  Complex nanophotonic structures hold the potential to deliver exquisitely
tailored optical responses for a range of applications. Metal-insulator-metal
(MIM) metasurfaces arranged in supercells, for instance, can be tailored by
geometry and material choice to exhibit a variety of absorption properties and
resonant wavelengths. With this flexibility, however, comes a vast space of
design possibilities that classical design paradigms struggle to effectively
navigate. To overcome this challenge, here we demonstrate a tandem residual
network approach to efficiently generate multiplexed supercells through inverse
design. By using a training dataset with several thousand full-wave
electromagnetic simulations in a design space of over three trillion possible
designs, the deep learning model can accurately generate a wide range of
complex supercell designs given a spectral target. Beyond inverse design, the
presented approach can also be used to explore the structure-property
relationships of broadband absorption and emission in such supercell
configurations. Thus, this study demonstrates the feasibility of
high-dimensional supercell inverse design with deep neural networks that is
applicable to complex nanophotonic structures composed of multiple subunit
elements that may exhibit coupling.
