Abstract
Photonic Crystal Fibres (PCFs) are emerging as an alternative to standard fibres for applications in many disciplines like fibre lasers & amplifiers, imaging, spectroscopy and telecommunications. They have superior light guiding properties compared to ordinary Optical Fibres (OFs). This paper illustrates the potential of neural networks to efficiently and accurately compute the optical properties of PCFs including solid-core, hollow-core and multi-core designs. The proposed method takes a range of design parameters and wavelengths as input to predict PCF optical properties like effective index, effective mode area, confinement loss and dispersion desired for optimal specifications. The neural network approach is significantly better in terms of the low computational runtimes (~5 milli-sec) required for predicting the properties against the longer runtimes (~18 sec) required for similar calculations by traditional numerical methods.
Published Version (
Free)
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 callDisclaimer: 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.
