A linear statistical FET model using principal component analysis

Purviance, J.; Petzold, M.C.; Potratz, C.

doi:10.1109/22.32222

Cited by 28 publications

(11 citation statements)

References 4 publications

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“…Experimental results in statistical modelling of GaAs MESFET's [94], [95], [ 141, [6] seem to confirm the above considerations. In fact, complex and strongly correlated distributions have been found both for S-matrices and for the electrical parameters of statistically characterized equivalent circuits; thus, large experimental data bases seem to be necessary for realistic Monte Carlo simulation [94], [95], [5] when using statistical behavioural modelling.…”

Section: Computer-aided Mmic Design Using Physics-based Device Mosupporting

confidence: 72%

“…In fact, complex and strongly correlated distributions have been found both for S-matrices and for the electrical parameters of statistically characterized equivalent circuits; thus, large experimental data bases seem to be necessary for realistic Monte Carlo simulation [94], [95], [5] when using statistical behavioural modelling. On the other hand, PBDMs seem to provide realistic statistical predictions on the basis alone of a limited set of moments characterizing the simpler statistics of the physical parameters.…”

Section: Computer-aided Mmic Design Using Physics-based Device Momentioning

confidence: 99%

See 1 more Smart Citation

Physics-based electron device modelling and computer-aided MMIC design

Filicori

Ghione

Naldi

1992

IEEE Trans. Microwave Theory Techn.

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Abstract-The paper provides an overview on the state of the art and future trends in physics-based electron device modelling for the computer-aided design of monolithic microwave IC's. After a review of the main physics-based approaches to microwave modelling, special emphasis is placed on innovative developments relevant to circuit-oriented device performance assessment, such as efficient physics-based noise and parametric sensitivity analysis. The use of state-of-the-art physicsbased analytical or numerical models for circuit analysis is discussed, with particular attention to the role of intermediate behavioural models in linking multidimensional device simulators with circuit analysis tools. Finally, the model requirements for yield-driven MMIC design are discussed, with the aim of pointing out the advantages of physics-based statistical device modelling; the possible use of computationally efficient approaches based on device sensitivity analysis for yield optimization is also considered.

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Section: Computer-aided Mmic Design Using Physics-based Device Mosupporting

confidence: 72%

Section: Computer-aided Mmic Design Using Physics-based Device Momentioning

confidence: 99%

Physics-based electron device modelling and computer-aided MMIC design

Filicori

Ghione

Naldi

1992

IEEE Trans. Microwave Theory Techn.

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“…Purviance et a1 [1,2] To test this we fit ECPs using SPECIAL'S intrinsic and extrinsic FET models and MicroCAT, then used the extracted ECPs to compute S-parameters at 6 GHz. Figure 4 shows that all three extractors predict not only the average S-parameter well, but also the standard deviations in the distributions.…”

Section: Model Dependencementioning

confidence: 99%

“…Purviance et a1 [1,2] argued that the standard equivalent-circuit (EC) models may fail to predict the S-parameter correlations and distributions well, and have proposed a new approach, called principal component analysis. Given the historical great effort that has been devoted to EC methods, we are reluctant to adopt an approach that abandons the EC method.…”

Section: Introductionmentioning

confidence: 99%

Statistical analysis of GaAs MESFET S‐parameter equivalent‐circuit models

Anholt¹,

Worley²,

Neidhard³

1991

Int. J. Microw. Mill.‐Wave Comput.‐Aided Eng.

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We compare the ability of three different equivalent-circuit extraction methods to give ensembles of model parameters that accurately predict not only average S-parameters but the S-parameter statistics, i.e., the standard deviations and intercorrelations between the real and imaginary parts. Measurements were made for 400 GaAs MESFETs fabricated on a single wafer with an MBE-grown active layer. Data is compared for different biases. We find that bimodal distributions give correlations that the equivalent-circuit models fail to model. The possibility of using uncorrelated equivalent-circuit values is also discussed.

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“…In [2][3][4], statistical models (recreate mean, standard deviation, and correlation of the equivalent-circuit model parameters) are built for FETs using principlecomponent analysis. In contrast to these related papers, we propose to use sensitivity analysis with an artificial neural network (ANN) as the statistical model.…”

Section: Introductionmentioning

confidence: 99%