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Session: TU4A3:30 PM Tuesday, May 25, 2010 Room: 203B |
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Session: TU4A | Advances in Space Mapping Technologies for Design Optimization |
Chair: | Paul Draxler, Qualcomm, Inc. and UCSD |
Co-Chair: | Jose E. Rayas-Sanchez, ITESO |
Abstract: | In this session, five papers offer new algorithms for design, modeling, and statistical analysis. These algorithms show the applicability of space mapping techniques to novel problems of increasing complexity in the areas of interconnects, filters, antennas, and metamaterial design. |
| | TU4A-1 | Response Corrected Tuning Space Mapping for Yield Estimation and Design Centering | 3:30 PM-3:50 PM | Q. S. Cheng1, J. W. Bandler1, S. Koziel2, 1McMaster University, Hamilton, Canada, 2Reykjavik University , Reykjavik, Iceland |
(1083) | We enhance a tuning space mapping algorithm through a response correction. We demonstrate that the response corrected tuning model can serve as a high-performance surrogate for fast yield estimation and design centering. We illustrate yield analysis of a second-order tapped-line microstrip filter using our model. We perform yield-driven design on a double-ring filter example and verify the design with the EM model. | | |
TU4A-2 | Surrogate Modeling of Microwave Circuits Using Polynomial Functional Interpolants | 3:50 PM-4:10 PM | J. E. Rayas-Sanchez, J. Aguilar-Torrentera, J. A. Jasso-Urzua, ITESO, Tlaquepaque, Mexico |
(1710) | A new formulation for developing surrogate models using polynomial-based functional interpolants is proposed in this work. Our formulation starts from a zero-order model that can be as simple as a fixed fine model response (for cases where a continuous coarse model is not available), or it can also be a simple linear input mapped coarse model. This zero-order model is enhanced by multidimensional polynomial interpolants around a central base point in the design space. The polynomial approximation is a low-order function of the design variables, and it is used to interpolate highly accurate electromagnetic responses in a region of interest around the selected central base point. Global optimal values for the surrogate model weighting factors are efficiently obtained in closed form, using compact formulas. Our technique is illustrated by a substrate integrated waveguide interconnect with CPW transitions. | | |
TU4A-3 | Robust Multi-Fidelity Simulation-Driven Design Optimization of Microwave Structures | 4:10 PM-4:30 PM | S. Koziel, S. Ogurtsov, Reykjavik University, Reykjavik, Iceland |
(1094) | Simple and robust optimization methodology for the simulation-driven design of microwave structures is presented. Our technique exploits a set of EM-based models of increasing discretization density that are sequentially optimized with the optimal design of the “coarser” model being the initial design for the “finer” one. The final design is refined using a polynomial-based approximation model constructed from the coarse-discretization EM-simulation data and corrected using single high-fidelity EM-simulation. The presented technique is easy to implement. It is particularly suitable for structures for which simulation-driven design is a must (e.g., because of the lack of good theoretical models). Operation of our algorithm is demonstrated using two examples of planar ultrawideband antennas and a microstrip filter. In all cases, an optimal design is obtained at a low computational cost corresponding to a few high-fidelity EM simulations of the structure being optimized. | | |
TU4A-4 | Adaptively Constrained Parameter Extraction for Robust Space Mapping Optimization of Microwave Circuits | 4:30 PM-4:50 PM | S. Koziel1, J. W. Bandler2, Q. S. Cheng2, 1Reykjavik University, Reykjavik, Iceland, 2McMaster University, Hamilton, Canada |
(1073) | The performance of the space mapping (SM) optimization algorithm depends both on approximation and generalization capabilities of the underlying surrogate model. Often, the surrogate is selected by trial and error which may lead to excessive computational overhead and poor quality of the optimization outcome. Here, we introduce an adaptively constrained parameter extraction process to automatically find an approximation-generalization trade-off through the adjustment of the surrogate model parameter space. As a result, we obtain improved performance of the SM algorithm both in terms of its convergence properties and the quality of the optimized design. Verification using several microwave design problems is provided. | | |
TU4A-5 | Automated Synthesis of Resonant-type Metamaterial Transmission Lines using Aggressive Space Mapping | 4:50 PM-5:10 PM | A. Rodriguez1, J. Selga2, M. Gil2, J. Carbonell3, V. E. Boria1, F. Martín2, 1Universidad Politécnica de Valencia, Valencia, Spain, 2Universitat Autònoma de Barcelona, Bellaterra, Spain, 3Universidad Politécnica de Valencia, Valencia, Spain |
(1204) | A technique for the automated generation of the layout of microstrip lines loaded with complementary split ring resonators (CSRRs) is proposed. This synthesis strategy presents a great practical interest, due to the reduction in terms of computational efforts, that is achieved without any significant lack of accuracy in the final solution. The application makes use of an aggressive space mapping (ASM) algorithm based on a constrained Broyden-based input approach. In this case, the parameter extraction (PE) does not require of any optimization, since the circuital parameters of the metamaterial transmission line can be determined following a straightfordward method. | | |
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