skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: To switch or not to switch – a machine learning approach for ferroelectricity

Abstract

With the advent of increasingly elaborate experimental techniques in physics, chemistry and materials sciences, measured data are becoming bigger and more complex. The observables are typically a function of several stimuli resulting in multidimensional data sets spanning a range of experimental parameters. As an example, a common approach to study ferroelectric switching is to observe effects of applied electric field, but switching can also be enacted by pressure and is influenced by strain fields, material composition, temperature, time, etc. Moreover, the parameters are usually interdependent, so that their decoupling toward univariate measurements or analysis may not be straightforward. On the other hand, both explicit and hidden parameters provide an opportunity to gain deeper insight into the measured properties, provided there exists a well-defined path to capture and analyze such data. Here, we introduce a new, two-dimensional approach to represent hysteretic response of a material system to applied electric field. Utilizing ferroelectric polarization as a model hysteretic property, we demonstrate how explicit consideration of electromechanical response to two rather than one control voltages enables significantly more transparent and robust interpretation of observed hysteresis, such as differentiating between charge trapping and ferroelectricity. Furthermore, we demonstrate how the new data representation readily fitsmore » into a variety of machine-learning methodologies, from unsupervised classification of the origins of hysteretic response via linear clustering algorithms to neural-network-based inference of the sample temperature based on the specific morphology of hysteresis.« less

Authors:
ORCiD logo [1];  [1];  [2]; ORCiD logo [3];  [1]; ORCiD logo [2]; ORCiD logo [1];  [1]
  1. Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, USA
  2. Department of Materials Science and Engineering, University of California, Berkeley, USA, Materials Sciences Division
  3. Department of Physics & CICECO – Aveiro Institute of Materials, University of Aveiro, Aveiro, Portugal, School of Natural Sciences and Mathematics
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division; National Science Foundation (NSF); US Army Research Office (ARO); Fundação para a Ciência e a Tecnologia (FCT)
OSTI Identifier:
1630186
Alternate Identifier(s):
OSTI ID: 1615406; OSTI ID: 1637317; OSTI ID: 1659630
Grant/Contract Number:  
AC02-05CH11231; DMR-1708615; W911NF-14-1-0104; UIDB/50011/2020; UIDP/50011/2020; AC05-00OR22725
Resource Type:
Published Article
Journal Name:
Nanoscale Advances
Additional Journal Information:
Journal Name: Nanoscale Advances Journal Volume: 2 Journal Issue: 5; Journal ID: ISSN 2516-0230
Publisher:
Royal Society of Chemistry
Country of Publication:
United Kingdom
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Neumayer, Sabine M., Jesse, Stephen, Velarde, Gabriel, Kholkin, Andrei L., Kravchenko, Ivan, Martin, Lane W., Balke, Nina, and Maksymovych, Peter. To switch or not to switch – a machine learning approach for ferroelectricity. United Kingdom: N. p., 2020. Web. doi:10.1039/C9NA00731H.
Neumayer, Sabine M., Jesse, Stephen, Velarde, Gabriel, Kholkin, Andrei L., Kravchenko, Ivan, Martin, Lane W., Balke, Nina, & Maksymovych, Peter. To switch or not to switch – a machine learning approach for ferroelectricity. United Kingdom. doi:https://doi.org/10.1039/C9NA00731H
Neumayer, Sabine M., Jesse, Stephen, Velarde, Gabriel, Kholkin, Andrei L., Kravchenko, Ivan, Martin, Lane W., Balke, Nina, and Maksymovych, Peter. Tue . "To switch or not to switch – a machine learning approach for ferroelectricity". United Kingdom. doi:https://doi.org/10.1039/C9NA00731H.
@article{osti_1630186,
title = {To switch or not to switch – a machine learning approach for ferroelectricity},
author = {Neumayer, Sabine M. and Jesse, Stephen and Velarde, Gabriel and Kholkin, Andrei L. and Kravchenko, Ivan and Martin, Lane W. and Balke, Nina and Maksymovych, Peter},
abstractNote = {With the advent of increasingly elaborate experimental techniques in physics, chemistry and materials sciences, measured data are becoming bigger and more complex. The observables are typically a function of several stimuli resulting in multidimensional data sets spanning a range of experimental parameters. As an example, a common approach to study ferroelectric switching is to observe effects of applied electric field, but switching can also be enacted by pressure and is influenced by strain fields, material composition, temperature, time, etc. Moreover, the parameters are usually interdependent, so that their decoupling toward univariate measurements or analysis may not be straightforward. On the other hand, both explicit and hidden parameters provide an opportunity to gain deeper insight into the measured properties, provided there exists a well-defined path to capture and analyze such data. Here, we introduce a new, two-dimensional approach to represent hysteretic response of a material system to applied electric field. Utilizing ferroelectric polarization as a model hysteretic property, we demonstrate how explicit consideration of electromechanical response to two rather than one control voltages enables significantly more transparent and robust interpretation of observed hysteresis, such as differentiating between charge trapping and ferroelectricity. Furthermore, we demonstrate how the new data representation readily fits into a variety of machine-learning methodologies, from unsupervised classification of the origins of hysteretic response via linear clustering algorithms to neural-network-based inference of the sample temperature based on the specific morphology of hysteresis.},
doi = {10.1039/C9NA00731H},
journal = {Nanoscale Advances},
number = 5,
volume = 2,
place = {United Kingdom},
year = {2020},
month = {5}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: https://doi.org/10.1039/C9NA00731H

