An Analytical Model to Estimate FinFET’s $V_{T}$ Distribution Due to Fin-Edge Roughness

Line-edge roughness induced fin-edge roughness (FER) is the primary source of V
_T
variation in FinFETs. Conventionally, stochastic simulations are performed to predict the device variability due to FER for a technology, which are computationally expensive. An analytical formulation to predict variability due to FER enables understanding of the effect of input parameters as well as provides quantitative results at fractional computational costs. In this paper, we develop and present an analytical model to estimate saturation V
_T
(V
_T
-sat) variability due to FER. The model is capable of capturing the V
_T
variability dependence on device parameters (L
_G
and W
_fin
) and variability parameters (correlation length Λ and standard deviation Δ) accurately. The entire VT-sat distribution obtained by the model is also presented and compared against the VT-sat distribution of stochastic simulations to show that the model captures the distribution effectively. We show that not only σ V
_T
but even μV
_T
is affected by variability parameters. Hence, such modeling is critical to defining nominal FinFET structure (LG and Wfin), which is affected by variability (Λ and Δ) especially for scaled FinFETs, where quantum-confinement effects are enhanced.