Valley notch filter in a graphene strain superlattice: Green's function and machine learning approach

Author(s)

Torres, V ; Silva, P. ; de Souza, E. A. T. ; Silva, L. A. ; Bahamon, D. A.

• Document type: Journal article
• Source: PHYSICAL REVIEW B; v. 100, n. 20, p. 10-pg., 2019-11-11.

Abstract

The valley transport properties of a superlattice of out-of-plane Gaussian deformations are calculated using a Green's function and a machine learning approach. Our results show that periodicity significantly improves the valley filter capabilities of a single Gaussian deformation; these manifest themselves in the conductance as a sequence by valley filter plateaus. We establish that the physical effect behind the observed valley notch filter is the coupling between counterpropagating transverse modes; the complex relationship between the design parameters of the superlattice and the valley filter effect make it difficult to estimate in advance the valley filter potentialities of a given superlattice. With this in mind, we show that a deep neural network can be trained to predict valley polarization with a precision similar to the Green's function but with much less computational effort. (AU)

• FAPESP's process: 12/50259-8 - Graphene: photonics and opto-electronics: UPM-NUS collaboration
• Grantee: Antonio Helio de Castro Neto
• Support Opportunities: Research Projects - SPEC Program
• FAPESP's process: 18/07276-5 - Mid- and far-infrared plasmonic biosensing with graphene
• Grantee: Christiano José Santiago de Matos
• Support Opportunities: Regular Research Grants
• FAPESP's process: 15/11779-4 - Plasmonic and nonlinear effects in graphene coupled to optical waveguides
• Grantee: Christiano José Santiago de Matos
• Support Opportunities: Research Projects - Thematic Grants