  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. We further demonstrate the pre-selected experimental workflows on
thus discovered domain walls, and report 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. 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.

Materials Science