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.
Vasudevan, Rama K., et al. "Bayesian inference in band excitation scanning probe microscopy for optimal dynamic model selection in imaging." Journal of Applied Physics, vol. 128, no. 5, Aug. 2020. https://doi.org/10.1063/5.0005323
Vasudevan, Rama K., Kelley, Kyle P., Eliseev, Eugene A., Jesse, Stephen, Funakubo, Hiroshi, Morozovska, Anna N., & Kalinin, Sergei V. (2020). Bayesian inference in band excitation scanning probe microscopy for optimal dynamic model selection in imaging. Journal of Applied Physics, 128(5). https://doi.org/10.1063/5.0005323
Vasudevan, Rama K., Kelley, Kyle P., Eliseev, Eugene A., et al., "Bayesian inference in band excitation scanning probe microscopy for optimal dynamic model selection in imaging," Journal of Applied Physics 128, no. 5 (2020), https://doi.org/10.1063/5.0005323
@article{osti_1649334,
author = {Vasudevan, Rama K. and Kelley, Kyle P. and Eliseev, Eugene A. and Jesse, Stephen and Funakubo, Hiroshi and Morozovska, Anna N. and Kalinin, Sergei V.},
title = {Bayesian inference in band excitation scanning probe microscopy for optimal dynamic model selection in imaging},
annote = {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.},
doi = {10.1063/5.0005323},
url = {https://www.osti.gov/biblio/1649334},
journal = {Journal of Applied Physics},
issn = {ISSN 0021-8979},
number = {5},
volume = {128},
place = {United States},
publisher = {American Institute of Physics (AIP)},
year = {2020},
month = {08}}
PHYSICS OF CANCER: INTERDISCIPLINARY PROBLEMS AND CLINICAL APPLICATIONS (PC’16): Proceedings of the International Conference on Physics of Cancer: Interdisciplinary Problems and Clinical Applications 2016, AIP Conference Proceedingshttps://doi.org/10.1063/1.4960276