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Register a new userMachine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency
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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.
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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.
Recently, phase-change materials (PCMs) have drawn more attention due to the dynamically tunable optical properties. Here, we investigate the active control of electromagnetically induced transparency (EIT) analogue based on terahertz (THz) metamaterials integrated with vanadium oxide (VO2). Utilizing the insulator-to-metal transition of VO2, the amplitude of EIT peak can be actively modulated with a significant modulation depth. Meanwhile the group delay within the transparent window can also be dynamically tuned, achieving the active control of slow light effect. Furthermore, we also introduce independently tunable transparent peaks as well as group delay based on a double-peak EIT with good tuning performance. Finally, based on broadband EIT, the active tuning of quality factor of the EIT peak is also realized. This work introduces active EIT control with more degree of freedom by employing VO2, and can find potential applications in future wireless and ultrafast THz communication systems as multi-channel filters, switches, spacers, logic gates and modulators.
As an analogue of electromagnetically induced transparency (EIT), plasmon-induced transparency (PIT) has been realized both in plasmonic metamaterial and waveguide structures. Via near-field coupling within unit cells, PIT with broadband could be produced by plasmonic metamaterials, which, however, has not been realized in on-chip plasmonic waveguide structures. Here, we introduce broadband PIT based on a plasmonic metal-insulator-metal (MIM) waveguide system. Utilizing the direct coupling structure, PIT emerges based on an easy-fabricated structure without gap. By tuning coupling distance, the transparent window can be continuously tuned from narrow- to broadband. Such device is promising for on-chip applications on sensing, filtering and slow light over a broad frequency range.
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.
Plasmon-induced transparency (PIT) displays complex nonlinear dynamics that find critical phenomena in areas such as nonlinear waves. However, such a nonlinear solution depends sensitively on the selection of parameters and different potentials in the Schrodinger equation. Despite this complexity, the machine learning community has developed remarkable efficiencies in predicting complicated datasets by regression. Here, we consider a recurrent neural network (RNN) approach to predict the complex propagation of nonlinear solitons in plasmon-induced transparency metamaterial systems with applied potentials bypassing the need for analytical and numerical approaches of a guiding model. We demonstrate the success of this scheme on the prediction of the propagation of the nonlinear solitons solely from a given initial condition and potential. We prove the prominent agreement of results in simulation and prediction by long short-term memory (LSTM) artificial neural networks. The framework presented in this work opens up a new perspective for the application of RNN in quantum systems and nonlinear waves using Schrodinger-type equations, for example, the nonlinear dynamics in cold-atom systems and nonlinear fiber optics.
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