Monte Carlo and particle swarm methods applied to the design of surface plasmon resonance sensors

One of the main challenges of designing Surface Plasmon Resonance - SPR - sensors is to maximize their sensitivity. In this paper two heuristic algorithms are investigated - Monte Carlo and Particle Swarm - for optimization of the sensitivity of SPR sensors in both the Kretschmann and the Otto configurations without the aid of a lorentzian approximation. Due to the random nature of the heuristic algorithms, a simple and robust approach has been obtained for achieving this optimization. A comparison is made in terms of computational efficiency between the proposed algorithms and the traditional optimization method, which demonstrates that the Particle Swarm algorithm is the most efficient among them. By applying this method, the spectral dependence of optimum parameters is obtained for sensors in Kretschmann and Otto configurations, for applications in both gaseous and aqueous media, using Au as the metal. Furthermore, a web app has been developed for designing optimized SPR sensors using the methods presented here.