The previous approaches on parametric yield prediction are limited to either predicting parametric yield for single performance metric

or balanced optimizing several single-parametric yields.Consequently

these approaches may result in significant yield loss.In order to avoid the deficiency

this paper suggests a copula-based multi-parametric yield prediction method.First

the method models power and timing randomly and correlatively.Then

saddle point line sampling is applied to estimate their marginal distributions.Finally

according to copula theory

the multi-parametric yield is predicted.Experimental results demonstrate that comparing to Monte Carlo (MC) simulation

the proposed method has higher simulation efficiency

the simulation time is reduced by more than 12%.In addition

the relative errors of the results of this method and MC simulation in different ISCAS benchmark circuits are maintained at less than 9%

and under arbitrary performance limit

the proposed method can predict multi-parametric yield effectively.It provides the designer with yield information that considers multiple performance metrics simultaneously.