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Ultrafast current imaging by Bayesian inversion

  • Somnath, Suhas | Oak Ridge National Laboratory
  • Law, Kody J. H. | Oak Ridge National Laboratory
  • Morozovska, Anna | Oak Ridge National Laboratory
  • Maksymovych, Petro | Oak Ridge National Laboratory
  • Kim, Yunseok | Oak Ridge National Laboratory
  • Lu, Xiaoli | Oak Ridge National Laboratory
  • Alexe, Marin | Oak Ridge National Laboratory
  • Archibald, Richard K | Oak Ridge National Laboratory
  • Kalinin, Sergei V | Oak Ridge National Laboratory
  • Jesse, Stephen | Oak Ridge National Laboratory
  • Vasudevan, Rama K | Oak Ridge National Laboratory
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Overview

Description

Spectroscopic measurements of current-voltage curves in scanning probe microscopy is the earliest and one of the most common methods for characterizing local energy-dependent electronic properties, providing insight into superconductive, semiconductor, and memristive behaviors. However, the quasistatic nature of these measurements renders them extremely slow. Here, we demonstrate a fundamentally new approach for dynamic spectroscopic current imaging via full information capture and Bayesian inference analysis. This "general-mode I-V" method allows three orders of magnitude faster rates than presently possible. The technique is demonstrated by acquiring I-V curves in ferroelectric nanocapacitors, yielding greater than 100,000 I-V curves in 20 minutes. This allows detection of switching currents in the nanoscale capacitors, as well as determination of dielectric constant. These experiments show the potential for the use of full information capture and Bayesian inference towards extracting physics from rapid I-V measurements, and can be used for transport measurements in both atomic force and scanning tunneling microscopy. The data was analyzed using pycroscopy - an open-source python package available at https://github.com/pycroscopy/pycroscopy

Funding resources

DOE contract number

DE-AC05-00OR22725

Originating research organization

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring organization

Office of Science (SC)

Details

DOI

10.13139/OLCF/1410993

Release date

November 20, 2017

Dataset

Dataset type

ND Numeric Data

Software

HDF5 drivers, pycroscopy

Acknowledgements

Users should acknowledge the OLCF in all publications and presentations that speak to work performed on OLCF resources:

This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

Category

  • 36 MATERIALS SCIENCE,
  • 97 MATHEMATICS AND COMPUTING

Keywords

  • Bayesian Inference,
  • current imaging,
  • lead zirconium titanate,
  • PZT