A quasi‐two‐dimensional analytical model for GaAs MESFETs is proposed. It enables the calculation of the dc, the small‐signal, and the noise behaviour of GaAs MESFETs and takes…
Abstract
A quasi‐two‐dimensional analytical model for GaAs MESFETs is proposed. It enables the calculation of the dc, the small‐signal, and the noise behaviour of GaAs MESFETs and takes into account both doping and low‐field mobility profiles in the active layer of the transistor. It is shown that the profile of the low‐field mobility near the bottom of the active layer has a considerable influence on the minimum noise figure.
Frank Schwierz, Valentin Nakov and Matthias Roßberg
An simple model for the simulation of the electrical behaviour of several types of junction controlled field‐effect transistors is proposed. It is based on the calculation of the…
Abstract
An simple model for the simulation of the electrical behaviour of several types of junction controlled field‐effect transistors is proposed. It is based on the calculation of the carrier concentration in the channel by means of a self‐consistent solution of Schrödinger and Poisson's equation in the direction perpendicular to the current flow. Based on the carrier concentration the dc, the small‐signal, and also the noise properties of the devices may be simulated. The calculated characteristics of a sub‐quarter micron gate GaAs MESFET, a δ‐doped GaAs FET and a Velocity Modulation Transistor will be presented and discussed.
Theodoros Zygiridis, Stamatis A. Amanatiadis, Theodosios Karamanos and Nikolaos V. Kantartzis
The extraordinary properties of graphene render it ideal for diverse contemporary and future applications. Aiming at the investigation of certain aspects commonly overlooked in…
Abstract
Purpose
The extraordinary properties of graphene render it ideal for diverse contemporary and future applications. Aiming at the investigation of certain aspects commonly overlooked in pertinent works, the authors study wave-propagation phenomena supported by graphene layers within a stochastic framework, i.e. when uncertainty in various factors affects the graphene’s surface conductivity. Given that the consideration of an increasing number of graphene sheets may increase the stochastic dimensionality of the corresponding problem, efficient surrogates with reasonable computational cost need to be developed.
Design/methodology/approach
The authors exploit the potential of generalized Polynomial Chaos (PC) expansions and develop low-cost surrogates that enable the efficient extraction of the necessary statistical properties displayed by stochastic graphene-related quantities of interest (QoI). A key step is the incorporation of an initial variance estimation, which unveils the significance of each input parameter and facilitates the selection of the most appropriate basis functions, by favoring anisotropic formulae. In addition, the impact of controlling the allowable input interactions in the expansion terms is investigated, aiming at further PC-basis elimination.
Findings
The proposed stochastic methodology is assessed via comparisons with reference Monte-Carlo results, and the developed reduced basis models are shown to be sufficiently reliable, being at the same time computationally cheaper than standard PC expansions. In this context, different graphene configurations with varying numbers of random inputs are modeled, and interesting conclusions are drawn regarding their stochastic responses.
Originality/value
The statistical properties of surface-plasmon polaritons and other QoIs are predicted reliably in diverse graphene configurations, when the surface conductivity displays non-trivial uncertainty levels. The suggested PC methodology features simple implementation and low complexity, yet its performance is not compromised, compared to other standard approaches, and it is shown to be capable of delivering valid results.