Journal
SMALL
Volume 18, Issue 48, Pages -Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/smll.202204130
Keywords
automated experiments; ferroelectrics; machine learning; non-linearity; piezoresponse force microscopy
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Funding
- center for 3D Ferroelectric Microelectronics (3DFeM), an Energy Frontier Research Center - U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences [DE-SC0021118]
- Oak Ridge National Laboratory's Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy, Office of Science User Facility
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An automated experiment is developed to investigate structural, chemical, and functional behaviors in complex materials and uncover the primary physical mechanisms that control device function. By exploring non-linear electromechanical responses in piezoresponse force microscopy (PFM), it is discovered that different materials exhibit different non-linear behavior patterns, and automated experiments have the potential to distinguish between competing physical mechanisms.
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.
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