Bidirectional Design for SPR-Photonic Crystal Fiber Magnetic Field Sensor Based on Deep Learning

Optical magnetic field sensors have gained prominence due to their compact size, high sensitivity, and broad dynamic range. Photonic crystal fiber (PCF) sensors have emerged as a significant role in optical sensing. Traditional numerical design methods of PCF such as finite element method (FEM) are computationally demanding and often restrict the design space due to iterative trial-and-error processes. In this work, a bidirectional design approach using a deep learning model based on deep neural networks (DNNs) and augmented gray wolf optimizer (AGWO) is proposed for surface plasmon resonance (SPR)-based PCF magnetic field sensor. This bidirectional design method contains forward modeling and inverse design. The forward modeling predicts the optical responses of sensor structures accurately in 55.99 ms with an
${R}$
-squared value of 0.9942. The inverse design approach, with a tandem network configuration for addressing the nonuniqueness challenge in inverse design, identifies the sensor design aligning with desired optical responses in less than 0.5 s. In addition, AGWO is utilized to further enhance the accuracy and convergence ability of DNN. The key results indicate that our proposed approach reduces computational effort and enhances design effectiveness in PCF sensor design, which can also offer an alternative method for the design of various optical devices.