Please use this identifier to cite or link to this item:
|Title:||Statistical noise margin estimation for sub-threshold combinational circuits||Authors:||Pu, Y.
De Gyvez, J.P.
|Issue Date:||2008||Citation:||Pu, Y.,De Gyvez, J.P.,Corporaal, H.,Ha, Y. (2008). Statistical noise margin estimation for sub-threshold combinational circuits. Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC : 176-179. ScholarBank@NUS Repository. https://doi.org/10.1109/ASPDAC.2008.4483935||Abstract:||The increasingly popular sub-threshold design is strongly calling for EDA support to estimate noise margins, minimum functional supply voltage, as well as the functional yield. In this paper, we propose a fast, accurate and statistical approach to accomplish these goals. First, we derive close-form functions based on a new equivalent resistance model which enables the fast estimation of noise margins of individual cells at the gate-level. Second, we propose to calculate and propagate the noise margin information with an affine arithmetic model that takes into account process variations and correspondent inter-cell correlations. Experiments with ISCAS benchmarks have shown that the new approach has an accuracy of 98.5% w.r.t. transistor-level Monte Carlo simulations. The running time per input vector of the new approach only needs a few seconds, in contrast to the many hours required by transistor-level DC Monte-Carlo simulations. To the best of our knowledge, we are the first to provide a fast, accurate and statistical methodology other than Monte-Carlo simulation for the noise margin estimation of sub-threshold combinational circuits. ©2008 IEEE.||Source Title:||Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC||URI:||http://scholarbank.nus.edu.sg/handle/10635/71864||ISBN:||9781424419227||DOI:||10.1109/ASPDAC.2008.4483935|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Apr 20, 2019
checked on Apr 21, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.