A deep learning method for empirical spectral prediction and inverse design of all-optical nonlinear plasmonic ring resonator switches.

ABSTRACT: All-optical plasmonic switches (AOPSs) utilizing surface plasmon polaritons are well-suited for integration into photonic integrated circuits (PICs) and play a crucial role in advancing all-optical signal processing. The current AOPS design methods still rely on trial-and-error or empirical approaches. In contrast, recent deep learning (DL) advances have proven highly effective as computational tools, offering an alternative means to accelerate nanophotonics simulations. This paper proposes an innovative approach utilizing DL for spectrum prediction and inverse design of AOPS. The switches employ circular nonlinear plasmonic ring resonators (NPRRs) composed of interconnected metal-insulator-metal waveguides with a ring resonator. The NPRR switching performance is shown using the nonlinear

SUBMITTER: Adibnia E 

PROVIDER: S-EPMC10924975 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature