A Multi-Objective Optimization of 2D Materials Modified Surface Plasmon Resonance (SPR) Based Sensors: An NSGA II Approach

Abstract

Modifying the structure of surface plasmon resonance based sensors by adding 2D materials has been proven to considerably enhance the sensor’s sensitivity in comparison to a traditional three layer configuration. Moreover, a thin semiconductor film placed on top of the metallic layer and stacked together with 2D materials enhances even more sensitivity, but at the cost of worsening the plasmonic couplic strength at resonance (minimum level of reflectivity) and broadening the response. With each supplementary layer added, the complexity of optimizing the performance increases due to the extended parameter space of the sensor. This study focused on overcoming these difficulties in the design process of sensors by employing a multi-objective genetic algorithm (NSGA II) alongside a transfer matrix method (TMM) and, at the same time, optimizing the sensitivity to full width at half maximum (FWHM), and the reflectivity level at a resonance for a four layer sensor structure. Firstly, the thin semiconductor’s refractive index was optimized to obtain the maximum achievable sensitivity with a narrow FWHM and a reflectivity level at a resonance of almost zero. Secondly, it was shown that refractive indices of barium titanate (BaTiO3) and silicon (Si) are the closest to the optimal indices for the silver—graphene/WS2 and MoS2 modified structures, respectively. Sensitivities up to 302 deg/RIU were achieved by Ag–BaTIO3–graphene/WS2 configurations with an FWHM smaller than 8 deg and a reflectivity level less than 0.5% at resonance.