Figures / Tables:

Fig. 1 Fig. 1: cKPFM on ferroelectric PZT. (a) Schematic of sequence of DC voltage pulses applied during cKPFM. Vwrite pulses are increasing and decreasing in a triangular envelope, whereas the Vread is applied between write pulses and sequentially increased with each cycle. (b) Traditional cKPFM diagram where response Dac is plottedmore » as a function of Vread with Vwrite steps color coded. (c) Hysteresis loop extracted from Vread = 0 as a function of Vwrite (top) and unfolded as a function of Vwrite step #. (d) Unfolded loops for all Vread stacked along a third dimension. These loops are projected on a 2D map where Dac is represented by the color scale, rows correspond to response during a certain Vread and columns correspond to Vwrite steps. (e) cKPFM with decreased Vread window as indicated in the green box in panel (d), (f) cKPFM map with a further decreased the probing window corresponding to the blue box in panel (e). The white dashed line in cKPFM maps in panels (d–f) indicate Vwrite.« less

Save / Share:

Works referenced in this record:

SciPy 1.0: fundamental algorithms for scientific computing in Python
journal, February 2020


SciPy 1.0: fundamental algorithms for scientific computing in Python
journal, February 2020


Dynamic piezoresponse force microscopy: Spatially resolved probing of polarization dynamics in time and voltage domains
journal, September 2012

  • Kumar, A.; Ehara, Y.; Wada, A.
  • Journal of Applied Physics, Vol. 112, Issue 5
  • DOI: 10.1063/1.4746080

High-veracity functional imaging in scanning probe microscopy via Graph-Bootstrapping
journal, June 2018


The giant electromechanical response in ferroelectric relaxors as a critical phenomenon
journal, June 2006


Ferroionic states in ferroelectric thin films
journal, May 2017

  • Morozovska, Anna N.; Eliseev, Eugene A.; Morozovsky, Nicholas V.
  • Physical Review B, Vol. 95, Issue 19
  • DOI: 10.1103/PhysRevB.95.195413

Exploring Local Electrostatic Effects with Scanning Probe Microscopy: Implications for Piezoresponse Force Microscopy and Triboelectricity
journal, October 2014

  • Balke, Nina; Maksymovych, Petro; Jesse, Stephen
  • ACS Nano, Vol. 8, Issue 10
  • DOI: 10.1021/nn505176a

Differentiating Ferroelectric and Nonferroelectric Electromechanical Effects with Scanning Probe Microscopy
journal, May 2015


Sequential piezoresponse force microscopy and the ‘small-data’ problem
journal, June 2018

  • Trivedi, Harsh; Shvartsman, Vladimir V.; Medeiros, Marco S. A.
  • npj Computational Materials, Vol. 4, Issue 1
  • DOI: 10.1038/s41524-018-0084-9

scikit-image: image processing in Python
journal, January 2014

  • van der Walt, Stéfan; Schönberger, Johannes L.; Nunez-Iglesias, Juan
  • PeerJ, Vol. 2
  • DOI: 10.7717/peerj.453

