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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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Related Experiment Video

Updated: Dec 4, 2025

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

Published on: April 12, 2014

An automated vertical drift correction algorithm for AFM images based on morphology prediction.

Yinan Wu¹, Yongchun Fang¹, Zhi Fan¹

¹Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, China; Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China.

Micron (Oxford, England : 1993)

|October 23, 2020

Summary

This study introduces a novel morphology prediction algorithm to correct vertical drift in atomic force microscope (AFM) images. The method effectively eliminates image distortion, enhancing AFM imaging applications.

Keywords:

Atomic force microscope Image correction Morphology prediction Vertical drift

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Area of Science:

Materials Science
Nanotechnology
Microscopy

Background:

Atomic Force Microscopy (AFM) is crucial for nanoscale imaging.
Environmental noise causes probe vibrations, leading to vertical drift and image distortion in AFM.
This distortion limits the precision and application scope of AFM.

Purpose of the Study:

To develop and validate a morphology prediction-based algorithm for correcting vertical drift in AFM images.
To improve the accuracy and reliability of AFM imaging by mitigating distortion.
To enable broader applications of AFM by enhancing image quality.

Main Methods:

A Gaussian-Hann filter was designed for initial processing of distorted AFM images.
An adaptive image binarization algorithm was developed for accurate object and background extraction.
An advanced morphology prediction algorithm, including approximation and detail prediction, was implemented using autoregressive models and discrete wavelet transform.

Main Results:

The proposed algorithm effectively eliminates vertical drift in AFM images.
Experimental results demonstrate significant improvement in image quality and accuracy.
The method successfully corrects distortion by utilizing extracted sample substrate information.

Conclusions:

The morphology prediction-based image correction algorithm offers an effective solution for vertical drift in AFM.
This advancement enhances the utility of AFM for precise nanoscale analysis.
The developed technique holds potential for widespread adoption in AFM applications.