Autonomous Fabrication of Tailored Defect Structures in 2D Materials using Machine Learning-enabled Scanning Transmission Electron Microscopy

TL;DR

This paper presents an autonomous, machine learning-enabled method for precisely fabricating atomic defects in 2D materials using STEM, demonstrated on MoS2, with potential for broad application in material engineering.

Contribution

The authors develop a fully autonomous framework combining machine learning and automated STEM control for defect fabrication in 2D materials, advancing precision and versatility.

Findings

Achieved controlled sulfur atom ejection in MoS2 using autonomous STEM.

Developed a machine learning system for real-time atomic identification from HAADF images.

Framework is adaptable to various 2D materials for defect engineering.

Abstract

Materials with tailored quantum properties can be engineered from atomic scale assembly techniques, but existing methods often lack the agility and accuracy to precisely and intelligently control the manufacturing process. Here we demonstrate a fully autonomous approach for fabricating atomic-level defects using electron beams in scanning transmission electron microscopy (STEM) that combines advanced machine learning and automated beam control. As a proof of concept, we achieved controlled fabrication of MoS-nanowire (MoS-NW) edge structures by iterative and targeted exposure of $MoS_2$ monolayer to a focused electron beam to selectively eject sulfur atoms, utilizing high-angle annular dark-field (HAADF) imaging for feedback-controlled monitoring structural evolution of defects. A machine learning framework combining a random forest model and convolutional neural networks (CNN) was developed to decode the HAADF image and accurately identify atomic positions and species. This atomic-level information was then integrated into an autonomous decision-making platform, which applied predefined fabrication strategies to instruct beam control about atomic sites to be ejected. The selected sites were subsequently exposed to localized electron beam using an FPGA-controlled scan routine with precise control over beam positioning and duration. While the MoS-NW edge structures produced exhibit promising mechanical and electronic properties, the proposed autonomous fabrication framework is material-agnostic and can be extended to other 2D materials for the creation of diverse defect structures and heterostructures beyond $MoS_2$.