Neural based modeling of nonlinear microwave devices and circuits

Artificial Neural Networks (ANN) have been recently recognized as a useful tool for modeling and design optimization problems in RF/microwave Computer Aided Design (CAD). Neural network models can be trained from measured or simulated microwave data. The resulting neural models can be used during microwave design to provide instant answers to the task they learned, which otherwise are computationally expensive. This thesis addresses the application of ANN to efficient and accurate modeling of nonlinear microwave devices and circuits.; Major contributions of the thesis include the adjoint neural network (ADJNN) technique, the dynamic neural network (DNN) technique and an advanced neural model extrapolation technique. The ADJNN and the DNN are two approaches that address neural based nonlinear microwave device/circuit modeling in two different cases, i.e., in the cases that the simplified topology information of such device/circuit is available or unavailable. The ADJNN approach uses a combination of circuit and neural models, where the circuit dynamics are defined by the topology and the nonlinearity is defined by ANNs. The circuit topology can be obtained from empirical models or equivalent circuits. The ADJNN technique can be used to develop a neural based model for the nonlinear device/circuit using direct current (DC) and small-signal data. The trained model can be subsequently used to predict large-signal effects in microwave circuit or system design.; The DNN approach can be used to directly model the nonlinear microwave device or circuit from its input-output data without having to rely on its internal details. The DNN model itself can represent both dynamic effects and nonlinearity. An algorithm is developed to train the model with time or frequency domain large-signal information. Efficient representations of DNN are described for convenient incorporation of the trained model into high-level circuit or system simulation.; Further progress of neural based nonlinear microwave device/circuit modeling is made by the advanced neural model extrapolation technique. It enables neural based nonlinear device/circuit models to be robustly used in iterative computational loops, e.g., optimization and Harmonic Balance (HB), involving neural model inputs as iterative variables. Compared with standard neural based methods (i.e., without extrapolation), the proposed technique improves neural based microwave optimization and makes nonlinear circuit design significantly more robust.; The techniques developed in this thesis provide enhanced efficiency, accuracy and robustness for neural based nonlinear microwave device/circuit modeling. It is a unique contribution to further realizing the flexibility of neural based approaches in nonlinear microwave modeling, simulation and optimization.