Automated time domain modeling of linear and nonlinear microwave circuits using recurrent neural networks

In this thesis, the recurrent neural network (RNN) is employed as a dynamic time-domain (TD) model for both linear and nonlinear microwave circuits. An automated RNN modeling technique is proposed to efficiently determine the training waveform distribution and internal RNN structure during the offline training process. This technique is an expansion of the existing automatic model generation (AMG) algorithm to support dynamic TD modeling. The automated process is used to train RNN with transient electromagnetic (EM) behavior of microwave structures for varying material and geometrical parameters. TD EM simulators are automatically driven by AMG in the appropriate manner to generate the necessary RNN training waveforms. AMG then varies the RNN structural parameters during training to learn the transient behavior with minimum RNN order while satisfying accuracy requirements. Once trained, the RNN macromodel is inserted into circuit simulators for use in circuit analysis. Automatic RNNT modeling is also applied to model nonlinear power amplifier (PA) behavior. An envelope formulation is used to specifically learn the AM/AM and AM/PM distortions due to third-generation (3G) digital modulation input. The RNN PA model is able to model these TD distortions after training and can accurately model the amplifier behavior in both time (AM/AM, AM/PM) and frequency (spectral re-growth).