  In recent years, the development of nanophotonic devices has presented a
revolutionary means to manipulate light at nanoscale. Recently, artificial
neural networks (ANNs) have displayed powerful ability in the inverse design of
nanophotonic devices. However, there is limited research on the inverse design
for modeling and learning the sequence characteristics of a spectrum. In this
work, we propose a novel deep learning method based on an improved recurrent
neural networks to extract the sequence characteristics of a spectrum and
achieve inverse design and spectrum prediction. A key feature of the network is
that the memory or feedback loops it comprises allow it to effectively
recognize time series data. In the context of nanorods hyperbolic
metamaterials, we demonstrated the high consistency between the target spectrum
and the predicted spectrum, and the network learned the deep physical
relationship concerning the structural parameter changes reflected on the
spectrum. Moreover, the proposed model is capable of predicting an unknown
spectrum based on a known spectrum with only 0.32% mean relative error. We
propose this method as an effective and accurate alternative to the application
of ANNs in nanophotonics, paving way for fast and accurate design of desired
devices.

Optics