Constraining Data Mining with Physical Models: Voltage- and Oxygen Pressure-Dependent Transport in Multiferroic Nanostructures
Abstract
Development of new generation electronic devices requires understanding and controlling the electronic transport in ferroic, magnetic, and optical materials, which is hampered by two factors. First, the complications of working at the nanoscale, where interfaces, grain boundaries, defects, and so forth, dictate the macroscopic characteristics. Second, the convolution of the response signals stemming from the fact that several physical processes may be activated simultaneously. Here, we present a method of solving these challenges via a combination of atomic force microscopy and data mining analysis techniques. Rational selection of the latter allows application of physical constraints and enables direct interpretation of the statistically significant behaviors in the framework of the chosen physical model, thus distilling physical meaning out of raw data. We demonstrate our approach with an example of deconvolution of complex transport behavior in a bismuth ferrite–cobalt ferrite nanocomposite in ambient and ultrahigh vacuum environments. Measured signal is apportioned into four electronic transport patterns, showing different dependence on partial oxygen and water vapor pressure. These patterns are described in terms of Ohmic conductance and Schottky emission models in the light of surface electrochemistry. Finally and furthermore, deep data analysis allows extraction of local dopant concentrations and barrier heights empowering ourmore »
- Authors:
-
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Inst. for Functional Imaging of Materials and Center for Nanophase Materials Sciences
- National Chiao Tung Univ., Hsinchu (Taiwan). Dept. of Materials Science and Engineering
- National Chiao Tung Univ., Hsinchu (Taiwan). Dept. of Materials Science and Engineering; Academia Sinica, Taipei (Taiwan). Inst. of Physics
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Council, R.O.C (Taiwan); Ministry of Education (Taiwan); National Chiao Tung Univ. (Taiwan)
- OSTI Identifier:
- 1261273
- Grant/Contract Number:
- AC05-00OR22725; NSC-101-2119-M-009−003-MY2; MOE-ATU 101W961
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nano Letters
- Additional Journal Information:
- Journal Volume: 15; Journal Issue: 10; Journal ID: ISSN 1530-6984
- Publisher:
- American Chemical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; Bayesian linear unmixing; Bismuth ferrite; cobalt ferrite; FORC-IV; multivariate analysis; oxide heterostructures
Citation Formats
Strelcov, Evgheni, Belianinov, Alexei, Hsieh, Ying-Hui, Chu, Ying-Hao, and Kalinin, Sergei V. Constraining Data Mining with Physical Models: Voltage- and Oxygen Pressure-Dependent Transport in Multiferroic Nanostructures. United States: N. p., 2015.
Web. doi:10.1021/acs.nanolett.5b02472.
Strelcov, Evgheni, Belianinov, Alexei, Hsieh, Ying-Hui, Chu, Ying-Hao, & Kalinin, Sergei V. Constraining Data Mining with Physical Models: Voltage- and Oxygen Pressure-Dependent Transport in Multiferroic Nanostructures. United States. https://doi.org/10.1021/acs.nanolett.5b02472
Strelcov, Evgheni, Belianinov, Alexei, Hsieh, Ying-Hui, Chu, Ying-Hao, and Kalinin, Sergei V. Thu .
"Constraining Data Mining with Physical Models: Voltage- and Oxygen Pressure-Dependent Transport in Multiferroic Nanostructures". United States. https://doi.org/10.1021/acs.nanolett.5b02472. https://www.osti.gov/servlets/purl/1261273.
@article{osti_1261273,
title = {Constraining Data Mining with Physical Models: Voltage- and Oxygen Pressure-Dependent Transport in Multiferroic Nanostructures},
author = {Strelcov, Evgheni and Belianinov, Alexei and Hsieh, Ying-Hui and Chu, Ying-Hao and Kalinin, Sergei V.},
abstractNote = {Development of new generation electronic devices requires understanding and controlling the electronic transport in ferroic, magnetic, and optical materials, which is hampered by two factors. First, the complications of working at the nanoscale, where interfaces, grain boundaries, defects, and so forth, dictate the macroscopic characteristics. Second, the convolution of the response signals stemming from the fact that several physical processes may be activated simultaneously. Here, we present a method of solving these challenges via a combination of atomic force microscopy and data mining analysis techniques. Rational selection of the latter allows application of physical constraints and enables direct interpretation of the statistically significant behaviors in the framework of the chosen physical model, thus distilling physical meaning out of raw data. We demonstrate our approach with an example of deconvolution of complex transport behavior in a bismuth ferrite–cobalt ferrite nanocomposite in ambient and ultrahigh vacuum environments. Measured signal is apportioned into four electronic transport patterns, showing different dependence on partial oxygen and water vapor pressure. These patterns are described in terms of Ohmic conductance and Schottky emission models in the light of surface electrochemistry. Finally and furthermore, deep data analysis allows extraction of local dopant concentrations and barrier heights empowering our understanding of the underlying dynamic mechanisms of resistive switching.},
doi = {10.1021/acs.nanolett.5b02472},
journal = {Nano Letters},
number = 10,
volume = 15,
place = {United States},
year = {Thu Aug 27 00:00:00 EDT 2015},
month = {Thu Aug 27 00:00:00 EDT 2015}
}
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