论文标题
通过自动扫描透射电子显微镜在单个缺陷水平上探测电子束诱导的转换
Probing electron beam induced transformations on a single defect level via automated scanning transmission electron microscopy
论文作者
论文摘要
基于深卷积神经网络的集合学习和迭代训练（ELIT）的扫描传输电子显微镜（STEM）数据流的实时分析的可靠方法，可以在操作显微镜上实施，从而探索通过自动化的STEM中的Electraded Experients sem中的电子束irapiation in sem中的特定原子构型的动力学探索。结合光束控制，这种方法允许在全自动模式下研究光束对选定的原子组和化学键的影响。在这里，我们证明了过渡金属二分法中的单个空缺线的原子上精确的工程以及拓扑缺陷石墨烯的创造和鉴定。基于ELIT的方法为直接分析茎数据的直接分析和实时反馈方案打开了途径，以探测电子束化学，原子操作和原子组装原子。
The robust approach for real-time analysis of the scanning transmission electron microscopy (STEM) data streams, based on the 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 graphene. The ELIT-based approach opens the pathway toward the direct on-the-fly analysis of the STEM data and engendering real-time feedback schemes for probing electron beam chemistry, atomic manipulation, and atom by atom assembly.
