Abstract—This paper presents a novel procedure for predicting integrated circuit parametric performance and yield when provided with sample transistor test results and a circuit schematic. Two enhancements to the existing Monte Carlo simulation procedures are described: 1) a multivariate nested model is used to reproduce random process-induced device variations, rather than the multivariate multinormal model typically used, and 2) the stochastic Monte Carlo method for mapping process variability into a performance distribution is replaced with a deterministic mapping technique. The use of multivariate nested distributions allows estimation not only of correlation between various model parameters, but also allows each of those variations to be apportioned among the various stages of the process (i.e., wafer to wafer, lot to lot, etc.). This allows matched devices to be more accurately simulated, without having to develop customized models for each configuration of matching, and provides focus for process improvement efforts into those areas with the maximum potential reward. The use of deterministic mapping provides simulation results which are repeatable and do not rely on chance to insure that the process parameter space has been evenly explored. A software package which implements the entire procedure has been written in C++. Index Terms—Monte Carlo simulation, multivariate statistics, parametric yield.

Conventional physical design flow separates the design of power network and signal network. Such a separated approach results in slow design convergence for wire-limited deep sub-micron designs. In this paper, we present a novel design methodology that simultaneously considers global signal routing and power network design under integrity constraints. The key part to this approach is a simple yet accurate power net estimation formula that decides the minimum number of power nets needed to satisfy both power and signal integrity constraints prior to detailed layout. The proposed design methodology is a one-pass solution to the co-design of power and signal networks in the sense that no iteration between them is required in order to meet design closure. Experiment results using large industrial benchmarks show that compared to the state-of-the-art alternative design approach, the proposed method can reduce the power network area by 19.4% on average under the same signal and power integrity constraints with better routing quality, but use less runtime.