Excited states in bilayer graphene quantum dots

We report on ground- and excited state transport through an electrostatically defined few-hole quantum dot in bilayer graphene in both parallel and perpendicular applied magnetic fields. A remarkably clear level scheme for the two-particle spectra is found by analyzing finite bias spectroscopy data within a two-particle model including spin and valley degrees of freedom. We identify the two-hole ground-state to be a spin-triplet and valley-singlet state. This spin alignment can be seen as Hund's rule for a valley-degenerate system, which is fundamentally different to quantum dots in carbon nano tubes and GaAs-based quantum dots. The spin-singlet excited states are found to be valley-triplet states by tilting the magnetic field with respect to the sample plane. We quantify the exchange energy to be 0.35meV and measure a valley and spin g-factor of 36 and 2, respectively

Combined minivalley and layer control in twisted double bilayer graphene

Control over minivalley polarization and interlayer coupling is demonstrated in double bilayer graphene twisted with an angle of 2.37$^\circ$. This intermediate angle is small enough for the minibands to form and large enough such that the charge carrier gases in the layers can be tuned independently. Using a dual-gated geometry we identify and control all possible combinations of minivalley polarization via the population of the two bilayers. An applied displacement field opens a band gap in either of the two bilayers, allowing us to even obtain full minivalley polarization. In addition, the wavefunctions of the minivalleys are mixed by tuning through a Lifshitz transition, where the Fermi surface topology changes. The high degree of control makes twisted double bilayer graphene a promising platform for valleytronics devices such as valley valves, filters and logic gates.Comment: Supplemental Material included in PD

Correlated electron-hole State in Twisted Double Bilayer Graphene

When twisted to angles near 1◦, graphene multilayers provide a window on electron correlation physics. Here we report the discovery of a correlated electron-hole state in double bilayer graphene twisted to 2.37 ◦. At this angle the moir´e states retain much of their isolated bilayer character, allow- ing their bilayer projections to be separately controlled by gates. We use this property to generate an energetic overlap between narrow isolated electron and hole bands with good nesting properties. Our measurements reveal the formation of ordered states with reconstructed Fermi surfaces, con- sistent with a density-wave state. This state can be tuned without introducing chemical dopants, enabling studies of correlated electron-hole states and their interplay with superconductivity.We acknowledge financial support from the European Graphene Flagship, the Swiss National Science Foundation via NCCR Quantum Science. P. Rickhaus acknowledges financial support from the ETH Fellowship program. Growth of hexagonal boron nitride crystals was supported by the Elemental Strategy Initiative conducted by MEXT, Japan and the CREST (JPMJCR15F3), JST. AHM and JZ were supported by the National Science Foundation through the Center for Dynamics and Control of Materials, an NSF MRSEC under Co- operative Agreement No. DMR-1720595 and by the Welch Foundation under grant TBF1473.Center for Dynamics and Control of Material

Fully Automated Identification of Two-Dimensional Material Samples

Thin nanomaterials are key constituents of modern quantum technologies and materials research. The identification of specimens of these materials with the properties required for the development of state-of-the-art quantum devices is usually a complex and tedious human task. In this work, we provide a neural-network-driven solution that allows for accurate and efficient scanning, data processing, and sample identification of experimentally relevant two-dimensional materials. We show how to approach the classification of imperfect and imbalanced data sets using an iterative application of multiple noisy neural networks. We embed the trained classifier into a comprehensive solution for end-to-end automatized data processing and sample identification