Qingsha S. Cheng

Accelerated Microwave Design Optimization With Tuning Space Mapping

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.

Combining Coarse and Fine Models for Optimal Design

In this article we reviewed the implicit space mapping concept. We illustrated it using a simple tapped-line microstrip filter example. We demonstrated the robustness of our approach by performing an accurate design of a microstrip hairpin filter. A detailed and easy-to-follow design optimization procedure was provided and the Agilent ADS implementation of the algorithm was described. A good electromagnetically validated design in Sonnet em was obtained in a few fine model simulations. Implicit space mapping is a simple approach to combine a circuit-theory based CAD model and an EM simulator model to achieve fast and accurate optimal design and modeling.

Space Mapping Design Framework Exploiting Tuning Elements

Inspired by the ideas of ¿simulator-based¿ tuning, implicit space mapping, and surrogate optimization, we propose an implementable microwave design framework. In this framework, we alter an electromagnetic (EM) model by embedding suitable tuning elements. The resulting tuning model is aligned with the original unaltered EM model. We then designate the aligned tuning model as surrogate for design optimization purposes. We illustrate our tuning space mapping framework using a simple microstrip line example. Several microwave examples, including a low-temperature co-fired ceramic filter demonstrate the frameworks implementation and robustness.

Robust Trust-Region Space-Mapping Algorithms for Microwave Design Optimization

Convergence is a well-known issue for standard space-mapping optimization algorithms. It is heavily dependent on the choice of coarse model, as well as the space-mapping transformations employed in the optimization process. One possible convergence safeguard is the trust region approach where a surrogate model is optimized in a restricted neighborhood of the current iteration point. In this paper, we demonstrate that although formal conditions for applying trust regions are not strictly satisfied for space-mapping surrogate models, the approach improves the overall performance of the space-mapping optimization process. Further improvement can be realized when approximate fine model Jacobian information is exploited in the construction of the space-mapping surrogate. A comprehensive numerical comparison between standard and trust-region-enhanced space mapping is provided using several examples of microwave design problems.

Reliable Space-Mapping Optimization Integrated With EM-Based Adjoint Sensitivities

We present a robust space mapping (SM) algorithm exploiting electromagnetic (EM)-based adjoint sensitivities for microwave design optimization. Our approach utilizes low-cost EM-based adjoint sensitivities and trust region methods to improve an SM algorithm at three levels, which are: 1) to build a better overall surrogate while ensuring convergence; 2) to speed up and safeguard the parameter extraction steps; and 3) to speed up and safeguard the surrogate optimization process. We describe the implementation at each level in detail. We review relevant adjoint sensitivity analysis methods. We also review prior SM methods that exploit both sensitivity and adjoint sensitivity. We summarize these methods in four categories. We compare our proposed approach with them. Efficiency, robustness, and versatility of our method are demonstrated by three design examples: an antenna, a planar filter, and a 3-D resonator filter. We compare the results with those obtained by SM without using adjoint sensitivity information and by direct optimization of the high-fidelity EM models.

Progress in Simulator-Based Tuning—The Art of Tuning Space Mapping [Application Notes]

We discuss tuning space mapping (port-tuning) techniques that can significantly reduce time and effort for design closure. We elaborate on various possible approaches. We distinguish between Type 1 and Type 0 embedding to indicate how tuning elements may be introduced into EM simulations to form suitable tuning models or surrogates. We optimize and update such surrogates iteratively to predict good EM designs. We illustrate the techniques using a simple bandstop filter and demonstrate their power using more complex filter design examples. Finally, we discuss from a physics point of view the possible locations of cuts, the effects of the cutting and reconnection, and we compare models that employ internal cuts with models that consider combinations of submodels.

Tuning space mapping: A novel technique for engineering design optimization

We introduce a tuning space mapping (TSM) technology for microwave design optimization. For the first time, we formulate the novel TSM concept and show how it relates to the standard space mapping methodology. The new method is based on a so-called tuning model that is created using engineering expertise and knowledge of the design problem, but also utilizes the efficiency of space mapping for translating the adjustment of the tuning parameters into relevant updates of the design variables. We illustrate our approach through optimization of a high-temperature superconducting (HTS) filter.

Accelerating Space Mapping Optimization with Adjoint Sensitivities

We propose a procedure for accelerating the space mapping optimization process. Exploiting both fine- and surrogate-model sensitivity information, a good mapping between the two model spaces is efficiently obtained. This results in a significant speed-up over direct gradient-based optimization of the original fine model and enhanced performance compared with other space mapping approaches. Our approach utilizes commercially available software with adjoint sensitivity analysis capabilities. It is illustrated through an example.

Tuning space mapping optimization exploiting embedded surrogate elements

Inspired by the tuning space mapping concept, the implicit space mapping concept, and surrogate optimization, we propose a simple microwave design optimization/tuning technique. Utilizing co-calibrated ports, our new tuning model is created by substituting sections in the electromagnetic (EM) model with corresponding sections of designable equivalent (surrogate) elements. Several microwave examples demonstrate how the tuning model can be used for design purposes.

An Implicit Space Mapping Technique for Component Modeling

The authors present an implicit space mapping (ISM) technique for modeling microwave and RF components, the first attempt to employ ISM-based surrogates specifically for modeling. The proposed technique calibrates the surrogate by mapping certain preassigned parameters. This surrogate employs linearly interpolated preassigned parameters. Using a three-section transformer, the authors demonstrate that our technique is simple to apply within Agilent ADS. It shows that they can easily enhance an HTS filter empirical model to the accuracy of an electromagnetic model, Sonnets em