Exploring Domain Wall Pinning in Ferroelectrics via Automated High Throughput AFM

TL;DR

This study uses automated high-throughput AFM with machine learning to analyze domain wall pinning in ferroelectric PbTiO₃ films, revealing how local microstructure influences domain wall dynamics and enabling predictive modeling for memory design.

Contribution

Introduces an ML-controlled automated Piezoresponse Force Microscopy workflow to quantify domain wall pinning and dynamics across large areas of ferroelectric films, providing microstructure-specific insights.

Findings

Domain wall displacement depends on local microstructure and field parameters.

Twin boundaries remain pinned up to certain bias levels, while single variant boundaries activate earlier.

Automated workflow enables large-scale, spatially resolved analysis of ferroelectric domain behavior.

Abstract

Domain-wall dynamics in ferroelectric materials are strongly position-dependent since each polar interface is locked into a unique local microstructure. This necessitates spatially resolved studies of the wall-pinning using scanning-probe microscopy techniques. The pinning centers and preexisting domain walls are usually sparse within image plane, precluding the use of dense hyperspectral imaging modes and requiring time-consuming human experimentation. Here, a large area epitaxial PbTiO$_3$ film on cubic KTaO$_3$ were investigated to quantify the electric field driven dynamics of the polar-strain domain structures using ML-controlled automated Piezoresponse Force Microscopy. Analysis of 1500 switching events reveals that domain wall displacement depends not only on field parameters but also on the local ferroelectric-ferroelastic configuration. For example, twin boundaries in polydomains regions like a$_1^-$/$c^+$ $\parallel$ a$_2^-$/$c^-$ stay pinned up to a certain level of bias magnitude and change only marginally as the bias increases from 20V to 30V, whereas single variant boundaries like a$_2^+$/$c^+$ $\parallel$ a$_2^-$/$c^-$ stack are already activated at 20V. These statistics on the possible ferroelectric and ferroelastic wall orientations, together with the automated, high-throughput AFM workflow, can be distilled into a predictive map that links domain configurations to pulse parameters. This microstructure-specific rule set forms the foundation for designing ferroelectric memories.