A highly sensitive multi-channel SPR-PCF based biosensor with deep learning prediction approach

Abstract

A very sensitive multi-channel refractive index-based biosensor with a detection range of 1.26 to 1.36 has been proposed. Titanium oxide (TiO2) was utilized as the dielectric, and gold as the plasmonic material in the sensor. The suggested sensor achieved a maximum sensitivity value of 48,000 nm/RIU and 7220 RIU-1 using the wavelength and amplitude interrogation techniques with a pitch layer variation of 5.55–5.95 μm and a metal layer variation of 30–50 nm. The suggested sensor can detect many compounds with amazing sensitivity, such as glucose, sucrose, ethanol, methanol, human intestinal mucosa, sevoflurane, and bio-chemicals. Additionally, a deep learning model that forecasts six optical properties has been proposed. The hyperparameters of the deep neural network model are tuned extensively to maximize accuracy. Our model had a Mean Squared Error of 0.006 and was significantly faster than conventional techniques. The results show that deep learning can predict optical features in photonic sensors, which can identify specific analytes or biomolecules in a sample without costly and time-consuming simulations. Photonic sensor researchers and engineers may utilize the suggested model on vast datasets and adjust it to new settings. This study may open the door to the use of deep learning-based optimization techniques to enhance biosensors.

Keywords

• Biosensor
• Optical sensor
• Multi-channel
• Deep learning
• Machine learning
• Sensitivity