Automated Model Generation for Microwave Components Using Adjoint Neural Network and EM Sensitivity Analysis

This letter proposes an advanced automated model generation (AMG) of microwave components using an adjoint artificial neural network (ANN) and electromagnetic (EM) sensitivity analysis. EM sensitivities are integrated into the AMG process for the first time to speed up the automated ANN model development. We propose a novel adaptive sampling algorithm, combining EM sensitivities and interpolation techniques to dynamically determine the optimal sampling scheme. This ensures obtaining the most accurate ANN with minimal data. We also propose a new adjoint ANN training method with EM sensitivities to automatically determine the suitable ANN structure. By utilizing both EM data and EM sensitivities, the modeling efficiency of the proposed AMG is effectively improved compared to existing AMGs. Two microwave modeling examples are provided to demonstrate the proposed algorithm.