Spatially Resolved Mapping of Disorder Type and Distribution in Random Systems using Artificial Neural Network Recognition

Abstract

The spatial variability of the polarization dynamics in thin film ferroelectric capacitors was probed by recognition analysis of spatially-resolved spectroscopic data. Switching spectroscopy piezoresponse force microscopy was used to measure local hysteresis loops and map them on a 2D random-bond, random-field Ising model. A neural-network based recognition approach was utilized to analyze the hysteresis loops and their spatial variability. Strong variability is observed in the polarization dynamics around macroscopic cracks due to the modified local elastic and electric boundary conditions, with most pronounced effect on the length scale of ~100 nm away from the crack.