Analytical Separated Neuro-Space Mapping Modeling Method of Power Transistor

Wang, Xu; Li, Tingpeng; Yan, Shuxia; Wang, Jian

doi:10.3390/mi14020426

Cited by 1 publication

(1 citation statement)

References 24 publications

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“…As a consequence, physics-based models feature enhanced generalization capability [70]. The most popular modeling technique utilizing physics-based surrogates is arguably space mapping (SM) [71][72][73][74][75][76] along with its numerous variations such as aggressive [77], implicit [78], or frequency SM [79]. Still, the necessity to devise a problemdependent low-fidelity model significantly narrows down the application areas in which physics-based models may be utilized.…”

Section: Introductionmentioning

confidence: 99%

Cost-Efficient Two-Level Modeling of Microwave Passives Using Feature-Based Surrogates and Domain Confinement

Pietrenko-Dabrowska,

Koziel,

Zhang

2023

Electronics

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A variety of surrogate modeling techniques has been utilized in high-frequency design over the last two decades. Yet, the curse of dimensionality still poses a serious challenge in setting up reliable design-ready surrogates of modern microwave components. The difficulty of the modeling task is only aggravated by nonlinearity of circuit responses. Consequently, constructing a practically usable surrogate model, valid across extended ranges of material, geometry, and operational parameters, is far from easy. As a matter of fact, conventional modeling techniques are merely capable of building models for microwave structures featuring a relatively small number of designable parameters within reduced ranges thereof. One possible way of mitigating these obstacles may be the employment of the recently proposed two-stage performance-driven modeling approach. Therein, the surrogate model domain is narrowed down to the section of the space where the vectors of adequate quality are located, thereby permitting significantly reducing the cost of acquiring the training data. Seeking even further cost reduction, this work introduces a novel modeling framework, which exploits problem-specific knowledge extracted from the circuit responses to achieve substantial cost-savings of training data acquisition. In our methodology, the modeling procedure targets response features instead of the complete responses. The response features are the characteristic locations of the circuit response, such as relevant minima or maxima over selected frequency bands. The dependency of the coordinates of the said features on circuit dimensions is considerably less nonlinear than is observed for the complete characteristics, which enables sizable reduction of the data acquisition cost. Numerical validation of our procedure involving three microwave structures corroborates its remarkable efficiency, which allows for setting design-ready surrogates using only a handful of samples.

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