Particle swarm optimization and finite-difference time-domain (PSO/FDTD) algorithms for a surface plasmon resonance-based gas sensor

Surface plasmon resonance (SPR) is a spotlight technique for environmental monitoring. In this regard, an optical gas sensor based on SPR is investigated and analyzed here. The sensor is used for detection of toxic gases such as cyanogen, ethanol, propane, nitrogen dioxide, and phosgene. The performance of the sensor is inspected and optimized by considering different parameters such as the thickness of the metal layer, the material used for the prism, and the incident light angle and wavelength. The finite-difference time-domain method is used for simulation, and the optimization algorithm is particle swarm optimization. Simulation results show that the best metal thickness for gold, silver, aluminum, and copper is 44.39 nm, 43.16 nm, 18.195 nm, and 32.5 nm, respectively. Also, utilizing different materials such as SiO₂, PMMA, BCB, MgF₂, and cyclomer for the prism and the effect of temperature variation on these materials are studied, and it is shown that MgF₂ demonstrates better performance. Furthermore, the results of the optimization indicate that the most suitable incident light angles (wavelengths) are 44.43° (900.59 nm), 45.5° (599.7 nm), 44.56° (300.1 nm), and 44.41° (899.4 nm) for gold, silver, aluminum, and copper, respectively. The sensor shows the best full-width at half-maximum and quality factor (Q) of 4.2 nm and 214.28, respectively, for the combination of MgF₂ prism and a gold layer. The maximum sensitivity and figure of merit for gold layer are >120 (nm/RIU) and >20, and for copper layer are >270 (nm/RIU) and >30, respectively.