Experimental Validation of Process-Induced Variability Aware SPICE Simulation Platform for Sub-20 nm FinFET Technologies

Abstract

We propose an experimentally validated physics-based process-induced variability (PIV) aware SPICE simulation framework-enabling the estimation of performance variation due to line-edge-roughness (LER), metal-gate-granularity (MGG), random-dopant-fluctuation (RDF), and oxide-thickness-variation (OTV) at sub-20 nm technology node devices. The framework utilizes LER, RDF, OTV, and MGG defining parameters such as fin-edge correlation coefficient (rho), autocorrelation length (Lambda), grain-size (GS), sigma[EOT], etc. as the inputs, and produces I-d-V-g distribution of ensemble size 250 as an output. We have validated the framework against 14 nm FinFET experimental data for I-d-V-g trends as well as for the threshold-voltage (V-T), ON-current (I-ON), and subthreshold slope (SS) distributions for a range of device dimensions with a reasonably good match. The worst and the best case R square errors are 0.64 and 0.98, respectively, for the validation. The very nature of the proposed framework allows the designers to use it fora vast range of process technologies. Such models are of dual importance, as it enables a PIV aware prediction of circuit-level performance, and provides a platform to estimate PIV parameters efficiently, on-par with sophisticated structural characterization tools.