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Behavioral models such as macromodels, surrogate models, metamodels, and response surface models have many applications in diverse research domains such as aerodynamics [1], hydrology [2], mechanical engineering [3], and many more. When considering the design flow of electronic devices, these models are often used to characterize the time- or frequency- dependent behavior of an electronic component while taking all electromagnetic (EM) phenomena into account: crosstalk, attenuation, dispersion, and coupling effects for example [4]. Such models are of crucial importance for efficient design space exploration, design optimization, and sensitivity analysis [5], [6]. A key advantage is that they are calculated independently of the device's physics and that they are valid for over a wide range of design variables, taking into account multiple geometrical layout or substrate features. Additionally, the models can easily be linked together in a model cascade. [ABSTRACT FROM PUBLISHER]

This paper presents a new advance in Neuro-space mapping (Neuro-SM) techniques for modeling nonlinear microwave devices. Suppose that existing device models (namely, coarse models) cannot match the behavior of a new device (referred to as the fine model). By neural network mapping of the voltage and current signals from the coarse to the fine models, Neuro-SM can modify the behavior of the coarse model to match that of the fine model. However, the efficiency of mapping depends on both the mapping structure and the coarse model. In this paper, a structural optimization technique is presented to achieve optimal combinations of mapping structure and coarse model. An aggressive optimization formulation exploring detailed structural variations in both the mapping and the coarse model is proposed, where the internal branches of coarse models and separate mappings for the voltage and current at gate and drain are used as basic topology variables. The formulation of such a structural optimization by an evolutionary optimization algorithm is proposed. Numerical examples of metal–semiconductor field-effect transistor and high electron-mobility transistor modeling demonstrate that, by using the proposed algorithm, optimal combinations of space mapping and coarse model structures can be achieved leading to the best modeling accuracy with the simplest mapping function. [ABSTRACT FROM AUTHOR]