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Atomic Force Microscopy

Atomic Force Microscopy

Atomic force microscopy (AFM) is a type of scanning probe microscopy that can analyze topographic details of various specimens like ceramics, glass, polymers, and biological samples. AFM offers over 1000 times more resolution than the optical imaging system. Images generated from AFM are three-dimensional surface profiles, offering an advantage over the flat, two-dimensional images from other imaging techniques.
The AFM Probe
The probe is regarded as the heart of any AFM setup and comprises the...

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Horizontal Distortion Correction of AFM Images Based on Automatic Labeling of Feature Graphics.

Ke Xu¹, Yuzhe Liu¹

¹School of Electrical & Control Engineering, Shenyang Jianzhu University, Shenyang, China.

Microscopy Research and Technique

|January 7, 2025

Summary

This study introduces an automatic feature marking method to correct horizontal distortion in atomic force microscope (AFM) images. The novel approach enhances accuracy and efficiency for diverse AFM imaging applications.

Keywords:

AFM edge detection image correction

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Area of Science:

Materials Science
Nanotechnology
Surface Science

Background:

Atomic Force Microscopy (AFM) images suffer from horizontal distortion due to probe-sample tilt angles.
Existing least squares fitting methods for AFM image correction are sensitive to sample topography, impacting accuracy.
Diverse AFM image features can complicate automated distortion correction.

Purpose of the Study:

To develop an automated method for correcting horizontal distortion in AFM images.
To improve the accuracy and efficiency of AFM image leveling and distortion correction.
To create a method adaptable to various types of AFM images.

Main Methods:

Proposed an AFM image level distortion correction method utilizing automatic feature marking.
Employed the Canny edge detection algorithm with an adaptive threshold for feature recognition.
Utilized a hole-filling algorithm to exclude feature data from line fitting, followed by least squares fitting.

Main Results:

Successfully detected and recognized feature graphics in AFM images automatically.
Effectively removed feature graphics data, preventing interference with line fitting correction.
Achieved accurate and efficient correction of horizontal distortion across diverse AFM images.

Conclusions:

The proposed automatic feature marking method significantly improves the accuracy of AFM image distortion correction.
This technique enhances the efficiency of the correction process, making it suitable for real-time applications.
The method demonstrates adaptability to various AFM image types, offering a versatile solution for researchers.