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Fast Stochastic Analysis of Electromigration in Power Distribution Networks

机译:配电网中电迁移的快速随机分析

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A fast and stochastic analysis methodology for electromigration (EM) assessment of power distribution networks is presented in this paper. We examine the impact of variability on EM time-to-failure (TTF), considering altered current densities due to global/local process variations as well as the fundamental factors that cause the conventional EM TTF distribution. Through the novel variations-aware current density model based on Hermite polynomial chaos, we demonstrate significant margins in EM lifetime when compared with the traditional worst case approach. On the other hand, we show that the traditional approach is altogether incompetent in handling transistor-level local variations leading to significantly optimistic lifetime estimates for lower metal level interconnects of power delivery network. Subsequently, we attempt to bridge the conventional, component-level EM verification method to the system level failures, inspired by the extreme order statistics. We make use of asymptotic order models to determine the TTF for the th component failure due to EM, and demonstrate application of this approach in developing IR drop aware system-level failure criteria.
机译:本文提出了一种用于配电网络电迁移(EM)评估的快速,随机分析方法。我们研究了可变性对EM失效时间(TTF)的影响,考虑了由于全局/局部过程变化以及导致常规EM TTF分布的基本因素而导致的电流密度变化。通过基于Hermite多项式混沌的新颖的感知变化的电流密度模型,我们证明了与传统最坏情况方法相比,EM寿命显着提高。另一方面,我们表明,传统方法在处理晶体管级局部变化方面完全不胜任,从而导致电力传输网络的较低金属级互连的寿命估计显着乐观。随后,我们尝试在极端顺序统计的启发下,将常规的组件级EM验证方法桥接到系统级故障。我们利用渐进阶模型来确定由EM引起的第th个组件故障的TTF,并演示了该方法在开发IR下降感知系统级故障准则中的应用。

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