Keywords: Signal processing, Matrix Completion, Piezeresponse Force Microscopy Data, Polarization Switching
TL;DR: We propose a new approach to tackle unreliable points in PFM data which are critical to understanding polarization switching in material characterization, and it shows potential in improved qualitative results in functional model extraction.
Abstract: Piezoresponse force microscopy (PFM) is a scanning microscopy technique that is used to evaluate the nanoscale strain response to an electric voltage applied to the surface of a ferroelectric material. PFM is a powerful tool for imaging, manipulation, and studying the nanoscale functional response of ferroelectric materials, which has been extensively used as a first pass test for ferroelectricity in novel materials with unknown functional properties. However, low signal-to-noise ratio observations arising from the loss of electromechanical signal during polarization switching often result in unreliable information extraction at these observations, hampering our understanding of the material characteristics. To address this challenge, we propose an information recovery framework utilizing subspace-based matrix completion to achieve improved characterization from PFM data. It enables us to efficiently recover and extract reliable information from the data, assisting the modeling efforts for PFM and providing insights for characterization and experimentation practices.
Paper Track: Papers
Submission Category: Automated Material Characterization