and extended defects on the topographically non-uniform surfaces or real materials. Our approach is universal in nature and can be applied to other surfaces for building comprehensive libraries of atomic defects in quantum materials.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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