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In this paper we study different modeling strategies for reparable systems, that have more than one aging parameters. We propose four approaches that use Monte-Carlo simulations, proportional hazards models, multi-variate Weibull along with numerical optimization methods and maximum likelihood estimation. The objective of this study is to investigate modeling strategy that predicts system's reliability...
In a nanoscale technology, memory bits are highly susceptible to process variation induced read/write failures. To address the above problem, in this paper a new memory cell is proposed which is highly stable against nanoscale process variations as well as power efficient. The effectiveness of the proposed cell is exhaustively evaluated through detailed Monte Carlo simulations. It is observed that...
We present an efficient optimization scheme for gate sizing in the presence of process variations. Our method is a worst case design scheme; however, it reduces the pessimism involved in traditional worst case methods by incorporating the effect of spatial correlations in the optimization procedure. The pessimism reduction is achieved by employing a bounded model for the parameter variations in the...
We propose a scalable and efficient parameterized block-based statistical static timing analysis algorithm incorporating both Gaussian and non-Gaussian parameter distributions, capturing spatial correlations using a grid-based model. As a preprocessing step, we employ independent component analysis to transform the set of correlated non-Gaussian parameters to a basis set of parameters that are statistically...
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