Efficient Aging-Aware SRAM Failure Probability Calculation via Particle Filter-Based Importance Sampling
Efficient Aging-Aware SRAM Failure Probability Calculation via Particle Filter-Based Importance Sampling
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DOI：
10.1587/transfun.e99.a.1390
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发表时间：
2016-07
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影响因子：
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通讯作者：
H. Awano;Masayuki Hiromoto;Takashi Sato
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文献类型：
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作者：
H. Awano;Masayuki Hiromoto;Takashi Sato
提出了一种高效的蒙特卡罗（MC）方法来计算SRAM电池由于负偏置温度不稳定性（NBTI）而导致的失效概率退化。在该方法中，粒子滤波器被用于增量跟踪SRAM单元的时间性能变化。通过重复使用最后一个时间步长的最终粒子分布作为初始分布，大大减少了获得稳定粒子分布所需的模拟次数。结合二值分类器的使用，可以快速判断MC样本是否导致单元故障，从而大大减少了捕获故障概率随时间变化的模拟总数。该方法比目前最先进的方法实现了13:4 （cid:2）的加速。
SUMMARY An e ffi cient Monte Carlo (MC) method for the calculation of failure probability degradation of an SRAM cell due to negative bias temperature instability (NBTI) is proposed. In the proposed method, a par- ticle filter is utilized to incrementally track temporal performance changes in an SRAM cell. The number of simulations required to obtain stable par- ticle distribution is greatly reduced, by reusing the final distribution of the particles in the last time step as the initial distribution. Combining with the use of a binary classifier, with which an MC sample is quickly judged whether it causes a malfunction of the cell or not, the total number of simu- lations to capture the temporal change of failure probability is significantly reduced. The proposed method achieves 13 : 4 (cid:2) speed-up over the state-of- the-art method.