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Needle in a Haystack: Information Recovery in Low Signal-to-Noise Piezoresponse Force Microscopy Data.

Kerisha N Williams1, Henry Shaowu Yuchi2, Gardy Kevin Ligonde3

  • 1School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332-0405, USA.

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Summary

This study introduces a Bayesian matrix completion model to enhance low signal-to-noise ratio data from Piezoresponse Force Microscopy (PFM). This method improves the reliability of ferroelectric material characterization by recovering crucial nanoscale functional responses.

Keywords:
Bayesian modelingmatrix completionpiezoresponse force microscopyscanning probe microscopyuncertainty quantification

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Physics

Background:

  • Piezoresponse Force Microscopy (PFM) is a key technique for studying ferroelectric materials at the nanoscale.
  • PFM measures surface displacement in response to electric voltage, revealing piezoelectric properties.
  • Low signal-to-noise ratio (SNR) is a common challenge, especially at domain walls or during polarization switching, hindering reliable data interpretation.

Purpose of the Study:

  • To develop a novel information recovery framework to improve the quality of PFM data.
  • To enable reliable extraction of nanoscale functional responses from low SNR measurements.
  • To provide a robust method for understanding ferroelectric material characteristics.

Main Methods:

  • Implementation of a Bayesian subspace-based matrix completion model.
  • Application of the framework to recover and extract PFM parameters from low SNR data.
  • Demonstration of the framework's utility in improving information extraction.

Main Results:

  • The proposed framework significantly enhances the quality of extracted PFM parameters.
  • Reliable information recovery is achieved even from datasets with low signal-to-noise ratio.
  • The method facilitates more accurate characterization of ferroelectric materials.

Conclusions:

  • The Bayesian matrix completion model offers an effective solution for overcoming low SNR limitations in PFM.
  • This framework provides valuable insights for material characterization and experimental design.
  • The recovery approach is adaptable to other scanning probe microscopy techniques and correlated measurements with low SNR data.