Ionically-Mediated Electromechanical Hysteresis in Transition Metal Oxides
journal, July 2012

  • Kim, Yunseok; Morozovska, Anna N.; Kumar, Amit
  • ACS Nano, Vol. 6, Issue 8
  • DOI: 10.1021/nn3020757

Giant piezoelectric voltage coefficient in grain-oriented modified PbTiO3 material
journal, October 2016

  • Yan, Yongke; Zhou, Jie E.; Maurya, Deepam
  • Nature Communications, Vol. 7, Issue 1
  • DOI: 10.1038/ncomms13089

Ferroelectric Field-Effect Transistors Based on MoS 2 and CuInP 2 S 6 Two-Dimensional van der Waals Heterostructure
journal, June 2018


Piezoresponse force microscopy (PFM)
journal, November 2011


Decoupling Mesoscale Functional Response in PLZT across the Ferroelectric–Relaxor Phase Transition with Contact Kelvin Probe Force Microscopy and Machine Learning
journal, November 2018

  • Neumayer, Sabine M.; Collins, Liam; Vasudevan, Rama
  • ACS Applied Materials & Interfaces, Vol. 10, Issue 49
  • DOI: 10.1021/acsami.8b15872

Smart machine learning or discovering meaningful physical and chemical contributions through dimensional stacking
journal, August 2019

  • Griffin, Lee A.; Gaponenko, Iaroslav; Zhang, Shujun
  • npj Computational Materials, Vol. 5, Issue 1
  • DOI: 10.1038/s41524-019-0222-z

Machine Detection of Enhanced Electromechanical Energy Conversion in PbZr 0.2 Ti 0.8 O 3 Thin Films
journal, May 2018


Mixed electrochemical–ferroelectric states in nanoscale ferroelectrics
journal, May 2017

  • Yang, Sang Mo; Morozovska, Anna N.; Kumar, Rajeev
  • Nature Physics, Vol. 13, Issue 8
  • DOI: 10.1038/nphys4103

Conduction at domain walls in oxide multiferroics
journal, January 2009

  • Seidel, J.; Martin, L. W.; He, Q.
  • Nature Materials, Vol. 8, Issue 3
  • DOI: 10.1038/nmat2373

Solid-state electrochemistry on the nanometer and atomic scales: the scanning probe microscopy approach
journal, January 2016

  • Strelcov, Evgheni; Yang, Sang Mo; Jesse, Stephen
  • Nanoscale, Vol. 8, Issue 29
  • DOI: 10.1039/C6NR01524G

Room-Temperature Electrocaloric Effect in Layered Ferroelectric CuInP 2 S 6 for Solid-State Refrigeration
journal, July 2019


Dynamic Conductivity of Ferroelectric Domain Walls in BiFeO 3
journal, May 2011

  • Maksymovych, Peter; Seidel, Jan; Chu, Ying Hao
  • Nano Letters, Vol. 11, Issue 5
  • DOI: 10.1021/nl104363x

Chemical State Evolution in Ferroelectric Films during Tip-Induced Polarization and Electroresistive Switching
journal, October 2016

  • Ievlev, Anton V.; Maksymovych, Petro; Trassin, Morgan
  • ACS Applied Materials & Interfaces, Vol. 8, Issue 43
  • DOI: 10.1021/acsami.6b10784

Surface Chemistry Controls Anomalous Ferroelectric Behavior in Lithium Niobate
journal, July 2018

  • Neumayer, Sabine M.; Ievlev, Anton V.; Collins, Liam
  • ACS Applied Materials & Interfaces, Vol. 10, Issue 34
  • DOI: 10.1021/acsami.8b09513

Thickness, humidity, and polarization dependent ferroelectric switching and conductivity in Mg doped lithium niobate
journal, December 2015

  • Neumayer, Sabine M.; Strelcov, Evgheni; Manzo, Michele
  • Journal of Applied Physics, Vol. 118, Issue 24
  • DOI: 10.1063/1.4938386

