4.6 Article

Tunable plasmonically induced transparency with giant group delay in gain-assisted graphene metamaterials

Journal

OPTICS EXPRESS
Volume 30, Issue 9, Pages 14103-14111

Publisher

Optica Publishing Group
DOI: 10.1364/OE.455954

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Funding

  1. National Natural Science Foundation of China [11947062]
  2. Natural Science Foundation of Hunan Province [2020JJ5551, 2021JJ40523]

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This study proposes a graphene metamaterial embedded into gain-assisted medium, which exhibits electromagnetically induced transparency-like spectra. The transmission characteristics of the metamaterial are quantitatively described using the transfer matrix method and temporal coupled-mode theory. By tuning the gain level and Fermi energy level, the transmission characteristics can be controlled. The optimized incorporation between gain medium and graphene nanoribbons results in destructive interference between high order graphene plasmonic modes, leading to significant phase transition with giant group delay and ultra-high group index.
We propose a graphene metamaterial consisting of several layers of longitudinally separated graphene nanoribbon array embedded into gain-assisted medium, demonstrating electromagnetically induced transparency-like spectra. Combined with finite-difference timedomain simulations, the transfer matrix method and temporal coupled-mode theory are adopted to quantitatively describe its transmission characteristics. These transmission characteristics can be tuned by altering the gain level in medium layer and the Fermi energy level in graphene. Additionally, it is the incorporation between gain medium and graphene nanoribbons with optimized geometrical parameters and Fermi energy level that the destructive interference between high order graphene plasmonic modes can be obtained, suggesting drastic phase transition with giant group delay and ultra-high group index up to 180 ps and 10(4), respectively. Our results can achieve efficient slow light effects for better optical buffers and other nonlinear applications. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

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