Machine Detection of Enhanced Electromechanical Energy Conversion in PbZr0.2Ti0.8O3 Thin Films
Abstract
Many energy conversion, sensing, and microelectronic applications based on ferroic materials are determined by the domain structure evolution under applied stimuli. New hyperspectral, multidimensional spectroscopic techniques now probe dynamic responses at relevant length and time scales to provide an understanding of how these nanoscale domain structures impact macroscopic properties. Such approaches, however, remain limited in use because of the difficulties that exist in extracting and visualizing scientific insights from these complex datasets. Using multidimensional band‐excitation scanning probe spectroscopy and adapting tools from both computer vision and machine learning, an automated workflow is developed to featurize, detect, and classify signatures of ferroelectric/ferroelastic switching processes in complex ferroelectric domain structures. This approach enables the identification and nanoscale visualization of varied modes of response and a pathway to statistically meaningful quantification of the differences between those modes. Among other things, the importance of domain geometry is spatially visualized for enhancing nanoscale electromechanical energy conversion.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 28, 2018
- Source ID
- 10.1002/adma.201800701
Entities
People
- Aileen I. Luo
- Brett Naul
- Joshua C Agar
- Joshua T. Maher
- Lane W Martin
- Nina Balke
- Rama K. Vasudevan
- Sergei V. Kalinin
- Shishir Pandya
- Stephen Jesse
- Stéfan J. van der Walt
- Ye Cao
Organizations
- Army Research Office
- Gordon and Betty Moore Foundation
- Lawrence Berkeley National Laboratory
- National Science Foundation
- Oak Ridge National Laboratory
- United States Department of Energy
- University of California
- University of Texas at Austin