Inthispaper,weproposeanovelapproachtoachievespectrumprediction,parameterfitting,inversedesign,andperformanceoptimizationfortheplasmonicwaveguide-coupledwithcavitiesstructure(PWCCS)basedonartificialneuralnetworks(ANNs).TheFanoresonanceandplasmon-inducedtransparencyeffectoriginatedfromthePWCCShavebeenselectedasillustrationstoverifytheeffectivenessofANNs.WeusethegeneticalgorithmtodesignthenetworkarchitectureandselectthehyperparametersforANNs.OnceANNsaretrainedbyusingasmallsamplingofthedatageneratedbytheMonteCarlomethod,thetransmissionspectrapredictedbytheANNsarequiteapproximatetothesimulatedresults.Thephysicalmechanismsbehindthephenomenaarediscussedtheoretically,andtheuncertainparametersinthetheoreticalmodelsarefittedbyutilizingthetrainedANNs.Moreimportantly,ourresultsdemonstratethatthismodel-drivenmethodnotonlyrealizestheinversedesignofthePWCCSwithhighprecisionbutalsooptimizessomecriticalperformancemetricsforthetransmissionspectrum.Comparedwithpreviousworks,weconstructanovelmodel-drivenanalysismethodforthePWCCSthatisexpectedtohavesignificantapplicationsinthedevicedesign,performanceoptimization,variabilityanalysis,defectdetection,theoreticalmodeling,opticalinterconnects,andsoon.
