SPR-based PCF sensors for next generation research: A review

Abstract

The article illustrates the survey of Surface plasmon resonance-based photonic crystal fiber sensors related to various structural analysis and the complexity analysis of recently used approaches, such as Deep learning/Machine learning techniques used in the real-time applications. The study illustrates about the fabrications, applications and types of modeling techniques which evolved in the Plasmonic-Photonics based sensors. It examines the type of sensing approaches utilized based on the placement of the analyte in the core (Internal Sensing) or in the cladding (External Sensing). The study thus focuses on numerical analysis, a commonly used approach in PCF-based SPR sensors, is namely known as the Finite Element Method (FEM), which splits the involved geometry into the finest parts of interrelated elements to optimize the sensing parameters and also the article gives the variations of FEM from Data-driven methods. Surface plasmon resonance-based biosensors have been used in various biological analyses, in several ideal cases, such as urine analysis, finding blood components with different parameters, determining the harmful effects of cancer diseases and evaluating malaria by changing the refractive index of the blood samples, have been highlighted in the clinical validation part. This paper also illustrates the concepts of frequently used designs, likely the D-shaped design with Single and Dual-open loops, interpreted for optimizing high sensitivity value. The advancement techniques, such as Deep learning/Machine learning and Interrogation techniques, which are involved in the SPR-based PCF sensor designs with various approaches of Neural network and also the critical analysis of DL/ML approach limitations in the Photonics-based SPR sensor design, have been addressed in this study.