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Register a new userPlasmon-induced nonlinearity enhancement and homogenization of graphene metasurfaces
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We demonstrate that the effective third-order nonlinear susceptibility of a graphene sheet can be enhanced by more than two orders of magnitude by patterning it into a graphene metasurface. In addition, in order to gain deeper physical insights into this phenomenon, we introduce a novel homogenization method, which is subsequently used to characterize quantitatively this nonlinearity enhancement effect by calculating the effective linear and nonlinear susceptibility of graphene metasurfaces. The accuracy of the proposed homogenization method is demonstrated by comparing its predictions with those obtained from the Kramers-Kronig relations. This work may open up new opportunities to explore novel physics pertaining to nonlinear optical interactions in graphene metasurfaces.
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Topologically protected plasmonic modes located inside topological bandgaps are attracting increasing attention, chiefly due to their robustness against disorder-induced backscattering. Here, we introduce a bilayer graphene metasurface that possesses plasmonic topological valley interface modes when the mirror symmetry of the metasurface is broken by horizontally shifting the lattice of holes of the top layer of the two freestanding graphene layers in opposite directions. In this configuration, light propagation along the domain-wall interface of the bilayer graphene metasurface shows unidirectional features. Moreover, we have designed a molecular sensor based on the topological properties of this metasurface using the fact that the Fermi energy of graphene varies upon chemical doping. This effect induces strong variation of the transmission of the topological guided modes, which can be employed as the underlying working principle of gas sensing devices. Our work opens up new ways of developing robust integrated plasmonic devices for molecular sensing.
Plasmon induced transparency (PIT) effect in a terahertz graphene metamaterial is numerically and theoretically analyzed. The proposed metamaterial comprises of a pair of graphene split ring resonators placed alternately on both sides of a graphene strip of nanometer scale. The PIT effect in the graphene metamaterial is studied for different vertical and horizontal configurations. Our results reveal that there is no PIT effect in the graphene metamaterial when the centers of both the split ring resonators and the graphene strip are collinear to each other. This is a noteworthy feature, as the PIT effect does not vanish for similar configuration in a metal-based metamaterial structure. We have further shown that the PIT effect can be tuned by varying the Fermi energy of graphene layer. A theoretical model using the three level plasmonic system is established in order to validate the numerical results. Our studies could be significant in designing graphene based frequency agile ultra-thin devices for terahertz applications.
We introduce the concept of nonlinear graphene metasurfaces employing the controllable interaction between a graphene layer and a planar metamaterial. Such hybrid metasurfaces support two types of subradiant resonant modes, asymmetric modes of structured metamaterial elements (metamolecules) and graphene plasmons exhibiting strong mutual coupling and avoided dispersion crossing. High tunability of graphene plasmons facilitates strong interaction between the subradiant modes, modifying the spectral position and lifetime of the associated Fano resonances. We demonstrate that strong resonant interaction, combined with the subwavelength localization of plasmons, leads to the enhanced nonlinear response and high efficiency of the second-harmonic generation.
Machine learning and optimization algorithms have been widely applied in the design and optimization for photonic devices. In this article, we briefly review recent progress of this field of research and show some data-driven applications (e.g. spectrum prediction, inverse design and performance optimization) for novel graphene metamaterials (GMs). The structure of the GMs is well-designed to achieve the wideband plasmon induced transparency effect, which is regarded as optimization object and can be theoretically demonstrated by using transfer matrix method. Some classical machine learning algorithms, including k nearest neighbour, decision tree, random forest and artificial neural networks, are utilized to equivalently substitute the numerical simulation in the forward spectrum prediction and complete the inverse design for the GMs. The calculated results demonstrate that all the algorithms are effective and the random forest has advantages in terms of accuracy and training speed. Moreover, the single-objective and multi-objective optimization algorithms are used to achieve steep transmission characteristics by synthetically taking many performance metrics into consideration. The maximum difference between the transmission peaks and dips in the optimized transmission spectrum can reach 0.97. In comparison to previous works, we provide a guidance for intelligent design of photonic devices and advanced materials based on machine learning and evolutionary algorithms.
A hybrid metal-graphene metamaterial (MM) is reported to achieve the active control of the broadband plasmon-induced transparency (PIT) in THz region. The unit cell consists of one cut wire (CW), four U-shape resonators (USRs) and monolayer graphene sheets under the USRs. Via near-field coupling, broadband PIT can be produced through the interference between different modes. Based on different arrangements of graphene positions, not only can we achieve electrically switching the amplitude of broadband PIT, but also can realize modulating the bandwidth of the transparent window. Simultaneously, both the capability and region of slow light can be dynamically tunable. This work provides schemes to manipulate PIT with more degrees of freedom, which will find significant applications in multifunctional THz modulation.
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