Deep learning-based modeling of photonic crystal nanocavities
Renjie Li, Xiaozhe Gu, Ke Li, Yaoran Huang, Zhen Li, Zhaoyu Zhang
Abstract
A deep learning (DL)-based approach has been proposed to accurately model the relationship between design parameters and the Q factor of photonic crystal (PC) nanocavities. A convolutional neural network (CNN), which consists of two convolutional layers and three fully-connected layers is trained on a large-scale dataset consisting of 12,500 nanocavities. The experimental results show that the CNN is able to achieve a state-of-the-art performance in terms of prediction accuracy (i.e., up to 99.9999%) 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 design of nanoscale lasers and photonic integrated circuits. We will open source the database and code as one of our main contributions to the photonics research community.