Constraining Data Mining with Physical Models: Voltage- and Oxygen Pressure-Dependent Transport in Multiferroic Nanostructures

Strelcov, Evgheni; Belianinov, Alexei; Hsieh, Ying-Hui; Chu, Ying-Hao; Kalinin, Sergei V.

doi:10.1021/acs.nanolett.5b02472

Title: Constraining Data Mining with Physical Models: Voltage- and Oxygen Pressure-Dependent Transport in Multiferroic Nanostructures

Journal Article · Thu Aug 27 00:00:00 EDT 2015 · Nano Letters

DOI:https://doi.org/10.1021/acs.nanolett.5b02472· OSTI ID:1261273

Strelcov, Evgheni ^[1]; Belianinov, Alexei ^[1]; Hsieh, Ying-Hui ^[2]; Chu, Ying-Hao ^[3]; Kalinin, Sergei V. ^[1]

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

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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: 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)

Grant/Contract Number:: AC05-00OR22725; NSC-101-2119-M-009−003-MY2; MOE-ATU 101W961

OSTI ID:: 1261273

Journal Information:: Nano Letters, Vol. 15, Issue 10; ISSN 1530-6984

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 23 works

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Title: Constraining Data Mining with Physical Models: Voltage- and Oxygen Pressure-Dependent Transport in Multiferroic Nanostructures

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