Deep learning-based modeling of photonic crystal nanocavities

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. (c) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Automated summary

A DL-based approach accurately models the relationship between design parameters and the Q factor of PC nanocavities, achieving high accuracy and convergence speed. This method overcomes shortcomings of existing methods, paving the way for rapid design of nanoscale lasers and photonic integrated circuits.