Circuit model for the GaAs m.e.s.f.e.t. valid to 12 GHz

Vendelin, G.D.; Omori, M.

doi:10.1049/el:19750046

Cited by 21 publications

(3 citation statements)

References 2 publications

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“…However, the k-factor that based on transistors' small-signal model (SSM) has not been discussed in these literatures. In the early 1975, Vendelin discussed the frequency of the peak k-factor in regard to GaAs MESFET using simple SSM [12], only a few of feedback parameters were used to represent the k-factor. In 1999, Gharpurey and Viswanathan had shown the formula of k-factor that is in terms of SSM parameters of BJT with common-emitter configuration [13].…”

Section: Solid-state Electronics 46 (2002) 451-458mentioning

confidence: 99%

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An investigation on RF CMOS stability related to bias and scaling

Wong

Chang

2002

Solid-State Electronics

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In recent times, CMOS devices have played an increasing important role in the area of RF-ICs to implement part of the high-frequency circuits. As a result, the high-frequency characteristics of CMOS devices, including stability, should be investigated in-depth. In the design of RF amplifier, the consideration to avoid oscillation in the circuits is a must. Basically, the instability involved in designing RF circuits can be reduced if the transistor is stable in the interesting frequency region. In this study, the stability factor of CMOS device is discussed. First, the stability factor (i.e. k-factor) is derived based on the microwave small-signal model of MOSFET. Therefore, small-signal parameters that affect stability can be carefully examined. In addition, the effects of biasing and dimensions on k-factor are also discussed. Since various operating point (or biasing) can cause device to have different effects on high frequency, the correlation between biasing and stability is discussed in this study. Due to the fact that CMOS device has continuously shrunk in size, a detailed discussion of the effect of MOSFETs dimension on stability is included. It is found from our experimental results that the smaller the gate length, the greater is the frequency range for CMOS device to become potentially unstable.

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Section: Solid-state Electronics 46 (2002) 451-458mentioning

confidence: 99%

“…5, parameters d and s with which they can strongly affect the stability as expressed in Eqs. (8a) and (8b) are the feedback related parameters in SSM [12,22,23]. It can be seen in Fig.…”

Section: Calculation Of New Stability-factor Modelmentioning

confidence: 99%

An investigation on RF CMOS stability related to bias and scaling

Wong

Chang

2002

Solid-State Electronics

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“…Two major solution categories have been proposed by researchers to solve the small-signal modeling problem of transistors. The first trend is based on the direct extraction of the small-signal circuit elements through analytic solutions [1][2][3][4]. This trend is very complicated because it depends on finding closed form expressions to relate the scattering parameters of the FET to the small-signal circuit elements.…”

Section: Introductionmentioning

confidence: 99%

Fet Small Signal Modelling Based on the DST and Mel Frequency Cepstral Coefficients

Elsharkawy¹,

El-Rabaie²,

Hindy³

et al. 2009

PIER B

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Abstract-In this paper, a new technique is proposed for field effect transistor (FET) small-signal modeling using neural networks. This technique is based on the combination of the Mel frequency cepstral coefficients (MFCCs) and discrete sine transform (DST) of the inputs to the neural networks. The input data sets to traditional neural systems for FET small-signal modeling are the scattering parameters and corresponding frequencies in a certain band, and the outputs are the circuit elements. In the proposed approach, these data sets are considered as forming random signals. The MFCCs of the random signals are used to generate a small number of features characterizing the signals. In addition, other MFCCs vectors are calculated from the DST of the random signals and appended to the MFCCs vectors calculated from the signals. The new feature vectors are used to train the neural networks. The objective of using these new vectors is to characterize the random input sequences with much more features to be robust against measurement errors. There are two benefits for this approach: a reduction in the number of neural networks inputs and hence a faster convergence of the neural training algorithm and robustness against measurement errors in the testing phase. Experimental results show that the proposed technique is less sensitive to measurement errors than using the actual measured scattering parameters.

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