Freeform Diffractive Metagrating Design Based on Generative Adversarial Networks
This provides a more efficient design tool for metasurface engineers, though it is incremental as it builds on existing generative methods.
The paper tackled the challenge of designing high-performance metasurfaces by using generative adversarial networks to produce topologically complex metagratings from optimized images, achieving high efficiency across deflection angles and wavelengths with iterative refinement.
A key challenge in metasurface design is the development of algorithms that can effectively and efficiently produce high performance devices. Design methods based on iterative optimization can push the performance limits of metasurfaces, but they require extensive computational resources that limit their implementation to small numbers of microscale devices. We show that generative neural networks can train from images of periodic, topology-optimized metagratings to produce high-efficiency, topologically complex devices operating over a broad range of deflection angles and wavelengths. Further iterative optimization of these designs yields devices with enhanced robustness and efficiencies, and these devices can be utilized as additional training data for network refinement. In this manner, generative networks can be trained, with a onetime computation cost, and used as a design tool to facilitate the production of near-optimal, topologically-complex device designs. We envision that such data-driven design methodologies can apply to other physical sciences domains that require the design of functional elements operating across a wide parameter space.