  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.