Band Excitation in Scanning Probe Microscopy: Recognition and Functional Imaging
journal, April 2014


Piezoresponse force microscopy and recent advances in nanoscale studies of ferroelectrics
journal, January 2006


Electromechanical Imaging and Spectroscopy of Ferroelectric and Piezoelectric Materials: State of the Art and Prospects for the Future
journal, August 2009


Big data and deep data in scanning and electron microscopies: deriving functionality from multidimensional data sets
journal, May 2015

  • Belianinov, Alex; Vasudevan, Rama; Strelcov, Evgheni
  • Advanced Structural and Chemical Imaging, Vol. 1, Issue 1
  • DOI: 10.1186/s40679-015-0006-6

Designing piezoelectric films for micro electromechanical systems
journal, September 2011

  • Trolier-McKinstry, S.; Griggio, F.; Yaeger, C.
  • IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, Vol. 58, Issue 9
  • DOI: 10.1109/TUFFC.2011.2015

Piezoelectric Pb(Zr x , Ti x )O 3 thin film cantilever and bridge acoustic sensors for miniaturized photoacoustic gas detectors
journal, August 2004

  • Ledermann, Nicolas; Muralt, Paul; Baborowski, Jacek
  • Journal of Micromechanics and Microengineering, Vol. 14, Issue 12
  • DOI: 10.1088/0960-1317/14/12/008

Multiple polarization states in symmetric ferroelectric heterostructures for multi-bit non-volatile memories
journal, January 2017

  • Boni, Georgia A.; Filip, Lucian D.; Chirila, Cristina
  • Nanoscale, Vol. 9, Issue 48
  • DOI: 10.1039/C7NR06354G

Deep neural networks for understanding noisy data applied to physical property extraction in scanning probe microscopy
journal, February 2019

  • Borodinov, Nikolay; Neumayer, Sabine; Kalinin, Sergei V.
  • npj Computational Materials, Vol. 5, Issue 1
  • DOI: 10.1038/s41524-019-0148-5

Ferroelectric or non-ferroelectric: Why so many materials exhibit “ferroelectricity” on the nanoscale
journal, June 2017

  • Vasudevan, Rama K.; Balke, Nina; Maksymovych, Peter
  • Applied Physics Reviews, Vol. 4, Issue 2
  • DOI: 10.1063/1.4979015

Acoustic Detection of Phase Transitions at the Nanoscale
journal, December 2015

  • Vasudevan, Rama K.; Khassaf, Hamidreza; Cao, Ye
  • Advanced Functional Materials, Vol. 26, Issue 4
  • DOI: 10.1002/adfm.201504407

1.6 V Nanogenerator for Mechanical Energy Harvesting Using PZT Nanofibers
journal, June 2010

  • Chen, Xi; Xu, Shiyou; Yao, Nan
  • Nano Letters, Vol. 10, Issue 6
  • DOI: 10.1021/nl100812k

Direct Probing of Charge Injection and Polarization-Controlled Ionic Mobility on Ferroelectric LiNbO 3 Surfaces
journal, November 2013

  • Strelcov, Evgheni; Ievlev, Anton V.; Jesse, Stephen
  • Advanced Materials, Vol. 26, Issue 6
  • DOI: 10.1002/adma.201304002

The Role of Electrochemical Phenomena in Scanning Probe Microscopy of Ferroelectric Thin Films
journal, June 2011

  • Kalinin, Sergei V.; Jesse, Stephen; Tselev, Alexander
  • ACS Nano, Vol. 5, Issue 7
  • DOI: 10.1021/nn2013518

Switching spectroscopy piezoresponse force microscopy of ferroelectric materials
journal, February 2006

  • Jesse, Stephen; Baddorf, Arthur P.; Kalinin, Sergei V.
  • Applied Physics Letters, Vol. 88, Issue 6
  • DOI: 10.1063/1.2172216

Ferroelectric domain wall pinning at a bicrystal grain boundary in bismuth ferrite
journal, October 2008

  • Rodriguez, Brian J.; Chu, Y. H.; Ramesh, R.
  • Applied Physics Letters, Vol. 93, Issue 14
  • DOI: 10.1063/1.2993327

    Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.