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<p>This paper presents a novel plasmonic sensor architecture incorporating graphene with an asymmetric grating structure to harness the Fano resonance effect. By exploiting discrete-continuum interference, this design achieves enhanced sensitivity and spectral tunability across a broad frequency range. To achieve optimization of sensor’s performance, a methodological fusion of a Deep Neural Network (DNN) with the Particle Swarm Optimization (PSO) algorithm is implemented. The training dataset for the DNN is derived from simulations conducted via the Finite Element Method (FEM), enabling the meticulous calibration and predictive analysis of pivotal structural parameters, including the periodicity of the grating, the asymmetry height, and the Fermi energy level of graphene. The resultant optimized sensor is characterized by a sensitivity of 5890&#xa0;nm/RIU and a Figure of Merit (FoM) of 150, highlighting the significant advancements achievable through the integration of state-of-the-art optimization methodologies with metamaterial design principles.</p>

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Enhancing the Figure of Merit in Graphene Plasmonic Sensors: The Impact of Asymmetric Gratings

  • 期刊论文

  • 发表日期：

    2025年7月15日

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  • Rasoul Keshavarzi,
  • Mohammad Reza Salehi,
  • Farzin Emami

摘要

This paper presents a novel plasmonic sensor architecture incorporating graphene with an asymmetric grating structure to harness the Fano resonance effect. By exploiting discrete-continuum interference, this design achieves enhanced sensitivity and spectral tunability across a broad frequency range. To achieve optimization of sensor’s performance, a methodological fusion of a Deep Neural Network (DNN) with the Particle Swarm Optimization (PSO) algorithm is implemented. The training dataset for the DNN is derived from simulations conducted via the Finite Element Method (FEM), enabling the meticulous calibration and predictive analysis of pivotal structural parameters, including the periodicity of the grating, the asymmetry height, and the Fermi energy level of graphene. The resultant optimized sensor is characterized by a sensitivity of 5890 nm/RIU and a Figure of Merit (FoM) of 150, highlighting the significant advancements achievable through the integration of state-of-the-art optimization methodologies with metamaterial design principles.