Automated Experiments of Local Non-linear Behavior in Ferroelectric Materials

TL;DR

This paper presents an automated multimodal imaging experiment to analyze the physical mechanisms behind non-linear electromechanical responses in ferroelectric materials, enhancing understanding of domain behaviors and surface effects.

Contribution

The study introduces an automated experimental approach combined with deep kernel learning to distinguish physical mechanisms causing non-linear responses in ferroelectric films.

Findings

PTO exhibits asymmetric non-linear behavior at domain walls.

Al0.93B0.07N shows high linear responses in well-poled regions.

Deep kernel learning helps identify dominant physical mechanisms.

Abstract

We develop and implement an automated experiment in multimodal imaging to probe structural, chemical, and functional behaviors in complex materials and elucidate the dominant physical mechanisms that control device function. Here the emergence of non-linear electromechanical responses in piezoresponse force microscopy (PFM) is explored. Non-linear responses in PFM can originate from multiple mechanisms, including intrinsic material responses often controlled by domain structure, surface topography that affects the mechanical phenomena at the tip-surface junction, and, potentially, the presence of surface contaminants. Using an automated experiment to probe the origins of non-linear behavior in model ferroelectric lead titanate (PTO) and ferroelectric Al0.93B0.07N films, it was found that PTO showed asymmetric nonlinear behavior across a/c domain walls and a broadened high nonlinear response region around c/c domain walls. In contrast, for Al0.93B0.07N, well-poled regions showed high linear piezoelectric responses paired with low non-linear responses and regions that were multidomain indicated low linear responses and high nonlinear responses. We show that formulating dissimilar exploration strategies in deep kernel learning as alternative hypotheses allows for establishing the preponderant physical mechanisms behind the non-linear behaviors, suggesting that this approach automated experiments can potentially discern between competing physical mechanisms. This technique can also be extended to electron, probe, and chemical imaging.