Surrogate-based EM Optimization Using Neural Networks for Microwave Filter Design

Abstract

A surrogate-based electromagnetic (EM) optimization using neural networks (NNs) is presented for computationally efficient microwave bandpass filter (BPF) design. This paper first describes the forward problem (EM analysis) and the inverse problems (EM design), and the two fundamental issues in BPF designs. The first issue is that the EM analysis is a time-consuming task, and the second one is that EM design highly depends on the structural optimization performed with the help of EM analysis. To accelerate the optimization design, two surrogate models of forward and inverse models are introduced here, which are built with the NNs. As a result, the inverse model can instantaneously guess initial structural parameters with high accuracy by simply inputting synthesized coupling-matrix elements into the NN. Then, the forward model in conjunction with optimization algorithm enables designers to rapidly find optimal structural parameters from the initial ones. The effectiveness of the surrogate-based EM optimization is verified through the structural designs of a typical fifth-order microstrip BPF with multiple couplings.