Deep Learning-Assisted Enhanced Fano Resonances in Symmetry-Breaking SOI Metasurface

Abstract

Metasurfaces analogues of Fano resonances provide a powerful platform for high sensitivity sensing, nonlinear optics, and light manipulation. However, previous Fano-resonant metasurfaces usually are not compatible with silicon complementary metal-oxide semiconductor circuits due to their hybrid material structures and large non-radiative loss. Herein, we theoretically demonstrate a silicon-on-insulator metasurface (SOIM) enhancing Fano resonances by using a tandem neural network design. Multiple Fano resonances with high Q-factor have been observed in the symmetry-breaking SOIM. The Fano-resonant mechanism of the SOIM is analyzed. Additionally, the spectral features of the Fano-resonant SOIM as a function of the symmetry tuning factor of the double silicon nanobars and the environment refractive index are also investigated. The result shows that the Fano-resonant SOIM as a methanol sensor with a sensitivity of 310 nm/RIU can achieve an overall figure of merit of 195 in the near-infrared spectral regime. The designed Fano-resonant SOIM shows enormous potential applications in highly sensitive sensors and light-matter interaction enhancement.

Keywords

• Neural networks
• refractive index sensing
• metamaterials
• silicon-on-insulator