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We propose, for the first time, neural space-mapping (NSM) optimization for electromagnetic-based design. NSM optimization exploits our space-mapping (SM)-based neuromodeling techniques to efficiently approximate the mapping. A novel procedure that does not require troublesome parameter extraction to predict the next point is proposed. The initial mapping is established by performing upfront fine-model analyses at a reduced number of base points. Coarse-model sensitivities are exploited to select those base points. Huber optimization is used to train, without testing points, simple SM-based neuromodels at each NSM iteration. The technique is illustrated by a high-temperature superconducting quarter-wave parallel coupled-line microstrip filter and a bandstop microstrip filter with quarter-wave resonant open stubs.

Abstract-We introduce a tuning space-mapping technology for microwave design optimization. The general tuning space-mapping algorithm is formulated, which is based on a so-called tuning model, as well as on a calibration process that translates the adjustment of the tuning model parameters into relevant updates of the design variables. The tuning model is developed in a fast circuit-theory based simulator and typically includes the fine model data at the current design in the form of the properly formatted scattering parameter values. It also contains a set of tuning parameters, which are used to optimize the model so that it satisfies the design specification. The calibration process may involve analytical formulas that establish the dependence of the design variables on the tuning parameters. If the formulas are not known, the calibration process can be performed using an auxiliary space-mapping surrogate model. Although the tuning space mapping can be considered to be a specialized case of the standard space-mapping approach, it can offer even better performance because it enables engineers to exploit their experience within the context of efficient space mapping. Our approach is demonstrated using several microwave design optimization problems.Index Terms-Computer-aided design (CAD), engineering optimization, space mapping, surrogate models, tuning.

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Abstract-There is a revival of the interest in adjoint sensitivity analysis techniques. This is partly because current computer-aided-design software based on full-wave electromagnetic (EM) solvers remains too slow for the purposes of practical high-frequency structure design despite the increasing capacity of computers. The adjoint-variable methods for design sensitivity analysis offer computational speed and accuracy. They can be used for efficient gradient-based optimization, in tolerance and yield analysis. Adjoint-based sensitivity analysis for circuits has been well studied and extensively covered in the microwave literature. In comparison, sensitivities with full-wave analysis techniques have attracted little attention, and there have been few applications into feasible and versatile algorithms. We review adjoint-variable methods used in high-frequency structure design with both circuit analysis techniques and full-wave EM analysis techniques. A brief discussion on adjoint-based sensitivity analysis for nonlinear dynamic systems is also included.