Diffractive Efficiency Prediction of Surface Relief Grating Waveguide Using Artificial Neural Network

Fu-Li Hsiao; Hsuan-Ming Chang; Wen-Kai Lin; Ying-Pin Tsai

doi:10.46604/ijeti.2024.13434

Authors

Fu-Li Hsiao Institute of Photonics, National Changhua University of Education, Changhua, Taiwan, ROC
Hsuan-Ming Chang Institute of Photonics, National Changhua University of Education, Changhua, Taiwan, ROC
Wen-Kai Lin Institute of Photonics, National Changhua University of Education, Changhua, Taiwan, ROC
Ying-Pin Tsai Institute of Photonics, National Changhua University of Education, Changhua, Taiwan, ROC

DOI:

https://doi.org/10.46604/ijeti.2024.13434

Keywords:

surface relief grating, diffractive efficiency, finite element method, artificial neural network

Abstract

This study aims to develop lightweight and comfortable wearable devices using surface-relief grating, which can be designed to meet different diffraction conditions. However, extensive calculations must be performed to obtain the impact of the variation in the structural dimensions. The finite element method is used to solve the diffractive efficiency and then replaced by trained artificial neural networks with a single hidden layer containing 25 neurons. By using raw data with geometric parameters as the features, the performance of the network is investigated with different numbers of raw data; in addition, the regression analysis shows a high R-value of approximately 0.999. The predicted results are compared with those calculated from the simulation. The diffraction efficiency tendencies vary with the different geometric parameters, which show a high level of agreement between the predicted and calculated data; this confirms that the proposed method supports and reduces the burden of extensive calculations.

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