Kim Kyoung-Youm, Jung Jaehoon
Appl Opt. 2017 Jul 20;56(21):5838-5843. doi: 10.1364/AO.56.005838.
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.
我们提出了一种使用克里金代理模型的用于等离子体纳米狭缝阵列传感器的高效多目标优化方法。采用回归函数为零阶、一阶和二阶多项式的通用克里金模型来估计目标函数。遗传算法的多目标扩展用于帕累托最优传感器几何结构。目标函数分别是定义为分子吸附时峰值波长偏移与传输光谱3 dB带宽之比的品质因数，以及峰值传输功率。使用有限元方法研究了等离子体狭缝传感器的光学特性，如传输功率、带宽和峰值偏移。
