Journal
ADVANCED SCIENCE
Volume 9, Issue 31, Pages -Publisher
WILEY
DOI: 10.1002/advs.202203957
Keywords
automated experiments; deep convolutional neural network; ferroelastic domain walls; piezoresponse force microscopy
Categories
Funding
- Center for 3D Ferroelectric Microelectronics - U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences [DE-SC0021118]
- INTERSECT Initiative as part of the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory
- U.S. Department of Energy [DE-AC05-00OR22725]
- Oak Ridge National Laboratory's Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy, Office of Science User Facility
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The functionality of ferroelastic domain walls in ferroelectric materials is explored using computer vision algorithms in real-time scanning probe microscopy experiments. A robust deep convolutional neural network (DCNN) is implemented for real-time operations and analysis of data streams, and the dynamic ferroelastic domain walls in different thin films are observed through experiments. This work establishes a framework for real-time data analysis and discusses strategies to mitigate the effects of out-of-distribution.
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.
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