Design of chiral plasmonic metamaterials based on interpretable deep learning

Chiral plasmonic metamaterials can amplify chiral signals, resulting in circular dichroism (CD) responses that are several orders of magnitude far beyond those of nature. However, the design process of chiral plasmonic metamaterials based on conventional methods is time-consuming. In recent years, the combination of deep learning (DL) and nanophotonics have accelerated the design of nanophotonic devices. Here, we construct the fully connected neural network model for the forward prediction and inverse design of chiral plasmonic metamaterials structures and introduce the permutation importance approach to optimize the model and increase its interpretability. Our experimental results show that using the peak magnitude of CD and the corresponding wavelength instead of the entire spectrum as the output in the forward prediction improves the accuracy of the peak magnitude of CD prediction, avoids the introduction of auxiliary networks, and simplifies the network structure; the permutation importance analysis shows that the gold length of the resonator is the most critical structural parameter affecting the CD response. In the inverse design, the permutation importance method helps us to make feature selections for the input of the network. By reducing 251 inputs (the whole CD spectrum) to 4 inputs (the peak magnitude of CD and the corresponding wavelength), the network can still maintain a good prediction performance and decrease the training time of the network. Our proposed method can be extended not only to other DL models to study the CD response of chiral metamaterials but also to other areas where DL is combined with metamaterials to accelerate the system optimization and design process of nanophotonic devices.