We present a novel Pelgrom-based predictive (PBP) model to estimate the impact of variability on the on-current of different state-of-the-art semiconductor devices. In this work, we focus on two of the most problematic sources of variability, the metal grain granularity (MGG) and the line edge roughness (LER). This model allows us to make an accurate prediction of the on-current standard deviation (σIon), being the relative error of the predicted data lower than 8% in 92% of the studied cases. The PBP model entails an immense reduction in the computational cost since once it is calibrated for an architecture, the prediction of the impact of a variability on devices with any given dimension can be made without any further simulations. This model could be useful for predicting the effect of variability on future technology nodes.
Keywords: FinFET, nanowire FET, nanosheet FET, Pelgrom, Monte Carlo (MC) simulations, Prediction model