Using machine learning to optimize optical response of all-dielectric core-shell nanoparticle

When designing a multi-layer core-shell nanoparticle for a desired optical response, it is necessary to understand what key structural parameters are at play. Here we utilize a neural network to train, and subsequently compute Mie optical responses for multi-layer nanoparticles, consisting of amorphous silicon as the core and silicon dioxide as the coating. Once trained, the neural network can efficiently simulate optical scattering responses faster than traditional transfer matrix method. Finally, our neural network is used to solve the inverse design problem in order to develop an understanding of the structural parameters (diameter, layer thickness and dielectric) that affect the quality factors of the nanoparticle optical response. Furthermore, we provide insights on how different loss functions used in the search algorithm can lead to profound differences in the optimization process, while still providing accurate optical spectra for our core-shell nanoparticles.