An accurate yield estimation approach for multivariate non-normal data in semiconductor quality analysis

摘要

The standard multivariate metrics for semiconductor product yield estimation and prediction in production processes usually assume that the parameters contributing to the yield are all normally distributed. However, the data met in production processes is not always multivariate normal. A variety of methods has been developed for multivariate non-normal data, but these usually rely on no statistical information, address only a specific type of multivariate distributions, or become very time consuming from the point of view of the computational cost. Moreover, the sample size of the multivariate data is often insufficient, as only a limited number of measurements are affordable. This results in inaccurate product yield estimation and high variance of the estimates. In this paper, a multivariate distribution fitting methodology is introduced, which, combined with multivariate random data sampling provides a global yield estimation approach. Compared with the simple failure counts method the estimation variance of the proposed method is two times smaller.