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Designing a compact photonic crystal decoder using graphene-SiO2 stack.

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Bibliographic Details
Title:	Designing a compact photonic crystal decoder using graphene-SiO2 stack.
Authors:	Soroosh, M., Shahbaznia, M., Maleki, M. J., Ganji, J.
Source:	Optical & Quantum Electronics; May2024, Vol. 56 Issue 5, p1-15, 15p
Subject Terms:	CHEMICAL potential, LIGHT transmission, OPTICAL control, GRAPHENE, PHOTONIC crystals
Abstract:	In this study, a novel approach is described for developing an electro-optic decoder utilizing a combination of photonic crystals and a graphene stack. The decoder structure comprises three main components: silicon waveguides, stacks, and one-dimensional photonic crystals. To create favorable interferences similar to reflectors based on Bragg layers, a one-dimensional array of air holes is designed in parallel with a waveguide. Within this array, a graphene-SiO2 stack is incorporated, enabling the creation of a tunable stop-band frequency filter within the wavelength range of 1489 to 1492 nm. This tunability is achieved by altering the graphene refractive index according to the applied chemical potential. By controlling the optical wave transmission through the waveguides, the transmission from the input to the output ports is effectively regulated. Simulation results indicate that decoding operation can be achieved by applying chemical potentials of 0.1 eV and 0.9 eV. The normalized power levels for logic states 1 and 0 are equal to 48% and 6.2%, respectively, resulting in a contrast ratio of 8.89 dB. Additionally, the compact size of the designed structure, occupying only 99 µm² of space, demonstrates its advantages over previous electro-optic works, particularly in terms of realizing optical circuits. [ABSTRACT FROM AUTHOR]
	Copyright of Optical & Quantum Electronics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Complementary Index

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