Prediction of 12 Photonic Crystal Fiber Optical Properties Using MLP in Deep Learning

In this letter, we proposed the use of feed-forward multilayer perceptron in deep learning-based artificial neural network (ANN) to accurately predict 12 optical parameters of silica-based photonic crystal fiber (PCF) within milliseconds using 6 input parameters. The optimized ANN has 3 hidden layers and each layer has 50 neurons. The PCF has several hexagonal-shaped layers with circular air holes, and it uses silica as the cladding and FK51A glass as the core. The PCF parameters that have been successfully predicted include birefringence, chromatic dispersion, effective area, effective refractive index, nonlinear coefficient, numerical aperture, power fraction, relative sensitivity, V-parameter, and loss profiles such as confinement loss, effective material loss, and scattering loss. The prediction has high accuracy with a loss of only 0.00567 and a learning rate of 0.0001. 7-fold validation and batching are used to increase scalability during validation. The proposed ANN is over 99.9% faster than conventional numerical simulation approaches.

Automated summary

In this study, a feed-forward multilayer perceptron in a deep learning-based artificial neural network (ANN) is proposed to accurately predict 12 optical parameters of silica-based photonic crystal fiber (PCF) using only 6 input parameters. The optimized ANN with 3 hidden layers and 50 neurons in each layer achieves high accuracy and significantly faster prediction compared to conventional numerical simulation methods.