TY - JOUR
T1 - Chebyshev affine-arithmetic-based parametric yield prediction under limited descriptions of uncertainty
AU - Sun, Jin
AU - Li, Jun
AU - Ma, Dongsheng
AU - Wang, Janet M.
N1 - Funding Information:
Manuscript received August 30, 2007; revised February 5, 2008 and April 30, 2008. Current version published September 19, 2008. This work was supported by the National Science Foundation under Grant CCF-0447900. This paper was recommended by Associate Editor D. Sylvester. J. Sun, D. Ma, and J. M. Wang are with the Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ 85721 USA (e-mail: [email protected]; [email protected]; [email protected]). J. Li is with Anova Solutions, Inc., Santa Clara, CA 95054 USA (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TCAD.2008.2003300
PY - 2008/10
Y1 - 2008/10
N2 - Due to the hard-to-measure distributions of real process data, it is difficult to provide accurate parametric yield prediction for modern circuit design. Most existing approaches are not able to handle the uncertain distribution properties coming from the process data. Other approaches are inadequate in considering correlations among the distributions of variations. This paper suggests a new approach that not only takes care of correlations among distributions but also provides a low-cost and efficient computation scheme. The proposed method approximates the parameter variations with Chebyshev affine arithmetic (CAA) to capture both the uncertainty and nonlinearity in a cumulative distribution function. The CAA-based probabilistic range presentation describes, both fully and partially, specified process and environmental parameters. Thus, we are able to predict the probability bounds for leakage consumption with unknown dependences among variations. The end result is the chip-level parametric yield estimation based on leakage prediction. Experimental results demonstrate that the new approach provides a reliable bound estimation, which leads to a 20% yield improvement compared with only using the intervals of partially specified uncertainties.
AB - Due to the hard-to-measure distributions of real process data, it is difficult to provide accurate parametric yield prediction for modern circuit design. Most existing approaches are not able to handle the uncertain distribution properties coming from the process data. Other approaches are inadequate in considering correlations among the distributions of variations. This paper suggests a new approach that not only takes care of correlations among distributions but also provides a low-cost and efficient computation scheme. The proposed method approximates the parameter variations with Chebyshev affine arithmetic (CAA) to capture both the uncertainty and nonlinearity in a cumulative distribution function. The CAA-based probabilistic range presentation describes, both fully and partially, specified process and environmental parameters. Thus, we are able to predict the probability bounds for leakage consumption with unknown dependences among variations. The end result is the chip-level parametric yield estimation based on leakage prediction. Experimental results demonstrate that the new approach provides a reliable bound estimation, which leads to a 20% yield improvement compared with only using the intervals of partially specified uncertainties.
KW - Chebyshev affine arithmetic (CAA)
KW - Dependence bounds
KW - Limited descriptions of uncertainty
KW - Process variations
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U2 - 10.1109/TCAD.2008.2003300
DO - 10.1109/TCAD.2008.2003300
M3 - Article
AN - SCOPUS:52649128725
SN - 0278-0070
VL - 27
SP - 1852
EP - 1865
JO - IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
JF - IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IS - 10
M1 - 4627544
ER -