Deep learning of interface structures from the 4D STEM data: cation intermixing vs. roughening

Abstract

Interface structures in complex oxides remain one of the active areas of condensed matter physics research, largely enabled by recent advances in scanning transmission electron microscopy (STEM). Yet the nature of the STEM contrast in which the structure is projected along the given direction precludes separation of possible structural models. Here, we utilize deep convolutional neural networks (DCNN) trained on simulated 4D scanning transmission electron microscopy (STEM) datasets to predict structural descriptors of interfaces. We focus on the widely studied interface between LaAlO3 and SrTiO3, using dynamical diffraction theory and leveraging high performance computing to simulate thousands of possible 4D STEM datasets to train the DCNN to learn properties of the underlying structures on which the simulations are based. We validate the DCNN on simulated data and show that it is possible (with >95% accuracy) to identify a physically rough from a chemically diffuse interface and achieve 85% accuracy in determination of buried step positions within the interface. The method shown here is general and can be applied for any inverse imaging problem where forward models are present.

arXiv comment18 pages, 4 figures

References in corpus (4)

• Magnetic effects at the interface between nonmagnetic oxides
• Suppression of Octahedral Tilts and Associated Changes of Electronic Properties at Epitaxial Oxide Heterostructure Interfaces
• LaAlO3 stoichiometry found key to electron liquid formation at LaAlO3/SrTiO3 interfaces
• Direct Observation of an Interface Dipole between Two Metallic Oxides Caused by Localized Oxygen Vacancies