Predicting Dielectric Waveguides Characteristics Using Deep Learning

We propose an unsupervised deep learning model based on physics-informed neural network (PINNS) to find the effective refractive index of a slab waveguide. The model accuracy could reach 99% within a time range from 60 to 120 seconds for symmetric and anti-symmetric waveguide. The results show the s...

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Bibliographic Details
Main Authors: Elsheikh, Omar E., Shaaban, Adel, Arafa, A., Gad, Nasr, Yahya, Ashraf, Gomaa, Lotfy Rabeh, Swillam, M.
Format: Conference Proceeding
Language:English
Subjects:
Deep learning
Dielectrics
Neural networks
optical detector
Photonics
Refractive index
Slabs
waveguides
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Summary:We propose an unsupervised deep learning model based on physics-informed neural network (PINNS) to find the effective refractive index of a slab waveguide. The model accuracy could reach 99% within a time range from 60 to 120 seconds for symmetric and anti-symmetric waveguide. The results show the success of the introduced method in solving fail cases of the compared methods.
ISSN:2693-8316
DOI:10.1109/PN56061.2022.9908369