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Information Recovery via Matrix Completion for Piezoresponse Force Microscopy Data
Henry Yuchi · Kerisha Williams · Gardy Ligonde · Matthew Repasky · Yao Xie · Nazanin Bassiri-Gharb
Event URL: https://openreview.net/forum?id=_QNr9Y8oYx »

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.

Author Information

Henry Yuchi (Georgia Institute of Technology)
Kerisha Williams (Georgia Institute of Technology)
Gardy Ligonde
Matthew Repasky (Georgia Institute of Technology)
Yao Xie (Georgia Institute of Technology)

Yao Xie is an Assistant Professor and Harold R. and Mary Anne Nash Early Career Professor in the H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, which she joined in 2013. She received her Ph.D. in Electrical Engineering (minor in Mathematics) from Stanford University in 2011, M.Sc. in Electrical and Computer Engineering from the University of Florida, and B.Sc. in Electrical Engineering and Computer Science from University of Science and Technology of China (USTC) . From 2012 to 2013, she was a Research Scientist at Duke University. Her research areas include statistics, signal processing, and machine learning, in providing theoretical foundation as well as developing computationally efficient and statistically powerful algorithms for big data in various applications such as sensor networks, imaging, and crime data analysis. She received the National Science Foundation CAREER Award in 2017 and her crime data analytics project received the Smart 50 Award at the Smart Cities Connect Conferences and Expo in 2018.

Nazanin Bassiri-Gharb (Georgia Tech Research Corporation)

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