Bayesian inference in band excitation scanning probe microscopy for optimal dynamic model selection in imaging

Vasudevan, Rama K.; Kelley, Kyle P.; Eliseev, Eugene A.; Jesse, Stephen; Funakubo, Hiroshi; Morozovska, Anna N.; Kalinin, Sergei V.

doi:10.1063/5.0005323

Bayesian inference in band excitation scanning probe microscopy for optimal dynamic model selection in imaging

Journal Article · Fri Aug 07 00:00:00 EDT 2020 · Journal of Applied Physics

DOI:https://doi.org/10.1063/5.0005323· OSTI ID:1649334

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Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)
National Academy of Sciences of Ukraine, Kyiv (Ukraine). Inst. for Problems of Materials Science
Tokyo Inst. of Technology, Yokohama (Japan)
National Academy of Sciences of Ukraine, Kyiv (Ukraine). Inst. of Physics

The universal tendency in scanning probe microscopy (SPM) over the last two decades is to transition from simple 2D imaging to complex detection and spectroscopic imaging modes. The emergence of complex SPM engines brings forth the challenge of reliable data interpretation, i.e., conversion from detected signals to descriptors specific to tip–surface interactions and subsequently to material’s properties. In this work, we implemented a Bayesian inference approach for the analysis of the image formation mechanisms in band excitation SPM. Compared to the point estimates in classical functional fit approaches, Bayesian inference allows for the incorporation of extant knowledge of materials and probe behavior in the form of corresponding prior distribution and return the information on the material functionality in the form of readily interpretable posterior distributions. We explore the nonlinear mechanical behaviors spatially in a classical ferroelectric material, PbTiO₃. We observe the non-trivial evolution of the Duffing stiffness term and the nonlinearity of the sample surface, determine spatial clustering of the nonlinear response, and perform a Landau analysis on predicting the nonlinear coefficient, which indicates that ferroelectric behavior can be a cause of the observed results. These observations suggest that the spectrum of anomalous behaviors at the ferroelectric domain walls may be broader than previously believed and can extend to non-conventional mechanical properties in addition to static and microwave conductance.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1649334

Alternate ID(s):: OSTI ID: 1646708

Journal Information:: Journal of Applied Physics, Journal Name: Journal of Applied Physics Journal Issue: 5 Vol. 128; ISSN 0021-8979

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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Bayesian inference in band excitation scanning probe microscopy for optimal dynamic model selection in imaging

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