Estimating Preisach Density via Subset Selection

Xin Li; Dohyung Kim; Sabine M. Neumayer; Mahshid Ahmadi; Sergei V. Kalinin

doi:10.1109/ACCESS.2020.2983364

Estimating Preisach Density via Subset Selection

Xin Li, Dohyung Kim, Sabine M. Neumayer, Mahshid Ahmadi, Sergei V. Kalinin

Functional Atomic Force Microscopy Group

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

Preisach density is drawing increasing attention for interpreting material properties for memory and storage electronics. Preisach density can be linked to the observed hysteresis loops via the Preisach model that is based on the superposition of relay operators. Reconstructing Preisach density from hysteresis is an ill-posed problem with nonunique solutions. To alleviate ambiguities, we address Preisach density reconstruction as a constrained subset selection task utilizing structured sparsity regularizations. We validate our approach under various simulation settings and apply it on experimental band-excitation piezoresponse spectroscopy (BEPS) datasets to gain insights in microstructure-dependent properties of the tip-surface contact.

Original language	English
Article number	9047961
Pages (from-to)	61767-61774
Number of pages	8
Journal	IEEE Access
Volume	8
DOIs	https://doi.org/10.1109/ACCESS.2020.2983364
State	Published - 2020

Funding

This work was supported in part by the U.S. Department of Energy, the Office of Science, the Office of Basic Energy Sciences, the Division of Materials Science and Engineering (S. M. N., S.V. K.), in part by the Energy Frontier Research Centers Program: Center for the Science of Synthesis Across Scales (CSSAS) under Award DE-SC0019288 (X. L.), and in part by the Center for Nanophase Materials Sciences, which is a DOE Office of Science User Facility under user project number CNMS2019-272 (D.K., M.A.).

Keywords

Hysteresis model
band-excitation piezoresponse spectroscopy
preisach density
subset selection

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10.1109/ACCESS.2020.2983364

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abstract = "Preisach density is drawing increasing attention for interpreting material properties for memory and storage electronics. Preisach density can be linked to the observed hysteresis loops via the Preisach model that is based on the superposition of relay operators. Reconstructing Preisach density from hysteresis is an ill-posed problem with nonunique solutions. To alleviate ambiguities, we address Preisach density reconstruction as a constrained subset selection task utilizing structured sparsity regularizations. We validate our approach under various simulation settings and apply it on experimental band-excitation piezoresponse spectroscopy (BEPS) datasets to gain insights in microstructure-dependent properties of the tip-surface contact.",

keywords = "Hysteresis model, band-excitation piezoresponse spectroscopy, preisach density, subset selection",

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AU - Neumayer, Sabine M.

AU - Ahmadi, Mahshid

AU - Kalinin, Sergei V.

PY - 2020

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AB - Preisach density is drawing increasing attention for interpreting material properties for memory and storage electronics. Preisach density can be linked to the observed hysteresis loops via the Preisach model that is based on the superposition of relay operators. Reconstructing Preisach density from hysteresis is an ill-posed problem with nonunique solutions. To alleviate ambiguities, we address Preisach density reconstruction as a constrained subset selection task utilizing structured sparsity regularizations. We validate our approach under various simulation settings and apply it on experimental band-excitation piezoresponse spectroscopy (BEPS) datasets to gain insights in microstructure-dependent properties of the tip-surface contact.

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Estimating Preisach Density via Subset Selection

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