Process Variability-Aware Performance Modeling In 65 nm CMOS

Abstract

With the continued and successful scaling of CMOS, process, voltage, and temperature (PVT), variations are increasing with each technology generation. The process variability impacts all design goals like performance, power budget and reliability of circuits significantly, resulting in yield loss. Hence, variability needs to be modeled and cancelled out by design techniques during the design phase itself. This thesis addresses the variability issues in 65 nm CMOS, across the domains of process technology, device physics and circuit design, with an eventual goal of accurate modeling and prediction of propagation delay and power dissipation. We have designed and optimized 65 nm gate length NMOS/PMOS devices to meet the specifications of the International Technology Roadmap for Semiconductors (ITRS), by two dimensional process and device simulation based design. Current design sign-off practices, which rely on corner case analysis to model process variations, are pessimistic and are becoming impractical for nanoscale technologies. To avoid substantial overdesign, we have proposed a generalized statistical framework for variability-aware circuit design, for timing sign-off and power budget analysis, based on standard cell characterization, through mixed-mode simulations. Two input NAND gate has been used as a library element. Second order statistical hybrid models have been proposed to relate gate delay, static leakage power and dynamic power directly in terms of the underlying process parameters, using statistical techniques of Design Of Experiments - Response Surface Methodology (DOE-RSM) and Least Squares Method (LSM). To extend this methodology for a generic technology library and for computational efficiency, analytical models have been proposed to relate gate delays to the device saturation current, static leakage power to device drain/gate resistance characterization and dynamic power to device CV-characterization. The hybrid models are derived based on mixed-mode simulated data, for accuracy and the analytical device characterization, for computational efficiency. It has been demonstrated that hybrid models based statistical design results in robust and reliable circuit design. This methodology is scalable to a large library of cells for statistical static timing analysis (SSTA) and statistical circuit simulation at the gate level for estimating delay, leakage power and dynamic power, in the presence of process variations. This methodology is useful in bridging the gap between the Technology CAD and Design CAD, through standard cell library characterization for delay, static leakage power and dynamic power, in the face of ever decreasing timing windows and power budgets. Finally, we have explored the gate-to-source/drain overlap length as a device design parameter for a robust variability-aware device structure and demonstrated the presence of trade-off between performance and variability, both at the device level and circuit level.