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

Abstract 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.