Adaptive multidimensional outlier analysis for analog and mixed signal circuits

Outlier devices behave differently from the majority of the devices and are considered to be potentially defective. Identifying outliers has many applications in test, including defect filters for alternate test, and setting pass/fail limits for automotive domain. In previous work, outliers have been identified using single dimensional and/or static methods which does not exploit information efficiently. In this work, we propose an adaptive multidimensional outlier analysis method that combines the information of multiple measurement parameters and judiciously selects only information rich parameters to maximize detection probability. Furthermore, the proposed method continously updates to track process shift to enable adaptation to the evolving processes. The proposed method can be integrated within an existing test framework to improve test quality with little or no additional test time cost. In this context, we integrate our technique with an adaptive test framework and show that the method enables improved test quality.