Circuit statistical modeling for partially correlated model...

Data processing: structural design – modeling – simulation – and em – Modeling by mathematical expression

Reexamination Certificate

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C703S014000

Reexamination Certificate

active

07640143

ABSTRACT:
A method, system and program product are disclosed for statistical modeling an integrated circuit that provides information about partial correlations between model parameters. The invention determines a variance-covariance matrix for data to be modeled; conducts principal component analysis on the variance-covariance matrix; and creates a statistical model with an independent distribution for each principal component, allowing calculation of each individual model parameter as a weighted sum by a circuit simulator. The statistical model provides information about how well individual transistors will track one another based on layout similarity. This allows the designer to quantify and take advantage of design practices that make all transistors similar, for example, by orienting all gates in the same direction. A method, system and program product for simulating a circuit using the statistical model are also included.

REFERENCES:
Camelio et al.; Compliant Assembly Variation Analysis Using Component Geometric Covariance; Journal of Manufacturing Science and Engineering; pp. 355-360; May 2004.
Slezak, J,. et al., “On the Correlations Between Model Process Parameters in Statistical Modeling,” NSTI-Nanotech 2004, www.nsti.org, ISBN 0-9728422 8-4, vol. 2, 2004, pp. 144-146.
Singhal, K,. et al., “Statistical Device Models From Worst Case Files and Electrical Test Data,” IEEE Transactions on Semiconductor Manufacturing, vol. 12, No. 4, Nov. 1999, pp. 470-484.
Jess, J. A. G., et al., “Statistical Timing for Parametric Yield Prediction of Digital Integrated Circuits,” Annual ACM IEEE Design Automation Conference, Proceedings of the 40thConference on Design Automation, Anaheim, CA, Jun. 2003, pp. 932-937.
Stadlober, E., et al., “Simulation Models for Robust Design Using Location Depth Methods,” Quality and Reliability Engineering International, vol. 19, 2003, pp. 317-326.

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