An analytical surface potential based universal model for the drain current voltage characteristics of Symmetric Double gate (DG) junctionless field effect transistors is presented. This novel universal model is valid for all operating regions from depletion to inversion regions of operations. The primary conduction mechanism is governed by the bulk current where the channel becomes fully depleted in turning it off. This model has been validated by using TCAD device simulating software. The comparison shows high accuracy of the proposed model.
This paper presents an analytical model for ultra scaled symmetric double gate (SDG) nanowire junctionless field effect transistor (JLFET), which includes charge quantization in all the regions of operation. This model is based on a first-order correction for the confined energies obtained by solving the Schrodinger’s equation. The model is able to predict the quantum mechanical effects (QME) on the surface potential, drain current and transconductance for a highly doped and extremely thin silicon layer of thickness down to 4nm. The results obtained are validated by comparing with GENIUS 3D TCAD quantum simulations.
In this work we present a semi-analytical model for the current voltage and Capacitance-Voltage characteristics of nano scaled undoped symmetric double gate (DG) MOSFETs. This model uses a parabolic potential approximation for the body potential whose coordinate is normal or perpendicular to the interfaces in all regions of device operation. The carrier confinement phenomenon is considered and we calculate the surface electric field which is used to determine the inversion charge sheet density. The density is used in a compact classical model of the symmetric DG MOSFET as a core model. Quantum effects like the threshold voltage shift and increase in the effective oxide thickness are applied through some modifications to the core model. The results are verified by ATLAS device software. The comparison shows the high accuracy of the proposed model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.