Efficient Parametric Yield Estimation Over Multiple Process Corners via Bayesian Inference Based on Bernoulli Distribution
Efficient Parametric Yield Estimation Over Multiple Process Corners via Bayesian Inference Based on Bernoulli Distribution
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DOI：
10.1109/tcad.2019.2940682
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发表时间：
2020-10
影响因子：
2.9
通讯作者：
Zhengqi Gao;Jun Tao;Dian Zhou;Xuan Zeng
中科院分区：
文献类型：
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作者：
Zhengqi Gao;Jun Tao;Dian Zhou;Xuan Zeng
多个工艺角上的参数成品率估计在鲁棒电路设计中起着重要作用。本文提出了一种基于伯努利分布的贝叶斯推断方法（BI-BD）来有效地估计二进制输出电路的多通道成品率。其核心思想是将不同角点之间的电路性能相关性编码为我们的先验知识。因此，在结合一些模拟样本后，可以通过基于迭代加权最小二乘法（IRLS）和期望最大化（EM）的贝叶斯推断来校准所有角落的产量估计。一个电路的例子表明，建议的BI-BD方法可以实现高达2.0\times $成本降低比传统的蒙特卡罗方法，而不放弃任何精度。
Parametric yield estimation over multiple process corners plays an important role in robust circuit design. In this article, we propose a novel Bayesian inference method based on Bernoulli distribution (BI-BD) to efficiently estimate the multicorner yields for binary output circuit. The key idea is to encode the circuit performance correlation among different corners as our prior knowledge. Consequently, after combining a few simulation samples, the yield estimation over all corners can be calibrated via Bayesian inference based on iterative reweighted least squares (IRLS) and expectation maximization (EM). A circuit example demonstrates that the proposed BI-BD method can achieve up to $2.0\times $ cost reduction over the conventional Monte Carlo method without surrendering any accuracy.