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Ultrafast current imaging by Bayesian inversion

Journal Article · · Nature Communications
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [2];  [4];  [4];  [4]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Inst. for Functional Imaging of Materials; DOE/OSTI
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematics Div.
  3. NAS of Ukraine, Kyiv (Ukraine). Inst. of Physics
  4. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Inst. for Functional Imaging of Materials
  5. Sungkyunkwan Univ., Suwon (Republic of Korea). School of Advanced Materials Science and Engineering
  6. Xidian Univ., Shaanxi (China). The State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology
  7. Univ. of Warwick, Coventry (United Kingdom). Dept. of Physics
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. This general-mode I–V method allows three orders of magnitude faster measurement rates than presently possible. The technique is demonstrated by acquiring I–V curves in ferroelectric nanocapacitors, yielding >100,000 I–V curves in <20 min. This allows detection of switching currents in the nanoscale capacitors, as well as determination of the dielectric constant. These experiments show the potential for the use of full information capture and Bayesian inference toward extracting physics from rapid I–V measurements, and can be used for transport measurements in both atomic force and scanning tunneling microscopy.
Research Organization:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
1624067
Journal Information:
Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 9; ISSN 2041-1723
Publisher:
Nature Publishing GroupCopyright Statement
Country of Publication:
United States
Language:
English

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Ultrafast current imaging by Bayesian inversion
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Cited By (5)

Application of pan-sharpening algorithm for correlative multimodal imaging using AFM-IR journal April 2019
Towards nanoscale electrical measurements in liquid by advanced KPFM techniques: a review journal July 2018
Fast Multifrequency Measurement of Nonlinear Conductance journal April 2019
Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics journal July 2019
Fast multifrequency measurement of nonlinear conductance text January 2018

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