Optica Open
Browse
arXiv.svg (5.58 kB)

Predicting the $Q$ factor and modal volume of photonic crystal nanocavities via deep learning

Download (5.58 kB)
preprint
posted on 2023-01-11, 22:37 authored by Renjie Li, Xiaozhe Gu, Ke Li, Zhen Li, Zhaoyu Zhang
A Deep Learning (DL) based forward modeling approach has been proposed to accurately characterize the relationship between design parameters and the optical properties of Photonic Crystal (PC) nanocavities. The proposed data-driven method using Deep Neural Networks (DNN) is set to replace conventional approaches manually performed in simulation software. The demonstrated DNN model makes predictions not only for the Q factor but also for the modal volume V for the first time, granting us precise control over both properties in the design process. Specifically, a three-channel convolutional neural network (CNN), which consists of two convolutional layers followed by two fully-connected layers, is trained on a large-scale dataset of 12,500 nanocavities. The experimental results show that the DNN has achieved a state-of-the-art performance in terms of prediction accuracy (up to 99.9999% for Q and 99.9890% for V ) and convergence speed (i.e., orders-of-magnitude speedup). The proposed approach overcomes shortcomings of existing methods and paves the way for DL-based on-demand and data-driven optimization of PC nanocavities applicable to the rapid prototyping of nanoscale lasers and integrated photonic devices of high Q and small V.

History

Disclaimer

This arXiv metadata record was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv.

Usage metrics

    Categories

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC