扫描透射电子显微镜
石墨烯
透射电子显微镜
Atom(片上系统)
电子
材料科学
阴极射线
反射高能电子衍射
电子束诱导沉积
原子物理学
纳米技术
物理
计算机科学
光学
电子衍射
衍射
嵌入式系统
量子力学
作者
Kevin M. Roccapriore,Matthew G. Boebinger,Ondrej Dyck,Ayana Ghosh,Raymond R. Unocic,Sergei V. Kalinin,Maxim Ziatdinov
出处
期刊:ACS Nano
[American Chemical Society]
日期:2022-10-07
卷期号:16 (10): 17116-17127
被引量:17
标识
DOI:10.1021/acsnano.2c07451
摘要
A robust approach for real-time analysis of the scanning transmission electron microscopy (STEM) data streams, based on ensemble learning and iterative training (ELIT) of deep convolutional neural networks, is implemented on an operational microscope, enabling the exploration of the dynamics of specific atomic configurations under electron beam irradiation via an automated experiment in STEM. Combined with beam control, this approach allows studying beam effects on selected atomic groups and chemical bonds in a fully automated mode. Here, we demonstrate atomically precise engineering of single vacancy lines in transition metal dichalcogenides and the creation and identification of topological defects in graphene. The ELIT-based approach facilitates direct on-the-fly analysis of the STEM data and engenders real-time feedback schemes for probing electron beam chemistry, atomic manipulation, and atom by atom assembly.
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