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Deep learning assisted design of high reflectivity metamirrors
Optics Express
|February 25, 2022
Summary
Machine learning now designs advanced optical metasurfaces for precise applications. A novel tandem neural network achieves high reflectivity and minimal phase mismatch, rivaling dielectric mirrors.
Area of Science:
- Nanophotonics and Metamaterials
- Machine Learning in Optics
Background:
- Optical metasurfaces offer precise control over electromagnetic waves.
- Machine learning (ML) aids in achieving complex optical functionalities.
- Current ML methods face challenges meeting stringent optical performance requirements for metasurface devices, especially in high-precision optical metrology.
Purpose of the Study:
- To develop an ML framework capable of designing optical metasurfaces that meet high-performance criteria.
- To create a focusing metamirror with superior optical characteristics.
Main Methods:
- Utilized a tandem neural network framework for metasurface design.
- Optimized the design for high reflectivity and minimal phase mismatch.
Main Results:
- Achieved a mean reflectivity (Rmean) of 99.993% and maximum reflectivity (Rmax) of 99.9998%.
- Attained a minimal phase mismatch (Δϕ) of 0.016%.
- The performance is comparable to state-of-the-art dielectric mirrors.
Conclusions:
- The tandem neural network framework effectively designs high-performance optical metasurfaces.
- This approach enables metasurface devices suitable for demanding applications like optical metrology.
- The developed focusing metamirror demonstrates performance on par with conventional high-quality optical components.
