Inverse design and forward modelling in nanophotonics using deep learning

Recent introduction of deep learning into nanophotonics has enabled efficient inverse design process. Once the deep learning network is trained, it allows fast inverse design for multiple design tasks. In this talk, we show several inverse designing nanophotonic structures using deep learning. We firstly discuss inverse design methods that increase the degree of freedom of design possibilities. These attempts include designing arbitrary shapes of nanophotonic structures, that are not limited to pre-defined structures, and designing both types of materials and structural parameters simultaneously. Also, for simultaneous design of materials and structural parameters, we developed a novel objective function that combines regression and classification problems. After then, we use reinforcement learning to optimize structure parameters. Several meta-devices including dielectric color filter, high efficiency hologram, perfect absorber, plasmonic structures, dielectric gratings and microwave antenna are designed using this method. If time allows, forward modelling methods will be discussed with a few examples.