Multiobjective optimization for a plasmonic nanoslit array sensor using Kriging models

We propose an efficient multiobjective optimization approach for a plasmonic nanoslit array sensor using Kriging surrogate models. The universal Kriging models whose regression functions are zeroth-, first-, and second-order polynomials are adopted to estimate objective functions. The multiobjective extension of the genetic algorithm is used for Pareto optimal sensor geometry. The objective functions are the figure of merit defined as a ratio of peak wavelength shift at molecular adsorption and 3 dB bandwidth of transmission spectrum, and peak transmission power, respectively. The optical properties of a plasmonic slit sensor are investigated, such as transmission power, bandwidth, and peak shift, using the finite element method.