Neuro-space mapping technique for semiconductor device modeling

Zhang, Lei; Zhang, Qi‐Jun

doi:10.1007/s11081-007-9024-0

Cited by 4 publications

(5 citation statements)

References 15 publications

(25 reference statements)

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“…Space mapping neural network [16,17,18,19] method as shown in fig.5 establishes a mathematical link between input space of fine and coarse models. Inputs of the coarse model are changed to satisfy more accurate results than coarse model and less computational burden than fine model via input mapping.…”

Section: Space Mapping Neural Network Methods (Smnn)mentioning

confidence: 99%

The recent developments in knowledge based neural modeling

Şimşek

Zhang

Kabir

et al. 2010

Procedia Computer Science

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Artificial neural networks have been recognized as an important technique in microwave modeling and optimization. This paper gives an overview and recent developments on the knowledge based neural modeling techniques in microwave modeling and design. The knowledge based artificial neural networks are constructed by incorporating the existing knowledge such as empirical formulas, equivalent circuit models and semi-analytical equations in neural network structures. The existing knowledge reduces the complexity of neural network model. This combination requires less training data and has better extrapolation performance than classical neural networks. The advantages of using knowledge based neural network modeling are demonstrated with two microwave modeling applications such as characteristic impedance modeling of thin-film microstrip line and parametric modeling of the differential via holes.

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Section: Space Mapping Neural Network Methods (Smnn)mentioning

confidence: 99%

The recent developments in knowledge based neural modeling

Şimşek

Zhang

Kabir

et al. 2010

Procedia Computer Science

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“…Artificial neural networks (ANNs) are a well‐established and very powerful mathematical tool, finding a variety of applications as a modeling tool in the field of RF and microwaves . One of the most attractive features of ANNs is their ability to learn and generalize from a set of training data, which is suitable to be exploited for building device models from the measured characteristics.…”

Section: Introductionmentioning

confidence: 99%

A review on the artificial neural network applications for small‐signal modeling of microwave FETs

Marinković

Crupi

Caddemi

et al. 2019

Int J Numerical Modelling

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The purpose of this paper is to provide a comprehensive overview of the fieldeffect transistor (FET) small-signal modeling using artificial neural networks (ANNs). To gain an in-depth insight into how to effectively develop an ANN model, we present a comparative study on the application of the ANNs for modeling the scattering (S-) parameters of a variety of FET technologies versus bias point, ambient temperature, and geometrical dimensions. As will be shown, the main challenge consists of identifying the most appropriate ANN model for the specific case under study. This is because the performance of an ANN-based model can vary significantly, depending especially on the choice of the model structure and the size and parameters of the chosen ANN. In addition, the choice of the model is related directly to the behavior of the FET characteristics, which might greatly depend on the selected device technology and operating conditions. The analysis of the present comparative study allows understanding how to properly construct ANN models to perform at their best for a successful FET modeling.

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“…(1) A new neuro-space mapping (Neuro-SM) technique [14]- [16] is presented to meet the constant need for new device models due to rapid progress in semiconductor technology. It aims to automatically modify the behavior of existing models to match new device behavior.…”

Section: Contributions Of the Thesismentioning

confidence: 99%

“…The statistical space mapping network utilizes the neuro-space mapping concept in [12], [14]- [16] by replacing the mapping neural network with a linear dynamic mapping shown in ,(^v 1> vf 1 >,...,^>,v 2 ,<,...,^>,^,...,i&>…”

Section: Statistical Space Mappingmentioning

confidence: 99%

“…We call this new technique "statistical neuro-space mapping". [16], and the output signals from the nominal model are further refined by an output mapping neural network to produce the final model outputs using the concept of prior knowledge input [11]. We name the input mapping and the output mapping as x-mapping and y-mapping, respectively, for convenience of description in the rest of the chapter.…”

Section: Proposed Statistical Neuro-sm Formulationmentioning

confidence: 99%

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Neuro-space mapping technique for microwave device modeling and its use in circuit simulation and statistical design

Zhang¹

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The author has granted a nonexclusive license allowing Library and Archives Canada to reproduce, publish, archive, preserve, conserve, communicate to the public by telecommunication or on the Internet, loan, distribute and sell theses worldwide, for commercial or noncommercial purposes, in microform, paper, electronic and/or any other formats. AVIS: L'auteur a accorde une licence non exclusive permettant a la Bibliotheque et Archives Canada de reproduire, publier, archiver, sauvegarder, conserver, transmettre au public par telecommunication ou par Plntemet, prefer, distribuer et vendre des theses partout dans le monde, a des fins commerciales ou autres, sur support microforme, papier, electronique et/ou autres formats. Canada Bien que ces formulaires aient inclus dans la pagination, il n'y aura aucun contenu manquant.

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Neuro-space mapping technique for semiconductor device modeling

Cited by 4 publications

References 15 publications

The recent developments in knowledge based neural modeling

The recent developments in knowledge based neural modeling

A review on the artificial neural network applications for small‐signal modeling of microwave FETs

Neuro-space mapping technique for microwave device modeling and its use in circuit simulation and statistical design

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