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Abstract The recent realization of ferroelectricity in scandium‐ and boron‐substituted AlN thin films has spurred tremendous research interests. Here we established a molecular dynamics simulation framework to model the ferroelectricity of AlN thin films. Through reparameterization of Vashishta potential for AlN, the coercive field strength and the AlN polarization were found to be close to experimental values. Furthermore, we examined the effects of film thickness, temperature, in‐plane strain on polarization‐electric field hysteresis loop, and the thickness‐dependent Curie temperature. Lastly, we incorporated electrodes towards atomic‐level modeling of ferroelectric device, by considering the induced charge at the interface between electrodes and ferroelectric film. We found that low dielectric contrast significantly lowers the coercive field for switching AlN.
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To switch or not to switch – a machine learning approach for ferroelectricity
With the advent of increasingly elaborate experimental techniques in physics, chemistry and materials sciences, measured data are becoming bigger and more complex. The observables are typically a function of several stimuli resulting in multidimensional data sets spanning a range of experimental parameters. As an example, a common approach to study ferroelectric switching is to observe effects of applied electric field, but switching can also be enacted by pressure and is influenced by strain fields, material composition, temperature, time, etc. Moreover, the parameters are usually interdependent, so that their decoupling toward univariate measurements or analysis may not be straightforward. On the other hand, both explicit and hidden parameters provide an opportunity to gain deeper insight into the measured properties, provided there exists a well-defined path to capture and analyze such data. Here, we introduce a new, two-dimensional approach to represent hysteretic response of a material system to applied electric field. Utilizing ferroelectric polarization as a model hysteretic property, we demonstrate how explicit consideration of electromechanical response to two rather than one control voltages enables significantly more transparent and robust interpretation of observed hysteresis, such as differentiating between charge trapping and ferroelectricity. Furthermore, we demonstrate how the new data representation readily fits into a variety of machine-learning methodologies, from unsupervised classification of the origins of hysteretic response via linear clustering algorithms to neural-network-based inference of the sample temperature based on the specific morphology of hysteresis.
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- Award ID(s):
- 1708615
- PAR ID:
- 10227259
- Date Published:
- Journal Name:
- Nanoscale Advances
- Volume:
- 2
- Issue:
- 5
- ISSN:
- 2516-0230
- Page Range / eLocation ID:
- 2063 to 2072
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/c9na00731h
