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

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 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. 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 .