Improved particle swarm optimization algorithm for high performance SPR sensor design

The surface plasmon resonance (SPR) sensor offers high sensitivity, good stability, simple structure, and is label-free. However, optimizing a multi-layered structure is quite time-consuming within the SPR sensor design process. Moreover, it is easy to overlook optimal design when using the conventional parameter sweeping method. In this paper, the improved particle swarm optimization (IPSO) algorithm with high global optimal solution convergence speed is applied for this purpose. Based on the IPSO algorithm, the SPR sensor with transition metal dichalcogenides (TMDCs) and graphene composite is proposed and optimized. The results show that the best Ag-ITO-WS2-graphene hybrid structure can be found by the IPSO algorithm, and the maximum sensitivity is 137.4 degrees/RIU, and the figure of merit (FOM) is 5.25 RIU-1. Compared with the standard particle swarm optimization algorithm, the number of iterations can be reduced. The development of the SPR sensor provides an optimization platform, which enormously improves the development efficiency of the multi-layer SPR sensor. (C) 2021 Optical Society of America

Automated summary

This paper introduces the improved particle swarm optimization algorithm for optimizing the design of surface plasmon resonance sensors, achieving the best structure and improving sensitivity and figure of merit.