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Strain Modulated Superlattices in Graphene

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arxiv 1903.10468 v3 pith:Q2JNB2GJ submitted 2019-03-25 cond-mat.mes-hall cond-mat.mtrl-sci

classification cond-mat.mes-hallcond-mat.mtrl-sci
keywords straingraphenelengthperiodicdifferentfieldsmagneticdirac
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Strain engineering of graphene takes advantage of one of the most dramatic responses of Dirac electrons enabling their manipulation via strain-induced pseudo-magnetic fields. Numerous theoretically proposed devices, such as resonant cavities and valley filters, as well as novel phenomena, such as snake states, could potentially be enabled via this effect. These proposals, however, require strong, spatially oscillating magnetic fields while to date only the generation and effects of pseudo-gauge fields which vary at a length scale much larger than the magnetic length have been reported. Here we create a periodic pseudo-gauge field profile using periodic strain that varies at the length scale comparable to the magnetic length and study its effects on Dirac electrons. A periodic strain profile is achieved by pulling on graphene with extreme (>10%) strain and forming nanoscale ripples, akin to a plastic wrap pulled taut at its edges. Combining scanning tunneling microscopy and atomistic calculations, we find that spatially oscillating strain results in a new quantization different from the familiar Landau quantization observed in previous studies. We also find that graphene ripples are characterized by large variations in carbon-carbon bond length, directly impacting the electronic coupling between atoms, which within a single ripple can be as different as in two different materials. The result is a single graphene sheet that effectively acts as an electronic superlattice. Our results thus also establish a novel approach to synthesize an effective 2D lateral heterostructure - by periodic modulation of lattice strain.

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

    cond-mat.mes-hall 2019-08 conditional novelty 6.0 of 10

    A graphene strain superlattice made of repeated Gaussian bumps produces valley-polarized conductance plateaus, and a deep neural network can approximate the valley polarization computed by Green's functions.

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