5 research outputs found
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Research data supporting Unsupervised machine learning applied to scanning precession electron diffraction data
The research presented in the publication is an investigation into the the application of the multivariate analysis methods of linear decomposition and data clustering to scanning precession electron diffraction data. Supporting this are several experimental, simulated, and model datasets.
The experimental data comprises several scanning precession electron diffraction (SPED) datasets of a nanowire of gallium arsenide. SPED is a TEM technique where a narrow beam is used to collect a diffraction pattern from every point in a sample scan. Optionally, the beam is precessed - rocked about a double cone above and below the sample. Two samples were studied with various precession angles, detailed in the file names and the metadata associated with each file.
The simulated data comprises three multislice simulations performed for the gallium arsenide crystal structure at three precession angles.
The model data comprises two simple datasets with basic features mimicking diffraction patterns
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Contact-free reversible switching of improper ferroelectric domains by electron and ion irradiation
Focused ion beam (FIB) and scanning electron microscopy (SEM) are used to reversibly switch improper ferroelectric domains in the hexagonal manganite ErMnO3. Surface charging is achieved by local ion (positive charging) and electron (positive and negative charging) irradiation, which allows controlled polarization switching without the need for electrical contacts. Polarization cycling reveals that the domain walls tend to return to the equilibrium configuration obtained in the as-grown state. The response of sub-surface domains is studied by FIB cross-sectioning, enabling imaging in the direction perpendicular to the applied electric field. The results clarify how the polarization reversal in hexagonal manganites progresses at the level of domains, resolving both domain wall movements and the nucleation and growth of new domains. Our FIB-SEM based switching approach is applicable to all ferroelectrics where a sufficiently large electric field can be built up via surface charging, facilitating contact-free high-resolution studies of the domain and domain wall response to electric fields in 3D
Electron energy loss spectroscopy investigation of Pb and Ti hybridization with O at the PbTiO(3)/SrTiO(3) interface
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Application of a long short-term memory for deconvoluting conductance contributions at charged ferroelectric domain walls
Ferroelectric domain walls are promising quasi-2D structures that can be leveraged for miniaturization of electronics components and new mechanisms to control electronic signals at the nanoscale. Despite the significant progress in experiment and theory, however, most investigations on ferroelectric domain walls are still on a fundamental level, and reliable characterization of emergent transport phenomena remains a challenging task. Here, we apply a neural-network-based approach to regularize local I(V)-spectroscopy measurements and improve the information extraction, using data recorded at charged domain walls in hexagonal (Er0.99,Zr0.01)MnO3 as an instructive example. Using a sparse long short-term memory autoencoder, we disentangle competing conductivity signals both spatially and as a function of voltage, facilitating a less biased, unconstrained and more accurate analysis compared to a standard evaluation of conductance maps. The neural-network-based analysis allows us to isolate extrinsic signals that relate to the tip-sample contact and separating them from the intrinsic transport behavior associated with the ferroelectric domain walls in (Er0.99,Zr0.01)MnO3. Our work expands machine-learning-assisted scanning probe microscopy studies into the realm of local conductance measurements, improving the extraction of physical conduction mechanisms and separation of interfering current signals