Surrogate Modeling of RF Circuit Blocks (original) (raw)
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Rf circuit block modeling via kriging surrogates
2008
The use of replacement metamodels (global surrogate models) has become commonplace as a cost effective alternative for performing complex high fidelity computer simulations. Due to their compact formulation and negligible evaluation time, global surrogate models are very useful tools for exploring the design space, what-if analysis, optimization, and sensitivity analysis. In addition, multiple surrogate models can be chained together to easily model large scale systems where a direct, fullwave simulation would be too cumbersome. Two crucial aspects of global surrogate modeling are data collection (sequential design) and hyperparameter optimization. Kriging models have become very popular in many domains but have seen little application in electronics and Electro-Magnetism. In this paper we study the impact of different hyperparameter optimization algorithms and sampling strategies on the accuracy of Kriging models as applied to a Low Noise Amplifier (LNA) RF circuit block.
Forward and Reverse Modeling of Low Noise Amplifiers Based on Circuit Simulations
Lecture Notes in Electrical Engineering, 2011
Forward and reverse modeling of RF circuit blocks are useful approaches in design space exploration. The underlying idea of forward modeling is the creation of accurate surrogate models, which can be used to predict the circuit performances replacing (expensive) circuit simulations. On the other hand, reverse modeling concerns multiobjective optimization to explore relevant trade-offs between performances. This paper provides a discussion of application of surrogate models and multiobjective optimization to narrow-band low noise amplifier design. We discuss numerical difficulties encountered when the forward model is derived by using surrogate models of low noise amplifier admittances to compute performance figures via analytical equations. Afterward, we provide an example where direct performace modeling leads to a more accurate result even when the simplest surrogate model type (a lookup table) is used. Finally, a detailed tutorial of the normal boundary intersection optimization method is provided.
IEEE Access
Contemporary microwave design heavily relies on full-wave electromagnetic (EM) simulation tools. This is especially the case for miniaturized devices where EM cross-coupling effects cannot be adequately accounted for using equivalent network models. Unfortunately, EM analysis incurs considerable computational expenses, which becomes a bottleneck whenever multiple evaluations are required. Common simulation-based design tasks include parametric optimization and uncertainty quantification. These can be accelerated using fast replacement models, among which the data-driven surrogates are the most popular. Notwithstanding, a construction of approximation models for microwave components is hindered by the dimensionality issues as well as high nonlinearity of system characteristics. A partial alleviation of the mentioned difficulties can be achieved with the recently reported performance-driven modeling methods, including the nested kriging framework. Therein, the computational benefits are obtained by appropriate confinement of the surrogate model domain, spanned by a set of pre-optimized reference designs, and by focusing on the parameter space region that contains high quality designs with respect to the considered performance figures. This paper presents a methodology that incorporates the concept of nested kriging and enhances it by explicit dimensionality reduction based on spectral decomposition of the reference design set. Extensive verification studies conducted for a compact rat-race coupler and a three-section impedance matching transformer demonstrate superiority of the presented approach over both the conventional techniques and the nested kriging in terms of modeling accuracy. Design utility of our surrogates is corroborated through application cases studies. INDEX TERMS Microwave design, compact circuits, surrogate modeling, domain confinement, principal component analysis, dimensionality reduction.
2016
Abstract—Many complex, real world phenomena are difficult to study directly using controlled experiments. Instead, the use of computer simulations has become commonplace as a cost effective alternative. However, regardless of Moore’s law, performing high fidelity simulations still requires a great invest-ment of time and money. Surrogate modeling (metamodeling) has become indispensable as an alternative solution for relieving this burden. Many surrogate model types exist (Support Vector Machines, Kriging, RBF models, Neural Networks,...) but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. The same is true for setting the surrogate model parameters (Bias-Variance trade-off). Traditionally, the solution to both problems has been a pragmatic one, guided by intuition, prior experience or simply available software packages. In this paper we present a more founded approach to these problems. We describe an adaptive surrogate ...
Wideband modeling of RF/analog circuits via hierarchical multi-point model order reduction
Proceedings of the ASP-DAC 2005. Asia and South Pacific Design Automation Conference, 2005.
This paper proposes a novel wideband modeling technique for high-performance RF passives and linear(ized) analog circuits. The new method is based on a recently proposed sdomain hierarchical modeling and analysis method [27]. Theoretically, we show that the s-domain hierarchical reduction is equivalent to implicit moment matching around s = 0, and that the existing hierarchical reduction method by one-point expansion is numerically stable for general tree-structured circuits. Practically, we propose a hierarchical multi-point reduction scheme for high-fidelity, wideband modeling of general passive or active linear circuits. A novel explicit waveform matching algorithm is proposed for searching the dominant poles and residues from different expansion points based on the unique hierarchical reduction framework. Experimental results with large analog circuits, on-chip spiral inductors are presented to validate the proposed method.
Surrogate modeling of microwave circuits using polynomial functional interpolants
2010 IEEE MTT-S International Microwave Symposium, 2010
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.
Macromodeling for RF passives via circuit reduction of VPEC model
Digest of Papers. 2004 Topical Meeting onSilicon Monolithic Integrated Circuits in RF Systems, 2004.
To accelerate the macro-model generation for RF passive components considering parasitics, we introduce the Vector Potential Equivalent Circuit (VPEC) to efficiently model the massively coupled passives, like transmission lines and inductors. It enables the passive sparsification of the inductance matrix by pruning the small off-diagonal elements with the desired accuracy. To further reduce the model order, we apply a hierarchical s-domain circuit reduction to generate a reduced driving-point impedance function, and then use the Brune's one-port network synthesis to realize the impedance function by a low-order RLCM ladder circuit as the compact macro-model. We have applied this method to generate the macro-model for the spiral inductor during the design of a cross-coupled LC oscillator. The synthesized lower-order macro-model is accurate up to 10GHz with 50X speedup in the time-domain simulation.
MORCIC: Model Order Reduction Techniques for Electromagnetic Models of Integrated Circuits
arXiv (Cornell University), 2023
Model order reduction (MOR) is crucial for the design process of integrated circuits. Specifically, the vast amount of passive RLCk elements in electromagnetic models extracted from physical layouts exacerbates the extraction time, the storage requirements, and, most critically, the post-layout simulation time of the analyzed circuits. The MORCIC project aims to overcome this problem by proposing new MOR techniques that perform better than commercial tools. Experimental evaluation on several analog and mixed-signal circuits with millions of elements indicates that the proposed methods lead to ×5.5 smaller ROMs while maintaining similar accuracy compared to golden ROMs provided by ANSYS RaptorX™.
GLOBAL MODELING OF RF AND MICROWAVE CIRCUITS
2002
Advances in computer hardware and algorithmic technology have brought us to the brink of being able to model large mixed-signal circuits incorporating comprehensive modeling of the full physics of devices in a circuit model. In this paper we describe an approach to delivering a revolutionary modeling tool that implements new modeling and simulation abstractions, fast linear and nonlinear solvers, full-wave EM modeling for on-chip parasitics and integrated RF/microwave circuit design modeling, digital and analogue behavioral modeling, and advanced electrothermal modeling.