There are many wafer level tests, such as Fail Bit Count (FBC), where conventional statistical analysis methods are inadequate because the associated data do not follow a normal distribution. This paper introduces a statistical failure analysis technique that does not rely on location and scale parameters and is thus able to handle such cases. It describes the math on which the method is based and explains how to determine effect size (ES) using the quantile comparison equivalence criteria (QCEC) and a statistical parameter, called the center of dispersion (CoD), that distinguishes between center difference and dispersion difference. It also includes a case study showing how the new method is used to assess the effect of a process change on dynamic random access memory test data and how it compares in terms of accuracy with conventional statistical techniques.

Rapid and accurate root cause analysis of the defect contributes to improvement in yield and quality in semiconductor manufacturing system. In particular, imperfection of final test can cause major problems for customers, so analysis on root cause of final test failure is important activity for high quality. It can be started with finding first test data which is highly correlated with final test failure. However, it is difficult to analyze the correlation of first test data and final test failures because the first test is made up of hundreds of test items, and the data also show non-parametric characteristics with extreme outlier. In this study, Kolmogorov-Smirnov test (K-S test), which is a non-parametric test method, is statistically applied to the first test data. The K-S test is intuitive and descriptive, which makes it easy to analyze the root cause. And K-S test showed a performance improvement compared to t-test statistic, which requires a normal distribution assumption. Therefore, our data mining approach can help analysis to improve yield and quality of mass production with highly scaled devices.