Distinguishing the optical properties of plasmonics and dielectric nanostructures via machine learning*

We demonstrate how machine learning, coupled with dimensionality reduction techniques, allows us to understand and classify the optical response of nanophotonic components with characteristic plasmonic and Mie-dielectric signatures. To reduce the dimensional space of our analytically-calculated spectra of all metallic and dielectric materials, we use principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embeddings (tSNE) as a linear and nonlinear dimensionality reduction technique respectively. Hereafter, by coupling this lower-dimensional space with an XGBoost-based multi-output regressor, we map optical spectra to the linear and nonlinear reduced maps. As experimental validation, we use a 3D electromagnetic full-field simulation solver to generate optical spectra to show that our machine learning technique can identify properties of experimentally-computed nanostructures such as size and intrinsic material properties and which dielectric environment they are in. Our methodology highlights an effective path to discover novel materials for optical sensing.