Deep-learning enabled photonic nanostructure discovery in arbitrarily large shape sets via linked latent space representation learning

Abstract

A cross-linked autoencoder neural network for photonics nanostructure discovery effectively links geometry and spectra representations. This approach aids in rapid multiple solution inverse design and assessing their fabrication sensitivity.