Portable Space Mapping for Efficient Statistical Modeling of Passive Components

Zhang, Lei; Aaen, Peter H.; Wood, John

doi:10.1109/tmtt.2011.2182655

Cited by 28 publications

(12 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then we regard and as constant value and obtain by solving (19) Using (16), can be obtained from . Next, we perform SM from the space to the space by solving (5).…”

Section: Stage 2: Mapping From Space To Space and Adjustment Of Rimentioning

confidence: 99%

“…Recent progress has focused on several areas, such as a recent review points towards a cognition interpretation of SM [4], portable SM for efficient modeling [5], three-level output SM [6], tuning SM [7], shape-preserving response prediction [8], parallel SM [9] and zero-pole SM [10]. A software implementation of space mapping such as the SMF framework with applications such as antenna design have also been described in the literature [11].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Cognition-Driven Formulation of Space Mapping for Equal-Ripple Optimization of Microwave Filters

Zhang

Feng

Gongal-Reddy

et al. 2015

IEEE Trans. Microwave Theory Techn.

141

View full text Add to dashboard Cite

Space mapping is a recognized method for speeding up electromagnetic (EM) optimization. Existing space-mapping approaches belong to the class of surrogate-based optimization methods. This paper proposes a cognition-driven formulation of space mapping that does not require explicit surrogates. The proposed method is applied to EM-based filter optimization. The new technique utilizes two sets of intermediate feature space parameters, including feature frequency parameters and ripple height parameters. The design variables are mapped to the feature frequency parameters, which are further mapped to the ripple height parameters. By formulating the cognition-driven optimization directly in the feature space, our method increases optimization efficiency and the ability to avoid being trapped in local minima. The technique is suitable for design of filters with equal-ripple responses. It is illustrated by two microwave filter examples.Index Terms-Cognition-driven design, computer-aided design (CAD), electromagnetic (EM) optimization, microwave filters, modeling, space mapping (SM).

show abstract

“…Then we regard and as constant value and obtain by solving (19) Using (16), can be obtained from . Next, we perform SM from the space to the space by solving (5).…”

Section: Stage 2: Mapping From Space To Space and Adjustment Of Rimentioning

confidence: 99%

mentioning

confidence: 99%

Cognition-Driven Formulation of Space Mapping for Equal-Ripple Optimization of Microwave Filters

Zhang

Feng

Gongal-Reddy

et al. 2015

IEEE Trans. Microwave Theory Techn.

141

View full text Add to dashboard Cite

show abstract

“…The small-signal S parameter of the proposed analytical neuro-SM model can be obtained through transforming its Y matrices Y f , which are mapped from the Y matrices of the coarse model Y c , described as where the first-order derivatives of f ANN and h ANN can be obtained using the adjoint neural network method. 21,22 For the large-signal case, the analytical relationship between the proposed neuro-SM model and the coarse model under the large-signal HB environment is derived as…”

Section: Introductionmentioning

confidence: 99%

An advanced analytical neuro–space mapping technique with sensitivity analysis for transistor modeling

Zhu

Liu

et al. 2016

Int J Numerical Modelling

View full text Add to dashboard Cite

This paper presents an advanced analytical neuro–space mapping (neuro‐SM) technique for accurate and efficient modeling of transistor devices. This is an improvement over the existing neuro‐SM, which aims to use neural networks to map a given approximate device model towards an accurate model. The proposed neuro‐SM retains the ability of the existing neuro‐SM in modifying the voltage relationship between the given approximate device model and the accurate model. The proposed technique can also map the current relationship between the given model and the accurate model. In this way, the proposed neuro‐SM can produce improved accuracy over the existing neuro‐SM. In addition, analytical formulas of mapping and sensitivities of the direct current, small‐signal S parameter, and large‐signal harmonic of the proposed neuro‐SM model with respect to mapping parameters and coarse‐model parameters are also derived. The sensitivity analysis can be used with a gradient‐based training technique to improve the model training efficiency. The validity and efficiency of the proposed approach are verified through 2 transistor modeling examples and use of the proposed neuro‐SM models in a large‐signal behavior analysis of an amplifier.

show abstract

“…The space mapping concept combines the computational efficiency of coarse models with the accuracy of fine models [19]. [96], tuning space mapping [97], [98], portable space mapping [99], parallel space mapping [25], coarse and fine mesh space mapping [100], [101]. Recent improvements in space mapping such as constrained parameter extraction using implicit space mapping [102], space mapping optimization using EM-based adjoint sensitivity [103], and fast EM modeling using shape-preserving response prediction and space mapping [104] focus on reducing the number of fine model evaluations.…”

Section: Space Mappingmentioning

confidence: 99%

Parametric Modeling of EM Behaviors of Microwave Components Using Combined Neural Networks and Pole-Residue Transfer Functions

Feng¹

View full text Add to dashboard Cite

Parametric modeling of electromagnetic (EM) behaviors has become important for EM design optimizations of microwave components. The EM based design, such as design optimization, what if analysis and yield-driven design, can be time consuming because it usually requires repetitive EM simulations with varying values of geometrical parameters as design variables. Parametric models can be developed from the information of EM responses as functions of geometrical parameters. The developed parametric models allow faster simulations and optimizations with varying values of geometrical parameters and subsequently can be implemented in high-level circuit and system design optimizations. This thesis proposes a novel technique to develop combined neural network and pole-residue-based transfer function models for parametric modeling of EM behaviors of microwave components. In this technique, neural networks are trained to learn the relationships between pole/residues of the transfer functions and geometrical parameters. The orders of the pole-residue transfer functions may vary over different regions of geometrical parameters. We develop a pole-residue tracking technique to solve this order-changing problem. After the proposed modeling process, the trained model can be used to provide accurate and fast predictions of the EM behavior of microwave components with geometrical parameters as variables. An advanced pole-residue tracking technique is proposed to exploit sensitivity information to solve the challenges of pole-residue tracking especially when the amount of training data are reduced and/or the geometrical step sizes between the data samples are enlarged. The proposed technique takes advantages of sensitivity i information to split one pole into two separate new poles to achieve the increase of the orders of the transfer functions and ultimately form transfer functions of a constant order over the entire region of geometrical parameters. The proposed technique addresses the challenges of pole-residue tracking when training data are limited. As a further advancement, we introduce EM sensitivity analysis into the poleresidue-based neuro-transfer function modeling technique. The purpose is to increase the model accuracy by utilizing EM sensitivity information and to speedup the model development process by reducing the number of training data required for developing the model. The proposed parametric model consists of the original and adjoint pole-residue based neuro-TF models. New formulations are derived for calculating the second order derivatives for training the adjoint pole-residue based neuro-TF model. By exploiting the sensitivity information, the proposed technique can further speed up the model development process over the existing pole-residue parametric modeling method without using sensitivity analysis. The proposed parametric modeling techniques in this thesis are demonstrated by several microwave examples. KEY WORDS: Electromagnetic, parametric modeling, neural network, transfer function, sensitivity analys...

show abstract

Portable Space Mapping for Efficient Statistical Modeling of Passive Components

Cited by 28 publications

References 16 publications

Cognition-Driven Formulation of Space Mapping for Equal-Ripple Optimization of Microwave Filters

Cognition-Driven Formulation of Space Mapping for Equal-Ripple Optimization of Microwave Filters

An advanced analytical neuro–space mapping technique with sensitivity analysis for transistor modeling

Parametric Modeling of EM Behaviors of Microwave Components Using Combined Neural Networks and Pole-Residue Transfer Functions

Contact Info

Product

Resources

About