Unravelling stacking order in epitaxial bilayer MX₂ using 4D-STEM with unsupervised learning.

Area of Science:

• Materials Science
• Condensed Matter Physics
• Nanotechnology

Background:

• Monolayer transition metal dichalcogenides (MX₂) research has expanded to multilayer systems.
• Stacking order in bilayer MX₂ significantly influences band structure, local confinement, and symmetry.
• Conventional methods for determining stacking order are limited to specific domain shapes.

Purpose of the Study:

• To develop and apply a novel method for determining stacking order in multilayer MX₂.
• To investigate stacking order in epitaxially grown bilayer MoS₂.
• To overcome limitations of existing techniques for complex material morphologies.

Main Methods:

• Utilized 4D scanning transmission electron microscopy (4D STEM).
• Employed multislice diffraction simulations for data analysis.
• Applied machine learning-based data segmentation for statistical analysis of grain orientation and stacking.

Main Results:

• Successfully unraveled the stacking order in epitaxially grown bilayer MoS₂.
• Demonstrated the capability of 4D STEM to extract detailed structural information.
• Obtained statistics on monolayer grain orientation and bilayer stacking.

Conclusions:

• 4D STEM combined with diffraction simulations and machine learning provides a robust method for analyzing multilayer MX₂.
• This technique is applicable to complex island shapes where traditional methods fail.
• The findings advance the understanding and characterization of multilayer transition metal dichalcogenides.