Semi‐Supervised Learning Leveraging Denoising Diffusion Probabilistic Models for the Characterization of Nanophotonic Devices

Albeit the recent successful demonstrations of nanophotonic device designs leveraging data-driven supervised learning methods, several challenges still remain. One of the primary constraints of these methods is their computational cost because they require a massive amount of highly time-consuming full-wave electromagnetic simulations. In this study, semi-supervised learning methods are implemented to avoid this issue. Photonic crystal waveguide devices, which offer interesting light-matter interactions such as the slow-light effect and the filtering effect, are employed to validate the methodologies. To utilize unlabeled data within this framework, a novel deep generative model, the denoising diffusion probabilistic model is introduced. Next, a pseudo-labeling process is introduced to assign synthetic labels to the unlabeled data. The performance of a semi-supervised learning method is compared with a supervised learning scheme, demonstrating that the performance of the neural network can be enhanced without additional numerical calculations. This method holds extensive potential to elevate the efficiency of designing and characterizing future nanophotonic devices. In this study, semi-supervised learning methods are implemented to accelerate the performance of the data-driven forward neural network models for the characterization of nanophotonic devices. To utilize unlabeled data within this framework, a novel deep generative model, the denoising diffusion probabilistic model are introduced. As a result, the performance of the neural network can be enhanced without additional numerical calculations. image