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Due to the progress of nanometer process technologies, variability of circuit parameters is increasing and the statistical static timing analysis (S-STA) has been studied intensively. The existing block-based S-STA algorithms assume that the distribution of delay of each circuit element does not change through the analysis. However, such a delay depends on the input-slew, and the slew to be considered...
Statistical static timing analysis (SSTA) is becoming complicated due to introduction of more and more advanced statistical techniques. In this paper, with the help of conditional moments, we propose a simple path-based timing approach, which permits us to consider gate topology and switching process induced correlations. Numerical results are presented to quantify the relative impact of these two...
We present a generic method for analyzing the effect of process variability in nanoscale circuits. The proposed framework uses kernel and a generic tail probability estimator to eliminate the need for a-priori density choice for the nature of circuit variation. This allows capturing the true nature of the circuit variation from a few random samples of its observed responses. The data-driven, non-parametric,...
The dramatic increase in leakage current, coupled with the swell in process variability in nano-scaled CMOS technologies, has become a major issue for future IC design. Moreover, due to the spread of leakage power values, leakage variability cannot be neglected anymore. In this work an accurate analytic estimation and modeling methodology has been developed for logic gates leakage under statistical...
In aortic valve stenosis (AS), heart murmurs arise as an effect of turbulent blood flow distal to the obstructed valves. With increasing AS severity, the flow becomes more unstable, and the ensuing murmur becomes more complex. We hypothesize that these hemodynamic flow changes can be quantified based on the complexity of the phonocardiographic (PCG) signal. In this study, sample entropy (SampEn) was...
We have developed a new statistical timing analysis approach that does not impose any assumptions on the nature of manufacturing variability and takes into account an arbitrary model of spatial correlation as well as all types of functional correlations (e.g. reconvergence-based correlations). The starting point for statistical timing analysis is small scale Monte Carlo (MC) simulation. In order to...
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