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, S. V. Kalinin

Research output: Contribution to journalArticle

23 Scopus citations

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.

Original languageEnglish (US)
Article number157203
JournalPhysical Review Letters
Volume103
Issue number15
DOIs
StatePublished - Oct 9 2009

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

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