Interval-valued reduced order statistical interconnect modeling

被引:17
作者
Ma, JD [1 ]
Rutenbar, RA [1 ]
机构
[1] Carnegie Mellon Univ, Dept Elect & Comp Engn, Pittsburgh, PA 15213 USA
来源
ICCAD-2004: INTERNATIONAL CONFERENCE ON COMPUTER AIDED DESIGN, IEEE/ACM DIGEST OF TECHNICAL PAPERS | 2004年
关键词
D O I
10.1109/ICCAD.2004.1382621
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
We show how recent advances in the handling of correlated interval representations of range uncertainty can be used to predict the impact of statistical manufacturing variations on linear interconnect. We represent correlated statistical variations in RLC parameters as sets of correlated intervals, and show how classical model order reduction methods-AWE and PRIMA-can be re-targeted to compute interval-valued, rather than scalar-valued reductions. By applying a statistical interpretation and sampling to the resulting compact interval-valued model, we can efficiently estimate the impact of variations on the original circuit. Results show the technique can predict mean delay with errors between 5-10%, for correlated RLC parameter variations up to 35%
引用
收藏
页码:460 / 467
页数:8
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