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ANN-based estimation of dispersion characteristics of slotted photonic crystal waveguides

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Abstract

In this paper, the dispersion characteristics of slotted photonic crystal waveguides (SPCWs) have been estimated for any arbitrary set of structural parameters using machine learning-based artificial neural network (ANN). The machine learning-based technique yields faster solutions of the three-dimensional eigenvalue equations, which otherwise require substantial time using the conventional plane wave expansion (PWE)-based numerical simulations. Most importantly, the novel contribution of the work lies in estimating the structural parameters of the SPCWs from the given specifications of the dispersion characteristics through an inverse computation. A simple feed-forward neural network has been employed for both the forward and inverse estimations. The computation performances using both the ANN model and PWE simulations are analyzed and compared. The research offers significant implications for the field of photonics. By employing machine learning techniques, particularly ANNs, researchers and engineers can swiftly and efficiently analyze the dispersion properties of SPCWs, facilitating rapid prototyping and optimization of photonic devices. Additionally, the capability to infer structural parameters from desired dispersion characteristics streamlines the design process, potentially leading to the development of customized waveguides tailored to specific applications.

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Acknowledgements

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Funding

No funding was received for conducting this study.

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Author notes

  1. C. Prakash, T. Datta, M. Sen and H. Mondal have contributed equally to this work.

Authors and Affiliations

  1. Department of Electrical Electronics and Communication Engineering, Galgotias University, Greater Noida, Uttar Pradesh, 203201, India

    Akash Kumar Pradhan

  2. Department of Electronics and Communication Engineering, University of Engineering and Management, Jaipur, Rajasthan, 303807, India

    Chandra Prakash

  3. Department of Physics and Electronics, Vivekananda Mahavidyalaya, Haripal, Hooghly, West Bengal, 712405, India

    Tanmoy Datta

  4. Department of Electronics Engineering, Indian Institute of Technology (ISM) Dhanbad, Dhanbad, Jharkhand, 826004, India

    Mrinal Sen

  5. Department of Electronics and Communication Engineering, Dibrugarh University Institute of Engineering and Technology, Dibrugarh, Assam, 786003, India

    Haraprasad Mondal

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  1. Akash Kumar Pradhan
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Contributions

The authors confirm contribution to the paper as follows. Conception of the problem, review of literature, design and collection were done by Akash Kumar Pradhan, Tanmoy Datta, Chandra Prakash, Mrinal Sen, and Haraprasad Mondal. Data collection was done by Akash Kumar Pradhan, Tanmoy Datta, Chandra Prakash, Mrinal Sen, and Haraprasad Mondal. Analysis and interpretation of results, drafting of the manuscript, preparation of figures and presentations were done by Akash Kumar Pradhan, Tanmoy Datta, Chandra Prakash, Mrinal Sen, and Haraprasad Mondal. All authors have reviewed the results and approved the final version of the manuscript.

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Correspondence to Tanmoy Datta.

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Pradhan, A.K., Prakash, C., Datta, T. et al. ANN-based estimation of dispersion characteristics of slotted photonic crystal waveguides. J Comput Electron (2024). https://doi.org/10.1007/s10825-024-02162-9

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  • DOI: https://doi.org/10.1007/s10825-024-02162-9

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