journal article·2012-11Open access

Switching terahertz waves with gate-controlled active graphene metamaterials

Seung Hoon Lee, Muhan Choi, Teun-Teun Kim, Seungwoo Lee, Ming Liu, Xiaobo Yin, Hong Kyw Choi, Seung S. Lee, Choon-Gi Choi, Sung-Yool Choi, Xiang Zhang, Bumki Min

Nature Materials· Volume 11· Issue 11

Abstract

The extraordinary electronic properties of graphene provided the main thrusts for the rapid advance of graphene electronics. In photonics, the gate-controllable electronic properties of graphene provide a route to efficiently manipulate the interaction of photons with graphene, which has recently sparked keen interest in graphene plasmonics. However, the electro-optic tuning capability of unpatterned graphene alone is still not strong enough for practical optoelectronic applications owing to its non-resonant Drude-like behaviour. Here, we demonstrate that substantial gate-induced persistent switching and linear modulation of terahertz waves can be achieved in a two-dimensional metamaterial, into which an atomically thin, gated two-dimensional graphene layer is integrated. The gate-controllable light-matter interaction in the graphene layer can be greatly enhanced by the strong resonances of the metamaterial. Although the thickness of the embedded single-layer graphene is more than six orders of magnitude smaller than the wavelength (<λ/1,000,000), the one-atom-thick layer, in conjunction with the metamaterial, can modulate both the amplitude of the transmitted wave by up to 47% and its phase by 32.2° at room temperature. More interestingly, the gate-controlled active graphene metamaterials show hysteretic behaviour in the transmission of terahertz waves, which is indicative of persistent photonic memory effects.
Source: europepmc · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Metamaterials and Metasurfaces Applications · Plasmonic and Surface Plasmon Research · Thermal Radiation and Cooling Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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