Auto-3DPFM: Automating Polarization-Vector Mapping at the Nanoscale

TL;DR

Auto-3DPFM is an automated 3D polarization mapping technique for ferroelectric materials that enhances accuracy, reproducibility, and throughput, enabling advanced research and integration with machine learning for materials discovery.

Contribution

It introduces a fully automated interferometric PFM method for 3D polarization vector characterization, including calibration, imaging, and analysis, with capabilities beyond traditional techniques.

Findings

Automates all steps of 3D polarization mapping in ferroelectrics.

Ensures high reproducibility and consistency in polarization measurements.

Enables identification of domain walls and their angles with a new vector-angle-difference method.

Abstract

The functional properties of ferroelectric materials are strongly influenced by ferroelectric polarization orientation; as such, access to consistent and precise characterization of polarization vectors is of substantial importance to ferroelectrics research. Here, we develop a fully automated three-dimensional piezoresponse force microscopy (Auto-3DPFM) technique automating all essential steps in interferometric PFM for 3D polarization vector characterization, including laser alignment, tip calibration and approach, image acquisition, polarization vector reconstruction, and visualization. The automation reduces the experimental burden of ferroelectric polarization vector characterization, while the back-and-forth calibration ensures consistency and reproducibility of 3D polarization reconstruction. An algorithmic workflow is also developed to identify domain walls and calculate their characteristic angles via a spatial vector-angle-difference method, presenting one unique capability enabled by Auto-3DPFM that is not accessible with traditional PFM techniques. Beyond representing a significant step forward in 3D polarization mapping, Auto-3DPFM promises to accelerate discovery via high-throughput and autonomous characterization in ferroelectric materials research. When integrated with machine learning and adaptive sampling strategies in self-driving labs, Auto-3DPFM will serve as a valuable tool for advancing ferroelectric physics and microelectronics development.