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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.
Affiliation
  • Strelcov E; Institute for Functional Imaging of Materials and Center for Nanophase Materials Sciences, Oak Ridge National Laboratory , Oak Ridge, Tennessee 37831, United States.
  • Belianinov A; Institute for Functional Imaging of Materials and Center for Nanophase Materials Sciences, Oak Ridge National Laboratory , Oak Ridge, Tennessee 37831, United States.
  • Hsieh YH; Department of Materials Science and Engineering, National Chiao Tung University , Hsinchu 30010, Taiwan.
  • Chu YH; Department of Materials Science and Engineering, National Chiao Tung University , Hsinchu 30010, Taiwan.
  • Kalinin SV; Institute of Physics, Academia Sinica , Taipei 105, Taiwan.
Nano Lett ; 15(10): 6650-7, 2015 Oct 14.
Article in En | MEDLINE | ID: mdl-26312554
ABSTRACT
Development of new generation electronic devices necessitates 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. Furthermore, deep data analysis allows extraction of local dopant concentrations and barrier heights empowering our understanding of the underlying dynamic mechanisms of resistive switching.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Nano Lett Year: 2015 Document type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Nano Lett Year: 2015 Document type: Article Affiliation country: United States