Multi-Corner Parametric Yield Estimation via Bayesian Inference on Bernoulli Distribution with Conjugate Prior
ISCAS(2020)
摘要
To efficiently estimate parametric yields over multiple process, voltage, temperature corners for binary output circuits, we propose a novel Bayesian Inference method based on Bernoulli distribution with conjugate prior in this paper. The key idea is to adopt a product of Beta distributions as the conjugate prior for the yields and encode circuit performance correlations among different corners into this prior. Next, the hyper-parameters are optimized by using multi-start Quasi-Newton method, and the yields over different corners are estimated via maximum-a-posteriori. Two circuit examples demonstrate that the proposed method achieves up to 3.0x cost reduction over the state-of-the-art methods without surrendering any accuracy.
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关键词
Conjugate prior,Bayesian Inference,Multi-corner yield estimation
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