An Accurate Process Induced Variability Aware Compact Model-based Circuit Performance Estimation for Design-Technology Co-optimization

Abstract: In sub-10nm FinFETs, Line-edge-roughness (LER) and metal-gate granularity (MGG) are the two most dominant sources of variability and are mostly modeled semi-empirically. In this work, compact models of LER and MGG are used. We show an accurate process-induced variability (PIV) aware compact model-based circuit performance estimation for Design-Technology Co-optimization (DTCO). This work is carried out using an experimentally validated BSIM-CMG model on a 7nm FinFET node. First, we have shown performance bench-marking of LER and MGG models with the state-of-the-art and shown {\textbackslash}4x({\textbackslash}2.3x) accuracy improvement for NMOS(PMOS) in the estimation of device figure of merits (DFoMs). Second, RO and SRAM circuits performance estimation is carried out for LER and MGG variability. Further, {\textbackslash}22\% more optimistic estimate of ({\sigma}/{\mu})\textsubscript{SHM} (Static Hold Margin) compared to the state-of-the-art model with V\textsubscript{DD} variation is shown. Finally, we demonstrate our improved DFoMs accuracy translated to more accurate circuits figure of merits (CFoMs) performance estimation. For worst-case SHM (3({\sigma}/{\mu})\textsubscript{SHM}@VDD=0.75 V) compared to state-of-the-art, dynamic(standby) power reduction by {\textbackslash}73\%({\textbackslash}61\%) is shown. Thus, our enhanced variability model accuracy enables more credible DTCO with significantly better performance estimates.