Optimization and Prediction of Spectral Response of Metasurfaces Using Artificial Intelligence

• Published in: Crystals (Basel) Vol. 10; no. 12; p. 1114
• Main Authors: Sarma, Raktim, Goldflam, Michael, Donahue, Emily, Pribisova, Abigail, Gennaro, Sylvain, Wright, Jeremy, Brener, Igal, Briscoe, Jayson
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2020; MDPI
• Subjects: Absorption spectra; Artificial intelligence; Artificial neural networks; Computer simulation; Design; Diffusion length; Electron diffusion; Electrons; Genetic algorithms; Ground plane; hot-electron generation; Metasurfaces; NANOSCIENCE AND NANOTECHNOLOGY; Neural networks; Optimization; Photochemistry; plasmonic metasurface; Plasmonics; Plasmons; Spectral sensitivity
• ISSN: 2073-4352; 2073-4352
• DOI: 10.3390/cryst10121114

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.