Designing nanophotonic structures using conditional-deep convolutional generative adversarial networks
This provides a fast and convenient approach for designing complex nanophotonic structures with desired optical properties, addressing a bottleneck in nanophotonics research.
The authors tackled the problem of time-consuming iterative simulations in nanophotonics by using conditional deep convolutional generative adversarial networks to design nanophotonic antennae from input reflection spectra, generating designs as images that matched the spectra well in simulations.
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.