Simulation of statistical variability in nano-CMOS transistors using drift-diffusion, Monte Carlo and non-equilibrium Green’s function techniques

Abstract

In this paper, we present models and tools developed and used by the Device Modelling Group at the University of Glasgow to study statistical variability introduced by the discreteness of charge and matter in contemporary and future Nano-CMOS transistors. The models and tools, based on Drift-Diffusion (DD), Monte Carlo (MC) and NonEquilibrium Green’s Function (NEGF) techniques, are encapsulated in the Glasgow 3D statistical ‘atomistic’ device simulator. The simulator can handle most of the known sources of statistical variability including Random Discrete Dopants (RDD), Line Edge Roughness (LER), Thickness Fluctuations in the Oxide (OTF) and Body (BTF), granularity of the Poly-Silicon (PSG), Metal Gate (MGG) and High-κ (HKG), and oxide trapped charges (OTC). The results of the statistical simulations are verified with respect to measurements carried out on fabricated devices. Predictions about the magnitude of the statistical variability in future generations of nano-CMOS devices are also presented.

Cite this paper

@inproceedings{Asenov2009SimulationOS, title={Simulation of statistical variability in nano-CMOS transistors using drift-diffusion, Monte Carlo and non-equilibrium Green’s function techniques}, author={A. Asenov and Andrew R. Brown and Gareth Roy and A. Martinez and Natalia Seoane and Scott Roy}, year={2009} }