Spatially resolved mapping of disorder type and distribution in random systems using artificial neural network recognition

A. Kumar, O. Ovchinnikov, S. Guo, F. Griggio, S. Jesse, S. Trolier-Mckinstry, S. V. Kalinin

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

The spatial variability of the polarization dynamics in thin film ferroelectric capacitors was probed by recognition analysis of spatially resolved spectroscopic data. Switching spectroscopy piezoresponse force microscopy (SSPFM) was used to measure local hysteresis loops and map them on a two dimensional (2D) random-bond, random-field Ising model. A neural-network based recognition approach was utilized to analyze the hysteresis loops and their spatial variability. Strong variability is observed in the polarization dynamics around macroscopic cracks because of the modified local-elastic and electric-boundary conditions, with the most pronounced effect on the length scale of ∼100 nm away from the crack. The recognition approach developed here is universal and can potentially be applied for arbitrary macroscopic and spatially resolved data, including temperature- and field-dependent hysteresis, I-V curve mapping, electron microscopy electron energy loss spectroscopy (EELS) imaging, and many others.

Original languageEnglish (US)
Article number024203
JournalPhysical Review B - Condensed Matter and Materials Physics
Volume84
Issue number2
DOIs
StatePublished - Jul 6 2011

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Hysteresis loops
hysteresis
disorders
Polarization
Cracks
Neural networks
Ferroelectric thin films
Ising model
Electron energy loss spectroscopy
cracks
Electron microscopy
Hysteresis
Microscopic examination
Capacitors
Boundary conditions
Spectroscopy
polarization
Imaging techniques
spectroscopy
electron microscopy

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

Cite this

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title = "Spatially resolved mapping of disorder type and distribution in random systems using artificial neural network recognition",
abstract = "The spatial variability of the polarization dynamics in thin film ferroelectric capacitors was probed by recognition analysis of spatially resolved spectroscopic data. Switching spectroscopy piezoresponse force microscopy (SSPFM) was used to measure local hysteresis loops and map them on a two dimensional (2D) random-bond, random-field Ising model. A neural-network based recognition approach was utilized to analyze the hysteresis loops and their spatial variability. Strong variability is observed in the polarization dynamics around macroscopic cracks because of the modified local-elastic and electric-boundary conditions, with the most pronounced effect on the length scale of ∼100 nm away from the crack. The recognition approach developed here is universal and can potentially be applied for arbitrary macroscopic and spatially resolved data, including temperature- and field-dependent hysteresis, I-V curve mapping, electron microscopy electron energy loss spectroscopy (EELS) imaging, and many others.",
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Spatially resolved mapping of disorder type and distribution in random systems using artificial neural network recognition. / Kumar, A.; Ovchinnikov, O.; Guo, S.; Griggio, F.; Jesse, S.; Trolier-Mckinstry, S.; Kalinin, S. V.

In: Physical Review B - Condensed Matter and Materials Physics, Vol. 84, No. 2, 024203, 06.07.2011.

Research output: Contribution to journalArticle

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AU - Ovchinnikov, O.

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AU - Jesse, S.

AU - Trolier-Mckinstry, S.

AU - Kalinin, S. V.

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