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
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-AC05-00OR22725Originating research organization
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)Sponsoring organization
Office of Science (SC)Details
DOI
10.13139/OLCF/1410993Release date
November 20, 2017Dataset
Dataset type
ND Numeric DataSoftware
HDF5 drivers, pycroscopyAcknowledgements
Papers using this dataset are requested to include the following text in their acknowledgements:
*Support for 10.13139/OLCF/1410993 is provided by the U.S. Department of Energy, project STF011 under Contract DE-AC05-00OR22725. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility.
Category
- 36 MATERIALS SCIENCE,
- 97 MATHEMATICS AND COMPUTING
Keywords
- Bayesian Inference,
- current imaging,
- lead zirconium titanate,
- PZT