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Analysing group indices and dispersion characteristics of engineered photonic crystal waveguides using artificial neural network

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Abstract

Artificial neural networks in machine learning are popular in solving complex data problems. This article proposes a neural network for predicting the dispersion relations and group indices of a dispersion-engineered photonic crystal waveguide mainly designed for slow light applications. The model is trained by the data sets generated from MPB programming. A supervised feedforward multilayer perceptron neural network is used for predicting the data. Three hidden layers are used, with 500 nodes in each layer. One hundred epochs are used to achieve the lowest mean squared error. Waveguide structures with lattice shifts in the range of 0.00a–0.25a are used to generate the training data sets. After the training, it was found that the model could predict the dispersion and group indices values within the trained data range. The model is effective in reducing the computational time.

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Abbreviations

ANN:

Artificial neural network

ML:

Machine learning

MLP:

Multilayer perceptron

MPB:

MIT photonic-bands

MSE:

Mean squared error

PBG:

Photonic bandgap

PC:

Photonic crystal

PCF:

Photonic crystal fibre

PCW:

Photonic crystal waveguide

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Acknowledgements

This work is funded by the Department of Science and Technology, Government of India (DST-GoI) under the INSPIRE fellowship program with fellowship number IF160435. The authors sincerely acknowledge DST-GoI for the same.

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Authors and Affiliations

  1. Department of Physics, Indian Institute of Technology, Tirupati, 517 619, India

    Vadapalli Durga Rama Pavan

  2. Department of Physics, National Institute of Technology, Warangal, 506 004, India

    Vangety Nikhil, Koustav Dey & Sourabh Roy

  3. Department of Computer Science and Engineering, National Institute of Technology, Warangal, 506 004, India

    B. Umamaheswara Sharma

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  1. Vadapalli Durga Rama Pavan
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  2. Vangety Nikhil
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  3. Koustav Dey
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Contributions

SR conceived the idea of an ML-based study of PCW. VDRP has performed the simulations, collected the raw data, and did the primary analysis in MPBVN, and KD developed the code to study PCW in MLBUS has tested the efficiency of the ANN model and performed the error analysis. Pavan has prepared the initial manuscript, and SR has analysed the results, drawn the conclusions and finalised the contents of the manuscript.

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Correspondence to Sourabh Roy.

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Pavan, V.D.R., Nikhil, V., Dey, K. et al. Analysing group indices and dispersion characteristics of engineered photonic crystal waveguides using artificial neural network. J Opt 53, 1438–1446 (2024). https://doi.org/10.1007/s12596-023-01285-9

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