  In this article, we propose a novel approach to achieve spectrum prediction,
parameter fitting, inverse design and performance optimization for the
plasmonic waveguide coupled with cavities structure (PWCCS) based on artificial
neural networks (ANNs). The Fano resonance and plasmon induced transparency
effect originated from the PWCCS have been selected as illustrations to verify
the effectiveness of ANNs. We use the genetic algorithm to design the network
architecture and select the hyper-parameters for ANNs. Once ANNs are trained by
using a small sampling of the data generated by Monte Carlo method, the
transmission spectrums predicted by the ANNs are quite approximate to the
simulated results. The physical mechanisms behind the phenomena are discussed
theoretically, and the uncertain parameters in the theoretical models are
fitted by utilizing the trained ANNs. More importantly, our results demonstrate
that this model-driven method not only realizes the inverse design of the PWCCS
with high precision but also optimizes some critical performance metrics for
transmission spectrum. Compared with previous works, we construct a novel
model-driven analysis method for the PWCCS which are expected to have
significant applications in the device design, performance optimization,
variability analysis, defect detection, theoretical modeling, optical
interconnects and so on.
