Wafer Pattern Recognition Using Tucker Decomposition

Ahmed Wahba, Li-C. Wang, Zheng Zhang, Nik Sumikawa

Abstract: In production test data analytics, it is often that an analysis involves the recognition of a conceptual pattern on a wafer map. A wafer pattern may hint a particular issue in the production by itself or guide the analysis into a certain direction. In this work, we introduce a novel approach to recognize patterns on a wafer map of pass/fail locations. Our approach utilizes Tucker decomposition to find projection matrices that are able to project a wafer pattern represented by a small set of training samples into a nearly-diagonal matrix. Properties of such a matrix are utilized to recognize wafers with a similar pattern. Also included in our approach is a novel method to select the wafer samples that are more suitable to be used together to represent a conceptual pattern in view of the proposed approach.