Ultrafast current imaging by Bayesian inversion

Dataset · Fri Jan 01 04:00:00 EST 2016

DOI:https://doi.org/10.13139/OLCF/1410993· OSTI ID:1410993

Somnath, Suhas ^[1]; Law, Kody J. H.; Morozovska, Anna; Maksymovych, Petro; Kim, Yunseok; Lu, Xiaoli; Alexe, Marin; Archibald, Richard K; Kalinin, Sergei V; Jesse, Stephen; Vasudevan, Rama K

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1. ORNL-OLCF

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 >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

• Research Organization: Oak Ridge Leadership Computing Facility; Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: Office of Science (SC)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1410993
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE
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