  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.
