Single and double plasmon induced absorption (PIA) effects have been numerically achieved in a metal-insulator-metal (MIM) waveguides end-coupled with resonators structure. Here, the structure composed of two MIM waveguides and three side-coupled rectangular resonators is proposed to generate double PIA effects. A multimode coupling mechanism derived from the coupled mode theory is established to describe the spectral features, which is greatly agree with the simulation results, may provide a guideline for designing and analyzing the integrated plasmonic devices based on the multiple PIA effects. What’s more, dynamical control of the amplitude and bandwidth of the multiple PIA effects can be achieved by means of filling poly (methy1 methacrylate) or Kerr material in the Fabry-Perot resonators. Compared with previous reports, the multiple PIA effects are analyzed theoretically in a plasmonic waveguides end-coupled with resonators structure, will have practical applications in plasmonic filters, modulators, sensors, switches and fast light in highly integrated plasmonic circuits.
In this article, we propose a novel method using machine learning, especially for artificial neural networks (ANNs) to achieve variability analysis and performance optimization of the plasmonic refractive index sensor (RIS). A Fano resonance (FR) based RIS which consisted of two plasmonic waveguides end-coupled to each other by an asymmetrical square resonator is taken as an illustration to demonstrate the effectiveness of the ANNs. The results reveal that the ANNs can be used in fast and accurate variability analysis because the predicted transmission spectrums and transmittances generated by ANNs are approximate to the actual simulated results. In addition, the ANNs can effectively solve the performance optimization and inverse design problems for the RIS by predicting the structure parameters for RIS accurately. Obviously, our proposed method has potential applications in optical sensing, device design, optical interconnects and so on.