An Ultra-Sensitive Visible-IR Range Fiber Based Plasmonic Refractive Index Sensor

TL;DR

This paper introduces a highly sensitive PCF-based plasmonic sensor capable of detecting a wide range of analyte refractive indices with exceptional accuracy, and integrates an ANN model for precise RI prediction, with potential biomedical applications.

Contribution

A novel ultra-sensitive PCF-based SPR sensor design combined with an artificial neural network model for accurate refractive index detection across a broad spectrum.

Findings

Wavelength sensitivity up to 123,000 nm/RIU

Figure of merit as high as 683 RIU$^{-1}$

Low wavelength resolution of 8.13 x 10$^{-8}$ RIU

Abstract

Photonic crystal fiber (PCF)-based plasmonic sensors have gained considerable attention because of their highly sensitive performance and broad range of sensing regimes. In this work, a relatively simple ultra-sensitive PCF-based surface plasmon resonance (SPR) sensor has been 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 RIU$^{-1}$, respectively, and extremely low values of wavelength resolution (WR) of 8.13 x 10$^{-8}$ RIU. In addition, a novel artificial neural network (ANN) model is proposed to be integrated into the practical setup in order to accurately predict the RIs by carefully examining the simulation data. The mean square error (MSE) and 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 device has the potential to serve as a viable option for sensing analyte refractive index (RI). Additionally, the sensor could be utilized for identifying cancerous cells and detecting urinary tract infections in humans.