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Two-dimensional silicon-based dielectric column photonic crystal point defect microcavity Neural network modelling

Tingshuang Zhang
Vol.1, No.1, pp.137-143, 2024 DOI: 10.62051/ijmsts.v1n1.13 March 25, 2024
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Abstract: In order to solve the problem that it is difficult to predict the microcavity photonic energy bands of point defects in two-dimensional silicon-based dielectric column photonic crystals, this paper proposes a method to predict the microcavity photonic energy bands of point defects in two-dimensional silicon-based photonic crystals by using an artificial neural network model. In this paper, the energy band structure of triangular lattice point defects at different radii is calculated using MPB, and a neural network model is established based on this data and the accuracy of the established model is verified.
Keywords: Photonic crystal, Silicon base, Point defect, Neural network modelling
APA Citation: Tingshuang Zhang (2024). Two-dimensional silicon-based dielectric column photonic crystal point defect microcavity Neural network modelling. International Journal of Materials Science and Technology Studies, 1(1), 137-143. https://doi.org/10.62051/ijmsts.v1n1.13

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