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Bayesian Model Fusion: A statistical framework for efficient pre-silicon validation and post-silicon tuning of complex analog and mixed-signal circuits

Publication ,  Conference
Li, X; Wang, F; Sun, S; Gu, C
Published in: IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
December 1, 2013

In this paper, we describe a novel statistical framework, referred to as Bayesian Model Fusion (BMF), that allows us to minimize the simulation and/or measurement cost for both pre-silicon validation and post-silicon tuning of analog and mixed-signal (AMS) circuits with consideration of large-scale process variations. The BMF technique is motivated by the fact that today's AMS design cycle typically spans multiple stages (e.g., schematic design, layout design, first tape-out, second tape-out, etc.). Hence, we can reuse the simulation and/or measurement data collected at an early stage to facilitate efficient validation and tuning of AMS circuits with a minimal amount of data at the late stage. The efficacy of BMF is demonstrated by using several industrial circuit examples. © 2013 IEEE.

Duke Scholars

Published In

IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD

DOI

ISSN

1092-3152

Publication Date

December 1, 2013

Start / End Page

795 / 802
 

Citation

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Li, X., Wang, F., Sun, S., & Gu, C. (2013). Bayesian Model Fusion: A statistical framework for efficient pre-silicon validation and post-silicon tuning of complex analog and mixed-signal circuits. In IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD (pp. 795–802). https://doi.org/10.1109/ICCAD.2013.6691204
Li, X., F. Wang, S. Sun, and C. Gu. “Bayesian Model Fusion: A statistical framework for efficient pre-silicon validation and post-silicon tuning of complex analog and mixed-signal circuits.” In IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD, 795–802, 2013. https://doi.org/10.1109/ICCAD.2013.6691204.
Li X, Wang F, Sun S, Gu C. Bayesian Model Fusion: A statistical framework for efficient pre-silicon validation and post-silicon tuning of complex analog and mixed-signal circuits. In: IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD. 2013. p. 795–802.
Li, X., et al. “Bayesian Model Fusion: A statistical framework for efficient pre-silicon validation and post-silicon tuning of complex analog and mixed-signal circuits.” IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD, 2013, pp. 795–802. Scopus, doi:10.1109/ICCAD.2013.6691204.
Li X, Wang F, Sun S, Gu C. Bayesian Model Fusion: A statistical framework for efficient pre-silicon validation and post-silicon tuning of complex analog and mixed-signal circuits. IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD. 2013. p. 795–802.

Published In

IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD

DOI

ISSN

1092-3152

Publication Date

December 1, 2013

Start / End Page

795 / 802