An Analytical Model to Estimate FinFET’s VT Distribution Due to Fin-Edge Roughness.

Line-edge roughness induced fin-edge roughness (FER) is the primary source of VT 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 VT ( $V_{{T\rm {-sat}}}) variability due to FER. The model is capable of capturing the LG and W\mathrm{ fin} ) and variability parameters (correlation length $\Lambda $ and standard deviation $\Delta )$ accurately. The entire $V_{{T\rm {-sat}}}$ distribution obtained by the model is also presented and compared against the $V_{{T\rm {-sat}}} distribution of stochastic simulations to show that the model captures the distribution effectively. We show that not only \sigma VT but even \mu VT is affected by variability parameters. Hence, such modeling is critical to defining nominal FinFET structure ( LG and W\mathrm{ fin} ), which is affected by variability ( $\Lambda $ and $\Delta $ ) especially for scaled FinFETs, where quantum-confinement effects are enhanced. [ABSTRACT FROM PUBLISHER]