Workflow automation of SEM acquisitions and feature tracking

Area of Science:

• Materials Science
• Nanotechnology
• Microscopy

Background:

• Quantitative analysis of nanoparticles (NPs) using scanning electron microscopy (SEM) requires numerous high-resolution images.
• Manual acquisition of these images is time-consuming and repetitive for researchers.

Purpose of the Study:

• To develop and demonstrate an automated workflow for SEM image acquisition for NP analysis.
• To improve the efficiency and accuracy of nanoparticle characterization.

Main Methods:

• A Python-based script was developed to automate SEM image acquisition, including beam repositioning and image stitching.
• The workflow involved feature segmentation and NP size computation from image montages at various magnifications (20,000x, 60,000x, 200,000x).
• Feature tracking with smart beam positioning was introduced to optimize acquisition by focusing on areas of interest.

Main Results:

• The automated workflow successfully generated size distributions for NP populations.
• High-magnification imaging was found to be crucial for accurate NP characterization due to pixel size limitations at lower magnifications.
• Automated acquisition significantly increases the area coverage at high resolutions, essential for representative NP analysis.

Conclusions:

• Automation of SEM image acquisition is vital for efficient and accurate nanoparticle characterization.
• The proposed workflow and feature tracking approach reduce acquisition time while ensuring comprehensive data.
• This method facilitates statistically robust analysis of nanoparticle populations.