A hierarchical ensemble causal structure learning approach for wafer manufacturing

In manufacturing, causal relations between components have become crucial to automate assembly lines. Identifying these relations permits error tracing and correction in the absence of domain experts, in addition to advancing our knowledge about the operating characteristics of a complex system. This paper is motivated by a case study focusing on deciphering the causal structure of a wafer manufacturing system using data from sensors and abnormality monitors deployed within the assembly line. In response to the distinctive characteristics of the wafer manufacturing data, such as multimodality, high-dimensionality, imbalanced classes, and irregular missing patterns, we propose a hierarchical ensemble approach. This method leverages the temporal and domain constraints inherent in the assembly line and provides a measure of uncertainty in causal discovery. We extensively examine its operating characteristics via simulations and validate its effectiveness through simulation experiments and a practical application involving data obtained from Seagate Technology. Domain engineers have cross-validated the learned structures and corroborated the identified causal relationships.