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Regularized logistic regression for fast importance sampling based SRAM yield analysis | IEEE Conference Publication | IEEE Xplore

Regularized logistic regression for fast importance sampling based SRAM yield analysis


Abstract:

In this paper, we propose a fast logistic regression based importance sampling methodology with ordered feature selection to avoid overfitting and enable regularization. ...Show More

Abstract:

In this paper, we propose a fast logistic regression based importance sampling methodology with ordered feature selection to avoid overfitting and enable regularization. We rely on the importance region search simulations to build a regularized logistic regression model that is capable of accurately predicting pass fail criteria for purposes of yield analysis stage. We also propose a cross-validation-based regularization framework for ordered feature selection. We prove the efficiency of the proposed methodology by analyzing state-of-the-art FinFET SRAM designs. The proposed methodology is comprehensive and computationally efficient resulting in high-fidelity models. We report on average around 4.5% false prediction rate for the importance sample points prediction. This translates into accurate yield prediction for the rare fail events. All this comes at significant savings in runtime.
Date of Conference: 14-15 March 2017
Date Added to IEEE Xplore: 04 May 2017
ISBN Information:
Print ISSN: 1948-3287
Conference Location: Santa Clara, CA, USA

References

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