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Solely based on data measurements, artificial neural networks (ANNs) modeling methodologies are capable of capturing small and large signal behavior of the transistor, with good accuracy, thus becoming excellent alternatives to more strenuous modeling approaches, such as physical and semi-empirical.
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Figure 4 The current voltage behavior of the graphene/Si transistor.
The behavior of the proposed transistor over the threshold region is obtained by modulating the tunneling current through the SBs at the two ends of the channel [20].
The gate current, Ig, versus gate voltage, Vg, behavior of the graphene/Si transistor is shown in Figure 4a.
(a) Gate current, Ig, versus gate voltage, Vg, behavior of the graphene/Si transistor and (b) the source-drain current, Ids, versus the source-drain voltage, Vds, with different gate voltages.
A further modeling technique, called the charge control model, permits the analysis of the behavior of transistor circuits during large-signal switching and during transistor saturation.
He is most widely recognized for describing an aspect of the behavior of bipolar transistors, known as the Early Effect.
In addition to the latter method, the more accurate transconductance change method is herein applied to describe the memory behavior of the intrinsic and parasitic transistors in a more comprehensive way.
As well-known with decreasing channel length the desired long-channel behavior of a transistor is degraded by short-channel effects [10 12].
The results indicate that the bandgap and electron effective mass of GeNR increase firstly by increasing the strain strength but then decrease and this behavior affects on the transistor figures of merit.
Therefore, an acceptable performance comparable to the conventional behavior of a Schottky transistor is obtained.
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