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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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Picometer-Precision Atomic Position Tracking through Electron Microscopy

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Exploring Domain-Wall Pinning in Ferroelectrics via Automated High-Throughput Atomic Force Microscopy.

Kamyar Barakati¹, Yu Liu¹, Hiroshi Funakubo²

¹Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States.

ACS Applied Materials & Interfaces

|November 17, 2025

Summary

This summary is machine-generated.

Domain-wall movement in ferroelectric materials depends on local structure. Machine learning-controlled microscopy reveals how ferroelectric-ferroelastic configurations influence domain-wall dynamics, enabling predictive design for ferroelectric memories.

Keywords:

automated scanning probe microscopy domain-wall pinning ferroelastic coupling ferroelectric domain walls self-driving laboratories

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Area of Science:

Condensed Matter Physics
Materials Science
Nanotechnology

Background:

Domain-wall dynamics in ferroelectric materials are highly sensitive to local microstructure.
Spatially resolved studies are crucial for understanding wall pinning, but traditional methods are time-consuming.
Sparse pinning centers and domain walls limit dense imaging techniques.

Purpose of the Study:

To quantify electric-field-driven domain-wall dynamics in ferroelectric-ferroelastic structures.
To investigate the influence of local microstructure on domain-wall displacement.
To develop a predictive framework for designing ferroelectric memories based on microstructure-specific rules.

Main Methods:

Utilized machine learning-controlled automated piezoresponse force microscopy (AFM) on a large-area epitaxial PbTiO3 film.
Analyzed 1500 domain switching events to correlate domain-wall displacement with field parameters and local configurations.
Characterized ferroelectric and ferroelastic wall orientations and their response to varying bias.

Main Results:

Domain-wall displacement is dependent on both applied electric field parameters and the local ferroelectric-ferroelastic configuration.
Different domain boundary types exhibit distinct pinning behaviors and activation fields (e.g., twin boundaries vs. single-variant boundaries).
Automated AFM workflow enabled high-throughput statistics crucial for predictive modeling.

Conclusions:

A microstructure-specific rule set linking domain configurations to pulse parameters was established.
This understanding forms the foundation for designing advanced ferroelectric memories.
The developed automated methodology accelerates the characterization of domain-wall dynamics.