Space mapping is a recognized surrogate-based optimization method to accelerate the electromagnetic (EM) design. In this article, for the first time, space mapping is elevated from solving the problem of EM optimization to solving the problem of multiphysics optimization for high power microwave filters. Multiphysics analysis, which involves the EM domain with other physics domains, is increasingly important for high-performance microwave components to obtain an accurate system design. To speed up the multiphysics design, a space-mapping-based surrogate model including a coarse model and two mapping functions is proposed in this article. We propose to use EM single physics responses as the coarse model to provide good approximations to fine model multiphysics responses. To avoid repetitive EM simulations during the surrogate model training and optimization process, the coarse model is developed using an artificial neural network (ANN). Frequency mapping and explicit input mapping are further performed to develop the proposed surrogate model. Multiple EM and multiphysics training samples are evaluated in parallel to develop the surrogate model. A trust-region algorithm, tailored to the space-mapping-based multiphysics optimization technique, is proposed to improve the convergence. By exploiting the knowledge of the coarse model established by relatively inexpensive EM data, the proposed technique can provide a larger and more efficient optimization update in each optimization iteration, consequently obtaining optimal solutions faster than the existing multiphysics optimization without space mapping. Two examples of multiphysics optimization of high-power microwave filters are used to validate the proposed technique.

This paper presents a nonlinear microwave device modeling technique that is based on time delay neural network (TDNN). The proposed technique can accurately model the nonlinear microwave devices when compared to static neural network modeling method. A new formulation is developed to allow for the proposed TDNN model to be trained with DC, small-signal, and large signal data, which can enhance the generalization of the device model. An algorithm is formulated to train the proposed TDNN model efficiently. This proposed technique is verified by GaAs metal-semiconductor-field-effect transistor (MESFET), and GaAs high-electron mobility transistor (HEMT) examples. These two examples demonstrate that the proposed TDNN is an efficient and valid approach for modeling various types of nonlinear microwave devices.

An advanced Neuro-space mapping (Neuro-SM) multiphysics parametric modeling approach for microwave passive components is proposed in this paper. The electromagnetic (EM) domain model, which represents the EM responses with respect to geometrical parameters, is regarded as a coarse model. The multiphysics domain model, which represents the multiphysics responses with respect to both geometrical parameters and multiphysics parameters, is regarded as a fine model. The proposed model is constructed by the input mapping, the output mapping and the coarse model. The input mapping is used to map multiphysics parameters to EM parameters. The output mapping is introduced to further narrow the gap between the output of the coarse model and the multiphysics data. In addition, a three-stage training method is proposed for efficiently developing the proposed multiphysics model. The proposed technique, which combines the efficiency of EM analysis and the accuracy of multiphysics analysis, can achieve better accuracy with less multiphysics data than existing modeling methods. The developed Neuro-SM multiphysics model provides accurate and fast predictions of multiphysics responses. Therefore, the design cycle of microwave passive components is shortened while the modeling cost is significantly reduced. Two microwave filter examples are utilized to demonstrate the accuracy of the proposed parametric modeling technique.

An accurate and efficient electromagnetic (EM) modeling approach based on Neuro-space mapping (Neuro-SM) is proposed for substrate integrated suspension line (SISL) devices. A new coarse model containing 1 layer of dielectric plates, 2 layers of metal plates and 2 air cavities are developed to accelerate EM model simulation speed. To improve EM model accuracy, a parameter mapping network and a frequency mapping network are added into the structure. The EM responses of the SISL devices are taken as the fine model, while the proposed coarse model and two mapping networks are considered as the advanced Neuro-SM model. A three-step training process is developed to reduce errors between the fine and Neuro-SM model. The reasonable Neuro-SM model structure and efficient training algorithm greatly simplify the SISL device modeling process and further shorten the SISL device design cycle. The experimental results of a low-pass filter with SISL structure show that the training and test errors of the proposed model are 1.1% and 1.3%, which proves that the established model could accurately represent the characteristic of the SISL device.

In this paper, an improved empirical formula modeling method using neuro-space mapping (Neuro-SM) for coupled microstrip lines is proposed. Empirical formulas with correction values are used for the coarse model, avoiding a slow trial-and-error process. The proposed model uses mapping neural networks (MNNs), including both geometric variables and frequency variables to improve accuracy with fewer variables. Additionally, an advanced method incorporating simple sensitivity analysis expressions into the training process is proposed to accelerate the optimization process. The experimental results show that the proposed model with its simple structure and an effective training process can accurately reflect the performance of coupled microstrip lines. The proposed model is more compatible than models in existing simulation software.