Latent learning for design and knowledge discovery in nanophotonics

Abstract

A new deep-learning approach based on dimensionality reduction techniques for the design and knowledge discovery in nanophotonic structures will be presented. It is shown that reducing the dimensionality of the response and design spaces in a class of nanophotonic structures can provide new insight into the physics of light-matter interaction in such nanostructures while facilitating their inverse design. These unique features are achieved while considerably reducing the computation complexity through dimensionality reduction. It is also shown that this approach can enable an evolutionary design method in which the initial design can be evolved intelligently into an alternative with favorable specification like less complexity, more robustness, less power consumption, etc. In addition to providing the details about the fundamental aspects of the latent learning approach, its application to design of reconfigurable metasurfaces will be demonstrated.