Hybrid deep learning for design of nanophotonic quantum emitter lenses

Inverse design of nanophotonic structures has allowed unprecedented control over light. These design processes however are accompanied with challenges, such as their high sensitivity to initial conditions, computational expense, and complexity in integrating multiple design constraints. Machine learning approaches, however, show complementary strengths, allowing huge sample sets to be generated nearly instantaneously, and with transfer learning, allowing modifications in design parameters to be integrated with limited retraining. Herein we investigate a hybrid deep learning approach, leveraging the accuracy and performance of adjoint-based topology optimization to produce a high-quality training set for a convolutional generative network. We specifically explore this in the context of 3D nanophotonic lenses, used for focusing light between plane-waves and single-point, single-wavelength sources such as quantum emitters. We demonstrate that this combined approach allows higher performance than adjoint optimization alone when additional design constraints are applied; can generate large datasets (which further allows faster iterative training to be performed); and can utilize transfer learning to be retrained on new design parameters with very few new training samples. This process can be used for general nanophotonic design, and is particularly beneficial when a range of design parameters and constraints would need to be applied.