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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: Jul 5, 2025

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

Precise Surface Profiling at the Nanoscale Enabled by Deep Learning.

Lalith Krishna Samanth Bonagiri^1,2, Zirui Wang³, Shan Zhou^1,3

¹Materials Research Laboratory, University of Illinois, Urbana, Illinois 61801, United States.

|January 22, 2024

Summary

This summary is machine-generated.

Deep learning removes atomic force microscopy tip artifacts from surface topography measurements. This AI approach recovers precise 3D height profiles of nanostructured materials.

Keywords:

atomic force microscopy deep learning machine learning nanoscale imaging surface profiling tip-convolution

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Area of Science:

Materials Science
Nanotechnology
Artificial Intelligence

Background:

Surface topography is crucial for micro/nanostructured materials and biological systems.
Atomic Force Microscopy (AFM) is a key nanoscale surface profiling tool.
AFM suffers from tip convolution, distorting height profiles, especially for sharp features.

Purpose of the Study:

To develop a deep learning (DL) method to eliminate tip convolution in AFM images.
To accurately recover true 3D surface height profiles from AFM data.

Main Methods:

Utilized an image-to-image translation deep learning methodology.
Trained an encoder-decoder based deep convolutional neural network.
Employed datasets of tip-convoluted and deconvoluted AFM image pairs.

Main Results:

The DL network successfully removed tip convolution from AFM topographic images.
Accurate 3D height profiles of nanocorrugated surfaces were recovered.
The method overcomes a significant limitation of traditional AFM analysis.

Conclusions:

Deep learning offers a powerful solution for correcting AFM surface topography data.
This AI-driven approach enhances the precision of nanoscale surface characterization.
The method has broad applicability for analyzing micro- and nanostructured materials.