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Modeling and study of FBG interrogator based on a two-dimensional image sensor

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Fiber Bragg Grating (FBG) interrogators contain a movable scattering element that tracks the FBG central wavelength. The movable element of the interrogator limits the interrogation speed. This paper proposes an interrogation method that does not use movable elements. This is achieved by using an Array Waveguide (AWG) to split the FBG reflected spectrum and a Convolutional Neural Network (CNN) for training to determine the central wavelength. Most of the known studies consider the AWG output as a one-dimensional data array for training the neural network. However, CNNs work best with two-dimensional images. This paper proposes to transform the AWG output using a two-dimensional image sensor with a circular configuration. This allows for higher accuracy and improved resolution in predicting the central wavelength. The AWG signal is projected onto a two-dimensional image sensor which has either a grid or a circular configuration. The number of AWG channels used is 32, which corresponds to a distance between channel wavelengths of 0.0625 nm. The circular configuration enables more accurate feature extraction using CNN. A 32-beam passive waveguide array in a circular configuration is used for FBG interrogation. It projects the FBG output signals onto the image sensor, enabling high-resolution Bragg wavelength prediction. Computer simulation of the proposed interrogation device demonstrated a predicted resolution of ±1 pm with 98 % accuracy. It should be noted that the presented values are estimates and are subject to refinement using a hardware prototype. Such devices are relatively easy to manufacture and are readily available to consumers.

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