Automated Experiments of Local Non-Linear Behavior in Ferroelectric Materials

Yongtao Liu, Kyle P. Kelley, Rama K. Vasudevan, Wanlin Zhu, John Hayden, Jon Paul Maria, Hiroshi Funakubo, Maxim A. Ziatdinov, Susan Trolier-McKinstry, Sergei V. Kalinin

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

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 is developed and implemented. 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 the presence of surface contaminants. Using an automated experiment to probe the origins of non-linear behavior in ferroelectric lead titanate (PTO) and ferroelectric Al0.93B0.07N films, it is found that PTO shows 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 show high linear piezoelectric responses, when paired with low non-linear responses regions that are multidomain show low linear responses and high nonlinear responses. It is shown 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 automated experiments can potentially discern between competing physical mechanisms. This technique can also be extended to electron, probe, and chemical imaging.

Original languageEnglish
Article number2204130
JournalSmall
Volume18
Issue number48
DOIs
StatePublished - Dec 1 2022

Funding

This effort (wurtzite film growth, SPM, data analysis) was supported as part of the center for 3D Ferroelectric Microelectronics (3DFeM), an Energy Frontier Research Center funded by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences under Award Number DE‐SC0021118, and the (workflow development) Oak Ridge National Laboratory's Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy, Office of Science User Facility.

Keywords

  • automated experiments
  • ferroelectrics
  • machine learning
  • non-linearity
  • piezoresponse force microscopy

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