Design of Dispersive Delay Structures (DDSs) Formed by Coupled C-Sections Using Predistortion with Space Mapping

The concept of space mapping is applied, for the first time, to the design of microwave dispersive delay structures (DDSs). DDSs are components providing specified group delay versus frequency responses for real-time radio systems. The DDSs considered in this paper are formed by cascaded coupled C-sections. It is first shown that aggressive space mapping does not provide sufficient accuracy in the synthesis of DDSs. To address this issue, we propose a predistortion space mapping technique. Compared to aggressive space mapping, this technique provides enhanced accuracy, while compared to output space mapping, it provides greater implementation simplicity. Two full-wave and one experimental examples are provided to illustrate the proposed predistortion space mapping technique

Basic Space Mapping: A Retrospective and its Application to Design Optimization of Nonlinear RF and Microwave Circuits

Space mapping (SM) is one of the most powerful and computationally efficient design optimization methodologies in RF and microwave engineering. Its impressive evolution in terms of algorithmic variations and diverse engineering applications is well-documented. Most of the SM-based design optimization cases, including those solved by advanced and sophisticated space mapping formulations, have been demonstrated for linear frequency-domain microwave circuits. In this paper, we provide a brief retrospective on the emergence of space mapping, including its initial impression on a worldwide authority on nonlinear microwave circuit simulation and design: Prof. Vittorio Rizzoli. We briefly review some of the most fundamental space mapping optimization concepts and emphasize their applicability to nonlinear transient-domain microwave circuit design optimization. We illustrate this by a typical problem of high-speed digital signal conditioning: the physical design of a set of CMOS inverters driving an FR4 printed circuit board interconnect.ITESO, A.C

System-Level Measurement-Based Design Optimization by Space Mapping Technology

Space mapping arose from the need to implement fast and accurate design optimization of microwave structures using full-wave EM simulators. Space mapping optimization later proved effective in disciplines well beyond RF and microwave engineering. The underlying coarse and fine models of the optimized structures have been implemented using a variety of EDA tools. More recently, measurement-based physical platforms have also been employed as “fine models.” Most space-mapping-based optimization cases have been demonstrated at the device-, component-, or circuit-level. However, the application of space mapping to high-fidelity system-level design optimization is just emerging. Optimizing highly accurate systems based on physical measurements is particularly challenging, since they are typically subject to statistical fluctuations and varying operating or environmental conditions. Here, we illustrate emerging demonstrations of space mapping system-level measurement-based design optimization in the area of signal integrity for high-speed computer platforms. Other measurement-based space mapping cases are also considered. Unresolved challenges are highlighted and potential general solutions are ventured.ITESO, A.C

An Early History of Optimization Technology for Automated Design of Microwave Circuits

This paper outlines the early history of optimization technology for the design of microwave circuits—a personal journey filled with aspirations, academic contributions, and commercial innovations. Microwave engineers have evolved from being consumers of mathematical optimization algorithms to originators of exciting concepts and technologies that have spread far beyond the boundaries of microwaves. From the early days of simple direct search algorithms based on heuristic methods through gradient-based electromagnetic optimization to space mapping technology we arrive at today’s surrogate methodologies. Our path finally connects to today’s multi-physics, system-level, and measurement-based optimization challenges exploiting confined and feature-based surrogates, cognition-driven space mapping, Bayesian approaches, and more. Our story recognizes visionaries such as William J. Getsinger of the 1960s and Robert Pucel of the 1980s, and highlights a seminal decades-long collaboration with mathematician Kaj Madsen. We address not only academic contributions that provide proof of concept, but also indicate early formative milestones in the development of commercially competitive software specifically featuring optimization technology.ITESO, A.C

Advanced RF and Microwave Design Optimization: A Journey and a Vision of Future Trends

In this paper, we outline the historical evolution of RF and microwave design optimization and envisage imminent and future challenges that will be addressed by the next generation of optimization developments. Our journey starts in the 1960s, with the emergence of formal numerical optimization algorithms for circuit design. In our fast historical analysis, we emphasize the last two decades of documented microwave design optimization problems and solutions. From that retrospective, we identify a number of prominent scientific and engineering challenges: 1) the reliable and computationally efficient optimization of highly accurate system-level complex models subject to statistical uncertainty and varying operating or environmental conditions; 2) the computationally-efficient EM-driven multi-objective design optimization in high-dimensional design spaces including categorical, conditional, or combinatorial variables; and 3) the manufacturability assessment, statistical design, and yield optimization of high-frequency structures based on high-fidelity multi-physical representations. To address these major challenges, we venture into the development of sophisticated optimization approaches, exploiting confined and dimensionally reduced surrogate vehicles, automated feature-engineering-based optimization, and formal cognition-driven space mapping approaches, assisted by Bayesian and machine learning techniques.ITESO, A.C

Neural modeling and space mapping: two approaches to circuit design

The drive in the microwave industry for manufacturability-driven design and time-to-market demands powerful and efficient computer-aided design tools. The need for statistical analysis and yield optimization coupled with the desire to use accurate physics-based and EM-based models leads to tasks that are computationally intensive using conventional approaches. We present two recent advances in the microwave CAD area, Artificial Neural Network (ANN) based modeling and Space Mapping (SM) based modeling for fast and accurate design of microwave components and circuits.Consejo Nacional de Ciencia y TecnologíaCarleton Universit

Microwave device modeling exploiting generalized space mapping

We present a comprehensive framework to engineering device modeling which we call Generalized Space Mapping (GSM). GSM significantly enhances the accuracy of available empirical models of microwave devices by utilizing a few relevant full-wave EM simulations. Our approach has been verified on several modeling problems. We present a microstrip shaped T-junction example.Bandler CorporationConsejo Nacional de Ciencia y TecnologíaNatural Sciences and Engineering Research Council of Canad

Software implementation of space mapping based neuromodels of microwave components

We present novel realizations of SM based neuromodels of microwave components using available software. In the SM based neuromodeling techniques a neural network is used to implement a mapping from the electromagnetic to the circuit-theoretic input space. The implicit knowledge in the circuit model allows us to decrease not only the number of learning points needed, but also the complexity of the neural network and to improve the generalization performance. A Frequency Space Mapped Neuromodel (FSMN) of a microstrip right angle bend is implemented using NeuroModeler, and entered into ADS as a library component through an ADS plug-in module.Consejo Nacional de Ciencia y TecnologíaCarleton Universit