A Nonnegative Tensor Factorization Approach for Three-Dimensional Binary Wafer-Test Data

Abstract

We introduce a new Blind Source Separation Approach called binNTF which operates on tensor-valued binary datasets. Assuming that several simultaneously acting sources or elementary causes are generating the observed data, the objective of our approach is to uncover the underlying sources as well as their individual contribution to each observation with a minimum number of assumptions in an unsupervised fashion. We motivate, develop and demonstrate our method in the context of binary wafer test data which evolve during microchip fabrication. In this application, we also have to deal with incomplete datasets which can occur due to the commonly used stop-on-first-fail testing procedure or result from the aggregation of several distinct tests into BIN categories.