Abstract

The layer-resolved quantum transport response of a twisted bilayer graphene device is investigated by driving a current through the bottom layer and measuring the induced voltage in the top layer. Devices with four- and eight-layer differentiated contacts were analyzed, revealing that in a nanoribbon geometry (four contacts), a longitudinal counterflow current emerges in the top layer, while in a square-junction configuration (eight contacts), this counterflow is accompanied by a transverse, or Hall, component. These effects persist despite weak coupling to contacts, onsite disorder, lattice relaxation and variations in device size. The observed counterflow response indicates a circulating interlayer current, which generates an in-plane magnetic moment excited by the injected current. Finally, due to the intricate relationship between the electrical layer response, energy, and twist angle, a clusterized machine learning model was trained, validated, and tested to predict various conductances.

Abstract

The layer-resolved quantum transport response of a twisted bilayer graphene device is investigated by driving a current through the bottom layer and measuring the induced voltage in the top layer. Devices with four- and eight-layer differentiated contacts were analyzed, revealing that in a nanoribbon geometry (four contacts), a longitudinal counterflow current emerges in the top layer, while in a square-junction configuration (eight contacts), this counterflow is accompanied by a transverse, or Hall, component. These effects persist despite weak coupling to contacts, onsite disorder, lattice relaxation and variations in device size. The observed counterflow response indicates a circulating interlayer current, which generates an in-plane magnetic moment excited by the injected current. Finally, due to the intricate relationship between the electrical layer response, energy, and twist angle, a clusterized machine learning model was trained, validated, and tested to predict various conductances.

Abstract

The layer-resolved quantum transport response of a twisted bilayer graphene device is investigated by driving a current through the bottom layer and measuring the induced voltage in the top layer. Devices with four- and eight-layer differentiated contacts were analyzed, revealing that in a nanoribbon geometry (four contacts), a longitudinal counterflow current emerges in the top layer, while in a square-junction configuration (eight contacts), this counterflow is accompanied by a transverse, or Hall, component. These effects persist despite weak coupling to contacts, onsite disorder, lattice relaxation and variations in device size. The observed counterflow response indicates a circulating interlayer current, which generates an in-plane magnetic moment excited by the injected current. Finally, due to the intricate relationship between the electrical layer response, energy, and twist angle, a clusterized machine learning model was trained, validated, and tested to predict various conductances.

Abstract

The layer-resolved quantum transport response of a twisted bilayer graphene device is investigated by driving a current through the bottom layer and measuring the induced voltage in the top layer. Devices with four- and eight-layer differentiated contacts were analyzed, revealing that in a nanoribbon geometry (four contacts), a longitudinal counterflow current emerges in the top layer, while in a square-junction configuration (eight contacts), this counterflow is accompanied by a transverse, or Hall, component. These effects persist despite weak coupling to contacts, onsite disorder, lattice relaxation and variations in device size. The observed counterflow response indicates a circulating interlayer current, which generates an in-plane magnetic moment excited by the injected current. Finally, due to the intricate relationship between the electrical layer response, energy, and twist angle, a clusterized machine learning model was trained, validated, and tested to predict various conductances.