Accurate wavelet neural-network-based model for electromagnetic optimization of microwave circuits

In this paper, a relevant automated electromagnetic (EM) optimization method and a novel, fast, and accurate artificial neural network are proposed for the efficient CAD modeling of microwave circuits. We lay the groundwork for our investigation of radial wavelet neural networks WNNs trained by BFGS (Broyden-Fletcher-Goldfarb-Shanno) and LBFGS (limited memory BFGS) algorithms and their application to determine the scattering parameters of the circuit under study. Wavelet theory may be exploited in deriving a good initialization for the neural network, and thus improved convergence of the learning algorithm. The optimization method combines a rigorous and accurate global EM analysis of the device performed with a finite-element method (FEM) and a fast neural model deduced from its segmented EM analysis. Finally, experimental results, which confirm the validity of the WNN model, and good agreement between theoretical optimization results and experimental ones are reported.