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<p>This research aims to design and simulate photonic crystal micro pillar based cavity sensors by varying structural parameters and materials. Configurations studied are single cavity, dual cavity and tri-cavity. Parameters Evaluated are radius 0.14&#xa0;μm to 0.2&#xa0;μm and lattice constant 0.9&#xa0;μm to 0.96&#xa0;μm. The material used for investigation is Gallium Arsenide (GaAs). Multiple linear regression (MLR) is used to quantify the relationship between design parameters and sensor performance metrics, such as resonance frequency and Q-factor. The key finding is integrating simulation data with predictive modelling providing insights into optimizing sensor designs for enhanced photonic applications. This research offers a robust framework for parameter-driven sensor development, highlighting the potential for improved performance in photonic crystal-based sensors. For GaAs single cavity, MLR data shows the variability in the coefficient estimate. The standard error is 6.57340096, relatively moderate, indicating a fair level of precision in prediction. Further, an F value of 11.1143 indicates that the regression model is statistically significant, meaning that at least one of the predictors is significantly related to the Q factor.</p>

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Design and parametric evaluation of multi cavity photonic sensors

  • 期刊论文

  • 发表日期：

    2025年4月4日

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  • Bishwajeet Pandey,
  • Wan Aezwani Wan Abu Bakar,
  • Pushpanjali Pandey,
  • Preeta Sharan

摘要

This research aims to design and simulate photonic crystal micro pillar based cavity sensors by varying structural parameters and materials. Configurations studied are single cavity, dual cavity and tri-cavity. Parameters Evaluated are radius 0.14 μm to 0.2 μm and lattice constant 0.9 μm to 0.96 μm. The material used for investigation is Gallium Arsenide (GaAs). Multiple linear regression (MLR) is used to quantify the relationship between design parameters and sensor performance metrics, such as resonance frequency and Q-factor. The key finding is integrating simulation data with predictive modelling providing insights into optimizing sensor designs for enhanced photonic applications. This research offers a robust framework for parameter-driven sensor development, highlighting the potential for improved performance in photonic crystal-based sensors. For GaAs single cavity, MLR data shows the variability in the coefficient estimate. The standard error is 6.57340096, relatively moderate, indicating a fair level of precision in prediction. Further, an F value of 11.1143 indicates that the regression model is statistically significant, meaning that at least one of the predictors is significantly related to the Q factor.