A survey of our recent work on probabilistic NMF is provided. All variants discussed here are illustrated by their application to the analysis of failure patterns emerging from manufacturing and processing silicon wafers. It starts with binNMF, a variant developed to apply NMF to binary data sets. The latter are modeled as a probabilistic superposition of a finite number of intrinsic continuous-valued failure patterns characteristic for the manufacturing process. We further discuss related theoretical work on a semi-non-negative matrix factorization based on the logistic function, which we called logistic NMF. While addressing uniqueness issues, we propose a Bayesian Optimality Criterion for NMF and a determinant criterion to geometrically constrain the solutions of NMF problems, leading to detNMF. This approach also provides an intuitive explanation for the often used multilayer approach. Finally, we present a Variational Bayes NMF (VBNMF) algorithm which represents a generalization of the famous Lee–Seung method. We also demonstrate its ability to estimate the intrinsic dimension (model order) of the NMF method.