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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Materials Science and Technology Studies
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
