Disorder Identification in Hysteresis Data: Recognition Analysis of the Random-Bond–Random-Field Ising Model

O. S. Ovchinnikov, S. Jesse, P. Bintacchit, S. Trolier-McKinstry, and S. V. Kalinin
Phys. Rev. Lett. 103, 157203 – Published 9 October 2009
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Abstract

An approach for the direct identification of disorder type and strength in physical systems based on recognition analysis of hysteresis loop shape is developed. A large number of theoretical examples uniformly distributed in the parameter space of the system is generated and is decorrelated using principal component analysis (PCA). The PCA components are used to train a feed-forward neural network using the model parameters as targets. The trained network is used to analyze hysteresis loops for the investigated system. The approach is demonstrated using a 2D random-bond–random-field Ising model, and polarization switching in polycrystalline ferroelectric capacitors.

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  • Received 26 May 2009

DOI:https://doi.org/10.1103/PhysRevLett.103.157203

©2009 American Physical Society

Authors & Affiliations

O. S. Ovchinnikov1, S. Jesse2, P. Bintacchit3, S. Trolier-McKinstry3, and S. V. Kalinin2,*

  • 1Department of Physics and Astronomy, University of Tennessee, Knoxville, Tennessee 37996, USA
  • 2Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
  • 3Department of Materials Science and Engineering and Materials Research Institute, Pennsylvania State University, University Park, Pennsylvania 16802, USA

  • *Corresponding author. sergei2@ornl.gov

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Issue

Vol. 103, Iss. 15 — 9 October 2009

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