Efficient Rare Failure Analysis Over Multiple Corners via Correlated Bayesian Inference

Efficient Rare Failure Analysis Over Multiple Corners via Correlated Bayesian Inference
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通过相关贝叶斯推理对多个角进行高效的罕见故障分析

DOI:
10.1109/tcad.2019.2949524
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发表时间:
2020-10
期刊:
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (IEEE TCAD)
影响因子:
--
通讯作者:
Xin Li
Xin Li
中科院分区:
其他
文献类型:
--
作者:
Zhengqi Gao;Jun Tao;Yangfeng Su;Dian Zhou;Xuan Zeng;Xin Li

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在这篇文章中,我们提出了一个有效的相关贝叶斯推理(CBI)方法来估计多个工艺角的大规模电路系统的系统级故障率。其核心思想是将电路性能的相关性在不同的角落编码成几个精心定义的故障事件的先验分布。这些分布的超参数可以通过贝叶斯推理从一些模拟样本中学习,接下来,通过考虑这些先验分布,可以同时估计不同角落的系统级故障率。进一步发展了一种迭代约束推理方法,以保证所提出的方法的数值稳定性和合法化所有估计的故障率。数值实验表明,与现有算法相比,该方法在保证精度的前提下,可以减少约10\times $的运行时间.
In this article, we propose an efficient correlated Bayesian inference (CBI) method to estimate the system-level failure rates for large-scale circuit systems over multiple process corners. The key idea is to encode the correlations of circuit performances among the different corners into the prior distributions of several carefully defined failure events. The hyper-parameters of these distributions can be learned from a few simulation samples via Bayesian inference and, next, the system-level failure rates over different corners can be simultaneously estimated by taking into account these prior distributions. An iteratively constrained inference method is further developed to guarantee the numerical stability of the proposed method and legalize all estimated failure rates. The numerical experiments demonstrate that compared to the state-of-the-art algorithm, the proposed method can achieve around $10\times $ runtime reduction without surrendering any accuracy.
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