Machine Learning-Based Optimization of Chiral Photonic Nanostructures: Evolution- and Neural Network-Based Design
This work addresses the need for rapid and efficient optimization of chiral photonic structures for applications in nanophotonics and optical information processing, representing an incremental advancement by applying existing machine learning methods to a specific domain.
The authors tackled the design of chiral photonic nanostructures for manipulating light-matter interactions, using evolutionary algorithms and neural networks to optimize optical properties, achieving a frequency-dependent modification in circular polarization with potential applications in visible light and transition-metal dichalcogenide exciton resonances.
Chiral photonics opens new pathways to manipulate light-matter interactions and tailor the optical response of meta-surfaces and -materials by nanostructuring nontrivial patterns. Chirality of matter, such as that of molecules, and light, which in the simplest case is given by the handedness of circular polarization, have attracted much attention for applications in chemistry, nanophotonics and optical information processing. We report the design of chiral photonic structures using two machine learning methods, the evolutionary algorithm and neural network approach, for rapid and efficient optimization of optical properties for dielectric metasurfaces. The design recipes obtained for visible light in the range of transition-metal dichalcogenide exciton resonances show a frequency-dependent modification in the reflected light's degree of circular polarization, that is represented by the difference between left- and right-circularly polarized intensity. Our results suggest the facile fabrication and characterization of optical nanopatterned reflectors for chirality-sensitive light-matter coupling scenarios employing tungsten disulfide as possible active material with features such as valley Hall effect and optical valley coherence.