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Title: Exploring Physics of Ferroelectric Domain Walls in Real Time: Deep Learning Enabled Scanning Probe Microscopy

Journal Article · · Advanced Science
ORCiD logo [1];  [1];  [2];  [3];  [4]
  1. Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak Ridge TN 37830 USA
  2. Department of Material Science and Engineering Tokyo Institute of Technology Yokohama 226‐8502 Japan
  3. Department of Materials Science and Engineering University of Tennessee Knoxville TN 37996 USA
  4. Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak Ridge TN 37830 USA, Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge TN 37830 USA

The functionality of ferroelastic domain walls in ferroelectric materials is explored in real-time via the in situ implementation of computer vision algorithms in scanning probe microscopy (SPM) experiment. The robust deep convolutional neural network (DCNN) is implemented based on a deep residual learning framework (Res) and holistically nested edge detection (Hed), and ensembled to minimize the out-of-distribution drift effects. The DCNN is implemented for real-time operations on SPM, converting the data stream into the semantically segmented image of domain walls and the corresponding uncertainty. Further the pre-defined experimental workflows perform piezoresponse spectroscopy measurement on thus discovered domain walls, and alternating high- and low-polarization dynamic (out-of-plane) ferroelastic domain walls in a PbTiO3 (PTO) thin film and high polarization dynamic (out-of-plane) at short ferroelastic walls (compared with long ferroelastic walls) in a lead zirconate titanate (PZT) thin film is reported. This work establishes the framework for real-time DCNN analysis of data streams in scanning probe and other microscopies and highlights the role of out-of-distribution effects and strategies to ameliorate them in real time analytics.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Pennsylvania State Univ., University Park, PA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
Grant/Contract Number:
AC05-00OR22725; SC0021118
OSTI ID:
1885605
Alternate ID(s):
OSTI ID: 1890323; OSTI ID: 1961600; OSTI ID: 1961607; OSTI ID: 1996026
Journal Information:
Advanced Science, Journal Name: Advanced Science Vol. 9 Journal Issue: 31; ISSN 2198-3844
Publisher:
Wiley Blackwell (John Wiley & Sons)Copyright Statement
Country of Publication:
Germany
Language:
English

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