BMF-BD: Bayesian model fusion on bernoulli distribution for efficient yield estimation of integrated circuits

Published

Conference Paper

Accurate yield estimation is one of the important yet challenging tasks for both pre-silicon verification and post-silicon validation. In this paper, we propose a novel method of Bayesian model fusion on Bernoulli distribution (BMF-BD) for efficient yield estimation at the late stage by borrowing the prior knowledge from an early stage. BMF-BD is particularly developed to handle the cases where the pre-silicon simulation and/or post-silicon measurement results are binary: either 'pass' or 'fail'. The key idea is to model the binary simulation/measurement outcome as a Bernoulli distribution and then encode the prior knowledge as a Beta distribution based on the theory of conjugate prior. As such, the late-stage yield can be accurately estimated through Bayesian inference with very few late-stage samples. Several circuit examples demonstrate that BMF-BD achieves up to 10× cost reduction over the conventional estimator without surrendering any accuracy. Copyright 2014 ACM.

Full Text

Duke Authors

Cited Authors

  • Fang, C; Yang, F; Zeng, X; Li, X

Published Date

  • January 1, 2014

Published In

International Standard Serial Number (ISSN)

  • 0738-100X

International Standard Book Number 13 (ISBN-13)

  • 9781479930173

Digital Object Identifier (DOI)

  • 10.1145/2593069.2593099

Citation Source

  • Scopus