An Efficient Prediction Framework for Multi-parametric Yield Based on Uncertainty of Performance-Relevant Structure

Abstract

Due to the aggressive shrinking of integrated circuit (IC) fabrication technology, variability in design parameters leads to significant yield loss. Therefore, efficient parametric yield prediction has been a critical task for today’s ICs, especially when multiple performances are considered simultaneously. However, most of the previous yield prediction works have failed to take into account uncertainty of performance-relevant structures. Neglecting the uncertainty effects will tend to result in predictive accuracy loss. To avoid the issue, this paper proposes a multi-parametric yield prediction framework based on the uncertainty of performance-relevant structures. In the proposed framework, saddle point estimation and mixed copula function are applied to maintain the uncertainty-relevant structures among performance metrics and predict the multi-parametric yield accurately. First, the framework constructs a general statistical model for performance metrics to illustrate the uncertainty of performance-relevant structures. The general model is explicitly expressed in terms of underlying process, voltage, and temperature (PVT) parameters. Then, based on the general performance model and taking total leakage current and gate delay for example, their marginal distributions are estimated by assuming the PVT parameters as normal random variables. Finally, a mixed copula-based yield prediction method is suggested to solve the uncertainty-relevant structure problem. Experimental results demonstrate that the proposed yield prediction framework is capable of predicting multi-parametric yield under arbitrary performance-relevant structures. Compared to Monte Carlo (MC) analysis, the relative errors of yield prediction are less than 5%, which means the framework has good accuracy and efficiency.