Automatic Alignment of an Orbital Angular Momentum Sorter in a Transmission Electron Microscope Using a Convolutional...

Area of Science:

• Physics
• Materials Science
• Electron Microscopy

Background:

• Transmission electron microscopy (TEM) requires precise alignment for high-resolution imaging.
• Orbital angular momentum (OAM) sorters enhance TEM capabilities but are sensitive to misalignments.
• Manual alignment of OAM sorters is time-consuming and requires expert knowledge.

Purpose of the Study:

• To develop an automated alignment method for TEMs with OAM sorters.
• To utilize a convolutional neural network (CNN) for real-time control of alignment parameters.
• To improve the spectral resolution and stability of OAM sorters in TEM.

Main Methods:

• Implementation of a CNN for controlling electron-optical parameters of the TEM.
• Integration of the CNN with the voltage source of the OAM sorter.
• Real-time compensation for mechanical and optical misalignments using the CNN.

Main Results:

• The CNN successfully achieved automatic alignment of the TEM and OAM sorter.
• The system compensated for misalignments, optimizing spectral resolution.
• Alignment was completed within a few frames, demonstrating rapid convergence.
• The CNN maintained stable alignment due to its fast fitting capabilities.

Conclusions:

• AI-driven automation offers a robust solution for aligning complex electron microscopy setups.
• CNNs can effectively control multiple parameters for optimizing OAM sorter performance.
• Automated alignment significantly reduces the time and expertise needed for TEM operation.