  Data-driven design approaches based on deep-learning have been introduced in
nanophotonics to reduce time-consuming iterative simulations which have been a
major challenge. Here, we report the first use of conditional deep
convolutional generative adversarial networks to design nanophotonic antennae
that are not constrained to a predefined shape. For given input reflection
spectra, the network generates desirable designs in the form of images; this
form allows suggestions of new structures that cannot be represented by
structural parameters. Simulation results obtained from the generated designs
agreed well with the input reflection spectrum. This method opens new avenues
towards the development of nanophotonics by providing a fast and convenient
approach to design complex nanophotonic structures that have desired optical
properties.
