Machine learning classification for field distributions of photonic modes

Machine learning techniques can reveal hidden structures in large amounts of data and have the potential to replace analytical scientific methods. Electromagnetic simulations of photonic nanostructures often produce data in significant amounts, particularly when three-dimensional field distributions are calculated. An optimisation task, aiming at increased light yield from emitters interacting with photonic nanostructures, enforces systematic analysis of these data. Here we present a method that combines finite element simulations and clustering for the identification of photonic modes with large local field energies and specific spatial properties. For illustration, we use an experimental–numerical data set of quantum dot fluorescence on a photonic crystal surface. The application of Gaussian mixture model-based clustering allows to reduce the electric field distributions to a minimal subset of prototypes and the identification of characteristic spatial mode profiles. The presented clustering method potentially enables systematic optimisation of nanostructures for biosensing, bioimaging, and photon upconversion applications. Machine learning techniques are increasingly expanding their capabilities of making predictions on data across a variety of fields. The authors present a machine learning based approach capable of classifying the three-dimensional spatial electromagnetic field distributions of photonic crystals.