Probabilistic arithmetic. I. numerical methods for calculating convolutions and dependency bounds
International Journal of Approximate Reasoning
Full chip leakage estimation considering power supply and temperature variations
Proceedings of the 2003 international symposium on Low power electronics and design
Modeling and Estimation of Leakage in Sub-90nm Devices
VLSID '04 Proceedings of the 17th International Conference on VLSI Design
Parametric yield estimation considering leakage variability
Proceedings of the 41st annual Design Automation Conference
Interval-valued reduced order statistical interconnect modeling
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Robust estimation of parametric yield under limited descriptions of uncertainty
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
| Hi-index | 0.01 |
In modern circuit design, it is difficult to provide reliable parametric yield prediction since the real distribution of process data is hard to measure. Most existing approaches are not able to handle the uncertain distribution property coming from the process data. Other approaches are inadequate considering correlations among the parameters. This paper suggests a new approach that not only takes care of the correlations among distributions but also provides a low cost and efficient computation scheme. The proposed method approximates the parameter variations with Chebyshev Affine Arithmetics (CAA) to capture both the uncertainty and the nonlinearity in Cumulative Distribution Functions (CDF). The CAA based probabilistic presentation describes both fully and partially specified process and environmental parameters. Thus we are capable of predicting probability bounds for leakage consumption under unknown dependency assumption among variations. The end result is the chip level parametric yield estimation based on leakage prediction. The experimental results demonstrate that the new approach provides reliable bound estimation while leads to 20% yield improvement comparing with interval analysis.