Establishing exhaustive metasurface robustness against fabrication uncertainties through deep learning

Photonic engineered materials have benefitted in recent years from exciting developments in computa-tional electromagnetics and inverse-design tools. However, a commonly encountered issue is that highly performant and structurally complex functional materials found through inverse-design can lose significant performance upon being fabricated. This work introduces a method using deep learning (DL) to exhaustively analyze how structural issues affect the robustness of metasurface supercells, and we show how systems can be designed to guarantee significantly better performance. Moreover, we show that an exhaustive study of structural error is required to make strong guarantees about the performance of engineered materials. The intro-duction of DL into the inverse-design process makes this problem tractable, enabling optimization runtimes to be measurable in days rather than months and allowing designers to establish exhaustive metasurface robustness guarantees.

Automated summary

The integration of deep learning into the inverse-design process for photonic engineered materials helps to analyze the impact of structural issues on material robustness and design systems to guarantee better performance. In-depth study of structural errors is crucial for providing strong guarantees about material performance and reducing optimization runtimes.