• 文献标题:   Valley notch filter in a graphene strain superlattice: Green's function and machine learning approach
  • 文献类型:   Article
  • 作  者:   TORRES V, SILVA P, DE SOUZA EAT, SILVA LA, BAHAMON DA
  • 作者关键词:  
  • 出版物名称:   PHYSICAL REVIEW B
  • ISSN:   2469-9950 EI 2469-9969
  • 通讯作者地址:   Univ Prebiteriana Mackenzie
  • 被引频次:   4
  • DOI:   10.1103/PhysRevB.100.205411
  • 出版年:   2019

▎ 摘  要

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.