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Multiobjective Constrained Optimization for Graphene-Based Plasmonic Sensor Design
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DOI：10.1109/TIM.2025.3637999.png)
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Abstract
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This article presents a multiobjective optimization approach for the design of surface plasmon resonance (SPR) sensors based on a multilayered structure, incorporating a coupling prism made of BK7 glass, a thin metal film, and graphene layers, designed to operate in wavelength interrogation mode (WIM). Using a genetic algorithm (GA) with integer constraints, the sensor geometry was optimized to maximize sensitivity and minimize the reflectance at the resonance wavelength. A nonlinear constraint was introduced to restrict the reflectance minimum to values below 0.3, ensuring physical viability and efficient plasmonic coupling. Additionally, a robustness analysis was performed by simulating perturbations in metal thickness, graphene layer thickness, and analyte refractive index, revealing the impact of fabrication tolerances on the sensor response. Analysis of variance (ANOVA) and sensitivity grouping analyses indicated that the number of graphene layers (NGLs) is the dominant factor influencing sensor performance. The results demonstrate that the proposed optimization method based on GA yields robust, high-performance SPR sensors suitable for precision refractometric applications.
Keywords:
Graphene
multiobjective optimization
optical biosensors
prism-shaped glass biochip (PGBIO)
surface plasmon resonance (SPR)
wavelength interrogation mode (WIM)
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5.9
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2.0W
Citations:
5.8W
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