Snake Scanning for SEM: Quantification and Correction of Its Inherent Misalignment Distortion Using an External Scan
Jieping Ding1, Ling'en Liu1, Ni Wang2
1School of Materials Science and Engineering, Beijing University of Technology, Beijing 100124, China.
Materials (Basel, Switzerland)
|January 10, 2026
Summary
Scanning electron microscope (SEM) image distortions are corrected using a novel controller and software. This system improves image accuracy by compensating for scanning artifacts, enhancing quantitative analysis in microscopy.
Area of Science:
- Materials Science and Engineering
- Microscopy and Imaging Technologies
- Computational Science
Background:
- Scanning electron microscope (SEM) image distortions significantly hinder accurate material characterization and quantitative analysis.
- Sources of distortion, including scanning coil hysteresis and electronic delays, present complex challenges for correction.
- Existing methods struggle to effectively quantify and rectify these inherent image artifacts.
Purpose of the Study:
- To develop a robust system for quantifying and correcting distortions in SEM images.
- To investigate the relationship between scanning parameters and the magnitude of image distortions.
- To provide a framework for improving image quality and enabling reliable quantitative analysis in SEM.
Main Methods:
- Independently developed a custom scanning controller and software for capturing unprocessed raw SEM data.
- Utilized snake scanning's row misalignment to split images, measure offsets with the ORB algorithm, and perform pixel compensation.
- Experimental validation compared corrected images against raster scanning and assessed reference-free quality metrics.
Main Results:
- Corrected SEM images demonstrated significantly reduced distortion artifacts.
- Structural similarity of corrected images matched raster scanning results, with improved reference-free quality.
- Distortion magnitude was found to be independent of magnification and primarily dependent on dwell time, stabilizing at a critical threshold.
Conclusions:
- The developed system effectively corrects SEM image distortions, enhancing accuracy for quantitative analysis.
- Clarified the relationship between scanning parameters (dwell time) and distortion behavior, enabling optimized SEM strategies.
- The proposed framework offers a scalable solution for distortion correction applicable to SEM and other microscopy techniques.
