Digital statistical analysis using VHDL

Manfred Dietrich, Uwe Eichler, J. Haase
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引用次数: 2

Abstract

Variations of process parameters have an important impact on reliability and yield in deep sub micron IC technologies. One methodology to estimate the influence of these effects on power and delay times at chip level is Monte Carlo Simulation, which can be very accurate but time consuming if applied to transistor-level models. We present an alternative approach, namely a statistical gate-level simulation flow, based on parameter sensitivities and a generated VHDL cell model. This solution provides a good speed/accuracy tradeoff by using the event-driven digital simulation domain together with an extended consideration of signal slope times directly in the cell model. The designer gets a fast and accurate overview about the statistical behavior of power consumption and timing of the circuit depending on the manufacturing variations. The paper shortly illustrates the general flow from cell characterization to the model structure and presents first simulation results.
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数字统计分析使用VHDL
在深亚微米集成电路技术中,工艺参数的变化对可靠性和良率有重要影响。估计这些影响对芯片级功率和延迟时间的影响的一种方法是蒙特卡罗模拟,如果应用于晶体管级模型,它可以非常准确,但耗时。我们提出了一种替代方法,即基于参数灵敏度和生成的VHDL细胞模型的统计门级仿真流。该解决方案通过使用事件驱动的数字仿真域以及直接在单元模型中扩展考虑信号斜率时间,提供了良好的速度/精度权衡。设计人员可以根据制造变化快速准确地了解电路功耗和时序的统计行为。本文简要说明了从细胞表征到模型结构的一般流程,并给出了第一个仿真结果。
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