Avoiding Mixed-Signal Field Returns by Outlier Detection of Hard-to-Detect Defects based on Multivariate Statistics

Abstract

With tightening automotive IC production test requirements, test escape rates need to decrease down to the 10 PPB level. To achieve this for mixed-signal ICs, advanced multivariate statistical techniques are needed, as the defects in the test escapes become increasingly more difficult to detect. Therefore, this paper proposes applying a cascade of advanced statistical techniques to identify measurements that can be used as predictors to flag future potential failures at test time with minimal misclassification of good devices. The approach uses measurement data from the ATE wafer probe tests and is also able to identify the likely location of the defect using only these measurements. The cascade has four steps: 1) remove bias and spatial patterns within the data, 2) divide the different tests into relevant groups, 3) reduce the dimensionality of each group, and 4) perform multiple regression to find the predictor values and use these values to compute an outlier score for each chip under test. As there is a risk of overfitting the outlier score, the number of predictors used is kept to a minimum. The effectiveness of the proposed methodology is demonstrated using test data from an industrial production chip with eight field-return cases. Predictors have been found that retroactively allowed the identification of these chips, with an average of 5% false classification of good devices, i.e. devices not returned from the field. In addition, the selected predictors corresponded to where the defects are located according to failure analysis of the field returns.