2 research outputs found
Constraining Data Mining with Physical Models: Voltage- and Oxygen Pressure-Dependent Transport in Multiferroic Nanostructures
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
Deep Data Analysis of Conductive Phenomena on Complex Oxide Interfaces: Physics from Data Mining
Spatial variability of electronic transport in BiFeO<sub>3</sub>–CoFe<sub>2</sub>O<sub>4</sub> (BFO–CFO) self-assembled heterostructures is explored using spatially resolved first-order reversal curve (FORC) current voltage (IV) mapping. Multivariate statistical analysis of FORC-IV data classifies statistically significant behaviors and maps characteristic responses spatially. In particular, regions of grain, matrix, and grain boundary responses are clearly identified. <i>k</i>-Means and Bayesian demixing analysis suggest the characteristic response be separated into four components, with hysteretic-type behavior localized at the BFO–CFO tubular interfaces. The conditions under which Bayesian components allow direct physical interpretation are explored, and transport mechanisms at the grain boundaries and individual phases are analyzed. This approach conjoins multivariate statistical analysis with physics-based interpretation, actualizing a robust, universal, data-driven approach to problem solving, which can be applied to exploration of local transport and other functional phenomena in other spatially inhomogeneous systems