Machine Detection of Enhanced Electromechanical Energy Conversion in PbZr0.2 Ti0.8 O3 Thin Films

© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Bibliographische Detailangaben
Veröffentlicht in:Advanced materials (Deerfield Beach, Fla.). - 1998. - 30(2018), 28 vom: 28. Juli, Seite e1800701
1. Verfasser: Agar, Joshua C (VerfasserIn)
Weitere Verfasser: Cao, Ye, Naul, Brett, Pandya, Shishir, van der Walt, Stéfan, Luo, Aileen I, Maher, Joshua T, Balke, Nina, Jesse, Stephen, Kalinin, Sergei V, Vasudevan, Rama K, Martin, Lane W
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2018
Zugriff auf das übergeordnete Werk:Advanced materials (Deerfield Beach, Fla.)
Schlagworte:Journal Article PZT domain structures ferroelectric materials machine learning scanning-probe microscopy
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520 |a © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. 
520 |a 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 
650 4 |a Journal Article 
650 4 |a PZT 
650 4 |a domain structures 
650 4 |a ferroelectric materials 
650 4 |a machine learning 
650 4 |a scanning-probe microscopy 
700 1 |a Cao, Ye  |e verfasserin  |4 aut 
700 1 |a Naul, Brett  |e verfasserin  |4 aut 
700 1 |a Pandya, Shishir  |e verfasserin  |4 aut 
700 1 |a van der Walt, Stéfan  |e verfasserin  |4 aut 
700 1 |a Luo, Aileen I  |e verfasserin  |4 aut 
700 1 |a Maher, Joshua T  |e verfasserin  |4 aut 
700 1 |a Balke, Nina  |e verfasserin  |4 aut 
700 1 |a Jesse, Stephen  |e verfasserin  |4 aut 
700 1 |a Kalinin, Sergei V  |e verfasserin  |4 aut 
700 1 |a Vasudevan, Rama K  |e verfasserin  |4 aut 
700 1 |a Martin, Lane W  |e verfasserin  |4 aut 
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773 1 8 |g volume:30  |g year:2018  |g number:28  |g day:28  |g month:07  |g pages:e1800701 
856 4 0 |u http://dx.doi.org/10.1002/adma.201800701  |3 Volltext 
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