Topic
Space mapping
About: Space mapping is a research topic. Over the lifetime, 446 publications have been published within this topic receiving 8169 citations.
Papers published on a yearly basis
Papers
TL;DR: In this article, the authors propose space mapping (SM) for circuit optimization utilizing a parameter space transformation, which is demonstrated by the optimization of a microstrip structure for which a convenient analytical/empirical model is assumed to be unavailable.
Abstract: We offer space mapping (SM), a fundamental new theory to circuit optimization utilizing a parameter space transformation. This technique is demonstrated by the optimization of a microstrip structure for which a convenient analytical/empirical model is assumed to be unavailable. For illustration, we focus upon a three-section microstrip impedance transformer and a double folded stub microstrip filter and explore various design characteristics utilizing an electromagnetic (EM) field simulator. We propose two distinct EM models: coarse for fast computations, and the corresponding fine for a few more accurate and well-targeted simulations. The coarse model, useful when circuit-theoretic models are not readily available, permits rapid exploration of different starting points, solution robustness, local minima, parameter sensitivities, yield-driven design and other design characteristics within a practical time frame. The computationally intensive fine model is used to verify the space-mapped designs obtained exploiting the coarse model, as well as in the SM process itself. >
698 citations
TL;DR: In this paper, the authors proposed a significantly improved space mapping (SM) strategy for electromagnetic (EM) optimization, which leverages every available EM analysis, producing dramatic results right from the first step, instead of waiting for upfront EM analyses at several base points.
Abstract: We propose a significantly improved space mapping (SM) strategy for electromagnetic (EM) optimization. Instead of waiting for upfront EM analyses at several base points, our new approach aggressively exploits every available EM analysis, producing dramatic results right from the first step. We establish a relationship between the novel SM optimization and the quasi-Newton iteration for solving a system of nonlinear equations. Approximations to the matrix of first-order derivatives are updated by the classic Broyden formula. A high-temperature superconducting microstrip filter design solution emerges after only six EM simulations with sparse frequency sweeps. Furthermore, less CPU effort is required to optimize the filter than is required by one single detailed frequency sweep. We also extend the SM concept to the parameter extraction phase, overcoming severely misaligned responses induced by inadequate empirical models. This novel concept should have a significant impact on parameter extraction of devices.
419 citations
Journal Article•
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TL;DR: A generic space-mapping optimization algorithm is formulated, explained step-by-step using a simple microstrip filter example, and its robustness is demonstrated through the fast design of an interdigital filter.
Abstract: In this article we review state-of-the-art concepts of space mapping and place them con- textually into the history of design optimization and modeling of microwave circuits. We formulate a generic space-mapping optimization algorithm, explain it step-by-step using a simple microstrip filter example, and then demonstrate its robustness through the fast design of an interdigital filter. Selected topics of space mapping are discussed, including implicit space mapping, gradient-based space mapping, the optimal choice of surrogate model, and tuning space mapping. We consider the application of space mapping to the modeling of microwave structures. We also discuss a software package for automated space-mapping optimization that involves both electromagnetic (EM) and circuit simulators.
344 citations
TL;DR: A comprehensive approach to engineering design optimization exploiting space mapping (SM) using a new generalization of implicit SM to minimize the misalignment between the coarse and fine models of the optimized object over a region of interest.
Abstract: This paper presents a comprehensive approach to engineering design optimization exploiting space mapping (SM). The algorithms employ input SM and a new generalization of implicit SM to minimize the misalignment between the coarse and fine models of the optimized object over a region of interest. Output SM ensures the matching of responses and first-order derivatives between the mapped coarse model and the fine model at the current iteration point in the optimization process. We provide theoretical results that show the importance of the explicit use of sensitivity information to the convergence properties of our family of algorithms. Our algorithm is demonstrated on the optimization of a microstrip bandpass filter, a bandpass filter with double-coupled resonators, and a seven-section impedance transformer. We describe the novel user-oriented software package SMF that implements the new family of SM optimization algorithms
330 citations
TL;DR: The idea of implicit space mapping (ISM) is introduced and how it relates to the well-established (explicit) space mapping between coarse and fine device models is shown.
Abstract: We introduce the idea of implicit space mapping (ISM) and show how it relates to the well-established (explicit) space mapping between coarse and fine device models. Through comparison, a general space mapping concept is proposed. A simple algorithm based on the novel ISM concept is implemented. It is illustrated on a contrived "cheese-cutting problem" and is applied to electromagnetics-based microwave modeling and design. An auxiliary set of parameters (selected preassigned parameters) is extracted to match the coarse model with the fine model. The calibrated coarse model (the surrogate) is then (re)optimized to predict a better fine model solution. This is an easy space mapping technique to implement since the mapping itself is embedded in the calibrated coarse model and updated automatically in the procedure of parameter extraction. We illustrate our approach through optimization of a high-temperature superconducting filter using Agilent ADS with Momentum and Agilent ADS with Sonnet's em.
237 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 6 |
| 2024 | 11 |
| 2023 | 10 |
| 2022 | 30 |
| 2021 | 13 |
| 2020 | 12 |