Noise analysis has become a critical concern in advanced chip designs. Traditional methods suffer from two common issues. First, noise that is propagated through the driver of a net is combined with noise injected by capacitively coupled aggressor nets using linear summation. Since this ignores the non-linear behavior of the driver gate the noise that develops on a net can be significantly underestimated. We therefore propose a new linear model that accurately combines propagated and injected noise on a net and which maintains the efficiency of linear simulation. After the propagated and injected noise are correctly combined on a victim net, it is necessary to determine.if the noise can result in a functional failure. This is the second issue that we discuss in this paper. Traditionally, noise failure criteria have been based on unity gain points of the DC or AC transfer curves. However, we will show that for digital designs, these approaches can result in a pessimistic analysis in some cases. while in other cases, they allow circuit operation that is extremely close to regions that are unstable and do not allow sufficient margin for error in the analysis. In this paper, we compare the effectiveness of the discussed noise failure criteria and also present a propagation based method, which is intended to overcome these drawbacks. The proposed methods were implemented in a noise analysis tool and we demonstrate results on indusuial circuits.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.