Generative Model based Optical Response Prediction for Plasmonic Sensing

Abstract

In recent times, plasmonic sensing is widely used for detecting tiny particles (micro / nano-scale) and plays a crucial role in diverse application domains such as sustainability, healthcare etc. In current scenario, numerical simulators are used for predicting the optical response of a plasmonic nanostructure by solving Maxwell’s equations. These simulators are highly expensive and extremely time-consuming. We are proposing an alternative of numerical simulators using DL-based generative model to predict optical response against a geometrical structure of plasmonic sensor with reduced computing time. Our method is based on a variant of variational auto-encoder (VAE) constrained to follow the resonance peak of the optical response during training. Our method is capable of predicting the optical response from a diverse set of plasmonic nano-structures. We have demonstrated the performance using publicly available optical ring resonator an H-shaped nano-structure geometry data.