Efficient analytical formulation and sensitivity analysis of neuro-space mapping for nonlinear microwave device modeling

Zhang, Lei; Xu, Jianjun; Yagoub, M.C.E.; Ding, Runtao; Zhang, Qi‐Jun

doi:10.1109/tmtt.2005.854190

Cited by 143 publications

(122 citation statements)

References 27 publications

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“…In this case, the derivative of at will coincide with the derivative of for all directions from , but it will use the Jacobian estimation of the fine model for all directions from . An appropriate formula is as follows: (15) where is given by (14), whereas is an orthogonal projection onto given by (16) with , , being the orthonormal basis of that can be obtained from , , using the Gram-Schmidt procedure. Equation (15) basically says that we trust the fine model Jacobian in directions where fine model data is available, however, the fine model Jacobian should match the surrogate Jacobian in directions of "no data."…”

Section: A Surrogate Model With Restricted Broyden Updatementioning

confidence: 99%

The state of the art of microwave CAD: EM-based optimization and modeling

Cheng

Koziel

Bakr

et al. 2010

Int J RF and Microwave Comp Aid Eng

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Abstract-Convergence is a well-known issue for standard space-mapping optimization algorithms. It is heavily dependent on the choice of coarse model, as well as the space-mapping transformations employed in the optimization process. One possible convergence safeguard is the trust region approach where a surrogate model is optimized in a restricted neighborhood of the current iteration point. In this paper, we demonstrate that although formal conditions for applying trust regions are not strictly satisfied for space-mapping surrogate models, the approach improves the overall performance of the space-mapping optimization process. Further improvement can be realized when approximate fine model Jacobian information is exploited in the construction of the space-mapping surrogate. A comprehensive numerical comparison between standard and trust-region-enhanced space mapping is provided using several examples of microwave design problems.Index Terms-Computer-aided design (CAD), electromagnetic (EM) optimization, microwave design, space mapping, trust-region methods.

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Section: A Surrogate Model With Restricted Broyden Updatementioning

confidence: 99%

The state of the art of microwave CAD: EM-based optimization and modeling

Cheng

Koziel

Bakr

et al. 2010

Int J RF and Microwave Comp Aid Eng

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“…In Neuro-SM, neural networks are used to automatically map and modify an existing equivalent circuit model also called coarse model to a desired/accurate model through a process named training. In order to fulfill the needs of the increased modeling complexity and the industry's increasing need for tighter accuracy, several improvements on the basis of [10] were subsequently studied to enhance the modeling accuracy and efficiency, such as Neuro-SM with the output mapping [13], dynamic Neuro-SM [14], and analytical Neuro-SM with sensitivity analysis [15]. Neuro-SM with the output mapping [13] was introduced, through incorporation of a new output/current mapping, for modeling of microwave devices.…”

Section: Introductionmentioning

confidence: 99%

A general neuro-space mapping technique for microwave device modeling

Zhu

Zhao

et al. 2018

J Wireless Com Network

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Accurate modeling of nonlinear microwave devices is critical for reliable design of microwave circuit and system. In this paper, a more general neuro-space mapping (Neuro-SM) method is proposed to fulfill the needs of the increased modeling complexity. The proposed technique retains the capability of the existing dynamic Neuro-SM in modifying the dynamic voltage relationship between the coarse model and the desired model. The proposed Neuro-SM also considers dynamic current mapping besides voltage mappings. In this way, the proposed Neuro-SM generalizes the previously published Neuro-SM methods and has the potential to produce a more accurate model of microwave devices with more dynamics and nonlinearity. A new formulation and new sensitivity analysis technique are derived to train the general Neuro-SM with dc, small-, and large-signal data. A new gradient-based training algorithm is also proposed to speed up the training. The validity and efficiency of the general Neuro-SM method are demonstrated through a real 2 × 50 μm GaAs pseudomorphic high-electron mobility transistor (pHEMT) modeling example. The proposed general Neuro-SM model can be implemented into circuit simulators conveniently.

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“…Compared to other conventional modeling techniques, such as numerical modeling method, which could be computationally expensive, or analytical method, which could be different to obtain for new devices, or empirical modeling solution, whose range and accuracy could be limited [8], the ANNs modeling technique is more efficient. In addition, under the condition of the urgent need of developing models for new device [19], ANNs modeling method could supply a way of fast model development.…”

Section: Introductionmentioning

confidence: 99%