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Prediction of electromagnetic field patterns of optical waveguide using neural network

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Abstract

Physical fields represent quantities that vary in space and/or time axes. Understanding the distribution of a field pattern is a key element in scientific discoveries and technological developments. In this article, by picking up the electromagnetic field of an optical waveguide as an example, we demonstrate how field patterns can be uncovered using artificial neural networks. The cross section plane of the optical waveguide is discretized into a set of tiny pixels, and the field values are obtained at these pixels. Deep learning model is created by assuming the field values as outputs, and the geometrical dimensions of the waveguide as inputs. The correlation between the field values in the adjacent pixels is established by mean of feedback using a recurrent neural network. The trained deep learning model enables field pattern prediction for the entire (and usual) parameter space for applications in the field of photonics.

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Acknowledgements

This work is supported by RGANS1901, “Al-Enabled Electronic-Photonic IC Design”.

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Authors and Affiliations

  1. Institute of High Performance Computing, Agency for Science, Technology, and Research (A-STAR), Fusionopolis, 1 Fusionopolis Way, #16-16, Connexis, 138632, Singapore

    Gandhi Alagappan & Ching Eng Png

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  1. Gandhi Alagappan
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Correspondence to Gandhi Alagappan.

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Alagappan, G., Png, C.E. Prediction of electromagnetic field patterns of optical waveguide using neural network. Neural Comput & Applic 33, 2195–2206 (2021). https://doi.org/10.1007/s00521-020-05061-9

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