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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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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.
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.
Coherent interaction of laser radiation with multilevel atoms and molecules can lead to quantum interference in the electronic excitation pathways. A prominent example observed in atomic three-level-systems is the phenomenon of electromagnetically induced transparency (EIT), in which a control laser induces a narrow spectral transparency window for a weak probe laser beam. The concomitant rapid variation of the refractive index in this spectral window can give rise to dramatic reduction of the group velocity of a propagating pulse of probe light. Dynamic control of EIT via the control laser enables even a complete stop, that is, storage, of probe light pulses in the atomic medium. Here, we demonstrate optomechanically induced transparency (OMIT)--formally equivalent to EIT--in a cavity optomechanical system operating in the resolved sideband regime. A control laser tuned to the lower motional sideband of the cavity resonance induces a dipole-like interaction of optical and mechanical degrees of freedom. Under these conditions, the destructive interference of excitation pathways for an intracavity probe field gives rise to a window of transparency when a two-photon resonance condition is met. As a salient feature of EIT, the power of the control laser determines the width and depth of the probe transparency window. OMIT could therefore provide a new approach for delaying, slowing and storing light pulses in long-lived mechanical excitations of optomechanical systems, whose optical and mechanical properties can be tailored in almost arbitrary ways in the micro- and nano-optomechanical platforms developed to date.
We experimentally investigate the nonlinear transmission spectrum of coherent light fields propagating through a Rydberg EIT medium with strong atomic interactions. In contrast to previous investigations, which have largely focused on resonant control fields, we explore here the full two-dimensional spectral response of the Rydberg gas. Our measurements confirm previously observed spectral features for a vanishing control-field detuning, but also reveal significant differences on two-photon resonance. In particular, we find qualitative deficiencies of mean-field models and rate-equation simulations as well as a third-order nonlinear susceptibility that accounts for pair-wise interaction effects at low probe-field intensities in describing the nonlinear probe-field response under EIT conditions. Our results suggest that a more complete understanding of Rydberg-EIT and emerging photon interactions requires to go beyond existing simplified models as well as few-photon theories.
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