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Optimizing PCF-SPR sensor design through Taguchi approach, machine learning, and genetic algorithms.

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Summary

This study introduces a highly sensitive photonic crystal fiber surface plasmon resonance sensor for detecting low refractive index variations. Optimized designs achieve remarkable spectral and amplitude sensitivities, showcasing potential for pharmaceutical inspection.

Keywords:
Genetic algorithmMulti-layer perceptronParticle swarm optimizationPhotonic crystal fiberSurface plasmon resonanceTaguchi approach

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Area of Science:

  • Photonics
  • Plasmonics
  • Optical Sensing

Background:

  • Photonic Crystal Fibers (PCF) incorporating Surface Plasmon Resonance (SPR) offer advanced sensing capabilities.
  • Low refractive index (RI) detection is crucial for applications like pharmaceutical inspection and environmental monitoring.
  • Existing PCF-SPR sensors require optimization for enhanced sensitivity and stability.

Purpose of the Study:

  • To design and optimize a novel dual-core PCF-SPR sensor for highly sensitive detection of low refractive indices.
  • To investigate the impact of design parameters on sensor performance.
  • To utilize Artificial Neural Networks (ANN) for predicting sensor properties and optimizing design.

Main Methods:

  • Fabrication of a dual-core PCF structure with an externally deposited silver (Ag) plasmonic layer, protected by titanium dioxide (TiO2).
  • Optimization of five key design parameters (pitch, air hole diameter, silver thickness) using the Taguchi L8(2^5) orthogonal array.
  • Application of Artificial Neural Network (ANN) optimization techniques, including Multi-Layer Perceptron (MLP) and Particle Swarm Optimization (PSO), to predict confinement loss (αloss).
  • Utilizing a genetic algorithm (GA) for optimizing sensor design to maximize confinement loss.

Main Results:

  • Achieved remarkable spectral sensitivity of 10,000 nm/RIU and amplitude sensitivity of 235,882 RIU-1 for low RI detection.
  • Demonstrated the effectiveness of PSO-ANN in accurately predicting sensor performance for unknown geometric dimensions.
  • Identified optimal design parameters yielding superior sensor responsiveness.

Conclusions:

  • The proposed dual-core PCF-SPR sensor exhibits exceptional sensitivity for low RI detection.
  • ANN optimization techniques, particularly PSO, provide efficient and accurate prediction of optical properties, facilitating rapid sensor design.
  • The sensor design holds significant promise for real-time monitoring in pharmaceutical inspection and detecting low RI analytes.