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- Title
Optimization and Prediction of Spectral Response of Metasurfaces Using Artificial Intelligence.
- Authors
Sarma, Raktim; Goldflam, Michael; Donahue, Emily; Pribisova, Abigail; Gennaro, Sylvain; Wright, Jeremy; Brener, Igal; Briscoe, Jayson
- Abstract
Hot-electron generation has been a topic of intense research for decades for numerous applications ranging from photodetection and photochemistry to biosensing. Recently, the technique of hot-electron generation using non-radiative decay of surface plasmons excited by metallic nanoantennas, or meta-atoms, in a metasurface has attracted attention. These metasurfaces can be designed with thicknesses on the order of the hot-electron diffusion length. The plasmonic resonances of these ultrathin metasurfaces can be tailored by changing the shape and size of the meta-atoms. One of the fundamental mechanisms leading to generation of hot-electrons in such systems is optical absorption, therefore, optimization of absorption is a key step in enhancing the performance of any metasurface based hot-electron device. Here we utilized an artificial intelligence-based approach, the genetic algorithm, to optimize absorption spectra of plasmonic metasurfaces. Using genetic algorithm optimization strategies, we designed a polarization insensitive plasmonic metasurface with 90% absorption at 1550 nm that does not require an optically thick ground plane. We fabricated and optically characterized the metasurface and our experimental results agree with simulations. Finally, we present a convolutional neural network that can predict the absorption spectra of metasurfaces never seen by the network, thereby eliminating the need for computationally expensive simulations. Our results suggest a new direction for optimizing hot-electron based photodetectors and sensors.
- Subjects
PLASMONICS; SPECTRAL sensitivity; ARTIFICIAL intelligence; CONVOLUTIONAL neural networks; SURFACE plasmons; LIGHT absorption; METALLIC surfaces
- Publication
Crystals (2073-4352), 2020, Vol 10, Issue 12, p1114
- ISSN
2073-4352
- Publication type
Article
- DOI
10.3390/cryst10121114