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In this article, we propose a novel approach to achieve spectrum prediction,parameter fitting, inverse design and performance optimization for theplasmonic waveguide coupled with cavities structure (PWCCS) based on artificialneural networks (ANNs). The Fano resonance and plasmon induced transparencyeffect originated from the PWCCS have been selected as illustrations to verifythe effectiveness of ANNs. We use the genetic algorithm to design the networkarchitecture and select the hyper-parameters for ANNs. Once ANNs are trained byusing a small sampling of the data generated by Monte Carlo method, thetransmission spectrums predicted by the ANNs are quite approximate to thesimulated results. The physical mechanisms behind the phenomena are discussedtheoretically, and the uncertain parameters in the theoretical models arefitted by utilizing the trained ANNs. More importantly, our results demonstratethat this model-driven method not only realizes the inverse design of the PWCCSwith high precision but also optimizes some critical performance metrics fortransmission spectrum. Compared with previous works, we construct a novelmodel-driven analysis method for the PWCCS which are expected to havesignificant applications in the device design, performance optimization,variability analysis, defect detection, theoretical modeling, opticalinterconnects and so on.

Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks