A Hybrid Neural and Circuit-Based Model Structure for Microwave Modeling

Neural networks have recently gained attention as powerful vehicles to microwave modeling, simulation, and optimization. A hybrid neural network structure incorporating prior circuit knowledge is proposed for modeling microwave components. In the proposed structure, a sub neural network establishes the mapping between original model input space and approximate circuit model input space. The neural network can learn such complicated space-mapping by training with EM simulation data. The hybrid neural models are computationally efficient and have an accuracy that is comparable to EM simulation. The proposed methodology is demonstrated through practical microwave modeling examples.