Inverse Design of Plasmonic Sensors Optimization and Performance Enhancement Using Deep Learning

Abstract

This research presents a reinforcement learning (RL)–based inverse design framework developed to enhance the performance of plasmonic refractive index nanosensors by optimizing their geometric and topological parameters. Plasmonic refractive index sensors operate by exploiting the interaction between light and the metal–insulator interface, where surface plasmon polaritons (SPPs) enable highly sensitive, label-free detection of refractive index variations in the surrounding medium. In this work, the inverse design framework was implemented using the Lumerical scripting language integrated with Python, enabling automated, iterative refinement of sensor geometry toward predefined targets of sensitivity and figure of merit (FOM). The optimization process employed a Deep Q-Network (DQN)–based reinforcement learning algorithm, wherein the agent dynamically adjusted key design parameters such as resonator radius, width, thickness, and nanorod dimensions. Beginning with a pentagonal ring resonator and an initial target sensitivity of 2000 nm/RIU, the RL agent progressively evolved the geometry into an octagonal configuration. Upon incorporating FOM into the reward function, the optimized design achieved a sensitivity of 2638.15 nm/RIU and an FOM of 10.71 RIU−1. The results demonstrate that coupling reinforcement learning with plasmonic inverse design significantly accelerates the discovery of high-performance sensor geometries, outperforming conventional manual and brute-force optimization methods, and highlight the potential of RL-driven optimization as a transformative approach for the intelligent design of plasmonic sensors with enhanced sensitivity, compactness, and operational efficiency.