Directed atomic fabrication using an aberration-corrected scanning transmission electron microscope (STEM) opens new pathways for atomic engineering of functional materials. In this approach, the electron beam is used to actively alter the atomic structure through electron beam induced irradiation processes. One of the impediments that has limited widespread use thus far has been the ability to understand the fundamental mechanisms of atomic transformation pathways at high spatiotemporal resolution. Here, we develop a workflow for obtaining and analyzing high-speed spiral scan STEM data, up to 100 fps, to track the atomic fabrication process during nanopore milling in monolayer MoS₂. An automated feedback-controlled electron beam positioning system combined with deep convolution neural network (DCNN) was used to decipher fast but low signal-to-noise datasets and classify time-resolved atom positions and nature of their evolving atomic defect configurations. Through this automated decoding, the initial atomic disordering and reordering processes leading to nanopore formation was able to be studied across various timescales. Using these experimental workflows a greater degree of speed and information can be extracted from small datasets without compromising spatial resolution. This approach can be adapted to other 2D materials systems to gain further insights into the defect formation necessary to inform future automated fabrication techniques utilizing the STEM electron beam.

使用像差校正扫描透射电子显微镜 （STEM） 的定向原子制造为功能材料的原子工程开辟了新的途径。在这种方法中，电子束用于通过电子束诱导照射过程主动改变原子结构。迄今为止，限制广泛使用的障碍之一是在高时空分辨率下理解原子转变途径的基本机制的能力。在这里，我们开发了一种工作流程，用于获取和分析高达 100 fps 的高速螺旋扫描 STEM 数据，以跟踪单层 MoS₂ 中纳米孔铣削过程中的原子制造过程。自动反馈控制的电子束定位系统与深度卷积神经网络 （DCNN） 相结合，用于破译快速但低信噪比的数据集，并对时间分辨的原子位置及其不断发展的原子缺陷配置的性质进行分类。通过这种自动解码，能够在不同的时间尺度上研究导致纳米孔形成的初始原子无序和重排序过程。使用这些实验工作流，可以在不影响空间分辨率的情况下，从小型数据集中提取更高程度的速度和信息。这种方法可以适用于其他 2D 材料系统，以进一步了解缺陷的形成，从而为未来利用 STEM 电子束的自动化制造技术提供信息。