Ultra-Sensitive Visible-IR Range Fiber Based Plasmonic Sensor: A Finite-Element Analysis and Deep Learning Approach for RI Prediction

Abstract

In this paper, a relatively simple and ultra-sensitive Photonic crystal fiber (PCF) based surface plasmon resonance (SPR) sensor is proposed for detecting different analyte refractive indices (RIs) ranging from 1.33 to 1.43 over a wide range of wavelength spectrum spanning $0.55~\mu $ m to $3.50~\mu $ m. The comprehensive finite-element simulations indicate that it is possible to achieve remarkable sensing performances such as wavelength sensitivity (WS) and figure of merit (FOM) as high as 123,000 nm/RIU and 683 RIU1, respectively, and extremely low value of wavelength resolution (WR) about $8.13\times 10^{-7}$ RIU. A novel artificial neural network (ANN) model is proposed which helps to accurately predict the RIs by carefully examining the simulation data. The mean square error (MSE) and prediction accuracy ( $R^{2}$ ) values for the ANN model are found about 0.0097 and 0.9987, respectively, indicating the high prediction capability of the proposed ANN model. Due to its exceptional sensitivity and precise detection capabilities, the proposed sensor has the potential to serve as a viable option for sensing analyte RI. Additionally, the sensor could be utilized for identifying cancerous cells and detecting urinary tract infections in humans.

Keywords

• Photonic crystal fiber
• surface plasmon resonance
• artificial neural network
• sensor