Statistical Neuro-Space Mapping Technique for Large-Signal Modeling of Nonlinear Devices

Zhang, Lei; Zhang, Qi‐Jun; Wood, John

doi:10.1109/tmtt.2008.2004894

Cited by 51 publications

(42 citation statements)

References 31 publications

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“…The existing equivalent-circuit models, called coarse models, need to be modified and extended in order to accommodate for new device behavior. Manual modification of models is a trial-and-error process and hybrid methods have been developed to help map the coarse model to the device data [8], [11]- [14], [31], [33], [34].…”

Section: A Motivationmentioning

confidence: 99%

“…There are many possible implementations for , , , , , and , ranging from simple linear mappings to powerful ANN-based mappings [13], [14]. As discussed in Section II-A, the optimal choice will depend on the problem and available coarse model structures.…”

Section: Chromosomal Encoding Of the Model Search Spacementioning

confidence: 99%

“…Techniques combining ANNs and space mapping have been developed for electromagnetic (EM) modeling [2], nonlinear device modeling [13], and statistical device modeling [14]. In addition to the combination of space mapping with ANNs, implementations using linear regression models [15], neuro-fuzzy models [16], locally weighted models [17], and hybrid models [18] have also been described.…”

mentioning

confidence: 99%

“…For example, the input mapping may not be sufficient if the output of the fine model is beyond the output range of the coarse model. Fortunately, a variety of mapping methods have been developed in passive/EM modeling that can be adopted for nonlinear device modeling, such as space mapping [8], [11]- [14], difference mapping (or difference method) [5], output mapping [5], prior knowledge input (PKI) method [32],andknowledge-basedneuralnetworkinvolvinghybridmapping [5], [33].…”

mentioning

confidence: 99%

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Evolutionary Neuro-Space Mapping Technique for Modeling of Nonlinear Microwave Devices

Gorissen

Zhang

et al. 2011

IEEE Trans. Microwave Theory Techn.

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Abstract-This paper presents a new advance in Neuro-space mapping (Neuro-SM) techniques for modeling nonlinear microwave devices. Suppose that existing device models (namely, coarse models) cannot match the behavior of a new device (referred to as the fine model). By neural network mapping of the voltage and current signals from the coarse to the fine models, Neuro-SM can modify the behavior of the coarse model to match that of the fine model. However, the efficiency of mapping depends on both the mapping structure and the coarse model. In this paper, a structural optimization technique is presented to achieve optimal combinations of mapping structure and coarse model. An aggressive optimization formulation exploring detailed structural variations in both the mapping and the coarse model is proposed, where the internal branches of coarse models and separate mappings for the voltage and current at gate and drain are used as basic topology variables. The formulation of such a structural optimization by an evolutionary optimization algorithm is proposed. Numerical examples of metal-semiconductor field-effect transistor and high electron-mobility transistor modeling demonstrate that, by using the proposed algorithm, optimal combinations of space mapping and coarse model structures can be achieved leading to the best modeling accuracy with the simplest mapping function.Index Terms-Genetic algorithms, knowledge-based modeling, nonlinear device modeling, space mapping.

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Section: A Motivationmentioning

confidence: 99%

Section: Chromosomal Encoding Of the Model Search Spacementioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Evolutionary Neuro-Space Mapping Technique for Modeling of Nonlinear Microwave Devices

Gorissen

Zhang

et al. 2011

IEEE Trans. Microwave Theory Techn.

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“…Perhaps the most popular type of technique of this kind is space mapping (SM) [22][23][24][25], where the surrogate is constructed by means of suitable correction of an underlying lowfidelity (or so-called coarse) model. The bottleneck of space mapping in terms of antenna modeling is the lack of fast coarse models, because low-fidelity antenna representations are normally obtained through coarse-discretization EM simulations, the cost of which cannot be neglected.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven model based design and analysis of antenna structures

Ulaganathan

Koziel

Bekasiewicz

et al. 2016

IET Microwaves, Antennas & Propagation

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Data-driven models, or metamodels, offer an efficient way to mimic the behaviour of computation-intensive simulators. Subsequently, the usage of such computationally cheap metamodels is indispensable in the design of contemporary antenna structures where computationintensive simulations are often performed in a large scale. Although metamodels offer sufficient flexibility and speed, they often suffer from an exponential growth of required training samples as the dimensionality of the problem increases. In order to alleviate this issue, a Gaussian process based approach, known as Gradient Enhanced Kriging, is proposed in this work to achieve cost-efficient modeling of antenna structures. The Gradient Enhanced Kriging approach incorporates adjoint-based sensitivity data in addition to function data obtained from electromagnetic simulations. The approach is illustrated using a dielectric resonator and an ultra-wideband antenna structures. The method demonstrates significant accuracy improvement with less number of training samples over the Ordinary Kriging approach which utilises function data only. The discussed technique has been favorably compared with Ordinary Kriging in terms of computational cost.

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Neural Networks for Microwave Circuits

Feng,

Na,

Jin

et al. 2024

Encyclopedia of RF and Microwave Engineering

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Neural networks, also called artificial neural networks (ANNs), are important techniques for performing forward/inverse modeling for microwave active/passive components to enhance a circuit design. With measured or simulated data of microwave devices, ANNs can be trained to learn relevant microwave relationships, which could be otherwise computationally expensive or for which efficient analytical formulas are not available. By training an ANN using data from electromagnetic (EM)/physics simulations, one can use the trained ANN as models for microwave circuits to replace the EM/physics models, which are typically CPU intensive, to significantly accelerate circuit design with EM/physics‐level accuracies. Fundamental concepts of the ANN structure and training, knowledge‐based neural networks, automated model generation, neuro‐transfer function modeling, deep neural network modeling, neural network‐based inverse modeling, and the use for EM/multiphysics design optimizations are described here.

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Statistical Neuro-Space Mapping Technique for Large-Signal Modeling of Nonlinear Devices

Cited by 51 publications

References 31 publications

Evolutionary Neuro-Space Mapping Technique for Modeling of Nonlinear Microwave Devices

Evolutionary Neuro-Space Mapping Technique for Modeling of Nonlinear Microwave Devices

Data‐driven model based design and analysis of antenna structures

Neural Networks for Microwave Circuits

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