A Generative Model for Inverse Design of Metamaterials
This work addresses the need for systematic, inverse design of metamaterials for applications like flat lenses and holographic imaging, representing a novel method for a known bottleneck.
The paper tackled the problem of designing metamaterials by replacing intuition-guided electromagnetic simulations with a deep learning generative model that produces geometric patterns from desired optical spectra, achieving an average accuracy of about 0.9 in matching spectra.
The advent of two-dimensional metamaterials in recent years has ushered in a revolutionary means to manipulate the behavior of light on the nanoscale. The effective parameters of these architected materials render unprecedented control over the optical properties of light, thereby eliciting previously unattainable applications in flat lenses, holographic imaging, and emission control among others. The design of such structures, to date, has relied on the expertise of an optical scientist to guide a progression of electromagnetic simulations that iteratively solve Maxwell's equations until a locally optimized solution can be attained. In this work, we identify a solution to circumvent this intuition-guided design by means of a deep learning architecture. When fed an input set of optical spectra, the constructed generative network assimilates a candidate pattern from a user-defined dataset of geometric structures in order to match the input spectra. The generated metamaterial patterns demonstrate high fidelity, yielding equivalent optical spectra at an average accuracy of about 0.9. This approach reveals an opportunity to expedite the discovery and design of metasurfaces for tailored optical responses in a systematic, inverse-design manner.