Anisotropic Electronic Structure of the Two-Dimensional Electron Gas at the AlOx/KTaO3(110) interface
E. A. Mart\'inez, J. Dai, M. Tallarida, N. M. Nemes, F. Y. Bruno

TL;DR
This study reveals the anisotropic electronic structure and large Rashba splitting of the 2DEG at the KTaO3(110) surface, providing insights into its potential for oxide electronics and spin-orbitronics applications.
Contribution
It presents the first detailed experimental and theoretical analysis of the anisotropic electronic structure and Rashba splitting at the KTaO3(110) 2DEG interface.
Findings
Anisotropic orbital character of the electron-like bands
Elliptical Fermi surface contours with perpendicular major axes
Large Rashba splitting up to 4 meV along [-110] direction
Abstract
Oxide-based two-dimensional electron gases (2DEGs) have generated significant interest due to their potential for discovering novel physical properties. Among these, 2DEGs formed in KTaO3 stand out due to the recently discovered crystal face-dependent superconductivity and large Rashba splitting, both of which hold potential for future oxide electronics devices. In this work, angle-resolved photoemission spectroscopy is used to study the electronic structure of the 2DEG formed at the (110) surface of KTaO3 after deposition of a thin Al layer. Our experiments revealed a remarkable anisotropy in the orbital character of the electron-like dispersive bands, which form a Fermi surface consisting of two elliptical contours with their major axes perpendicular to each other. The measured electronic structure is used to constrain the modeling parameters of self-consistent tight-binding slab…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsElectronic and Structural Properties of Oxides · Magnetic and transport properties of perovskites and related materials · Machine Learning in Materials Science
