## 2 Materials and Methods  
Based on the analysis of actual FMEA documents from a semiconductor manufacturing company, most data consistency impairments are attributed to one of two main categories: (a) FMEA documents' cell consistency or (b) FMEA relations consistency, where our proposed methods address both types of inconsistencies. Our primary approach relies on causal data science for checking the consistency of FMEA documents. Causal data science is concerned with the underlying data generation process. Thus, causal data science aims to adjust for spurious correlations present in the data. The spurious correlations could be a result but not limited to confounding bias or collider bias. Biases are translated in the FMEA documents to cases of merged cells. One example for confounder bias could be: _\"layer thickness \(A\)\"_ affect _\"Functional parameter \(A\) and Functional parameter \(B\)\"_. Collider bias may look like: _\"layer thickness \(A\) is out of spec\"_ due to _\"process \(A\) deviation or process \(B\) deviation\"_. In the cases of merged cells due to biases (i.e., confounding and conditioning on colliders), they result from how the FMEA documents are crafted, i.e., first identify the failure modes, then place the potential effects and the root causes. Thus, the failure mode could be a collider for the root causes or a confounder for the effects. The FMEA documents do not seem to support the cases of interaction between multiple concepts, based on our study of FMEA documents. Thus, cases of merged cells that describe the interaction between concepts are typical to occur. An example could be: _\"layer \(A\) stress **in combination** with layer \(B\) thickness limit violation\"_ causes _\"functional parameter \(C\) to deviate\"_. The _\"layer \(A\) stress\"_ alone is insufficient to cause _\"the functional parameter \(C\) to deviate\"_. Also \"the functional parameter \(C\) deviation\"_ is not caused by _\"layer \(B\) thickness limit violation\"_ alone. In such cases, the merged cell might contain additional information about the relationships of the individual concepts. However, in some cases, not all the individual concepts are stated. To handle such cases, we follow [PERSON] and [PERSON] steps as proposed in [PERSON] and [PERSON] (2015). [PERSON] and [PERSON] studied the interaction between concepts introducing sufficient causal directed acyclic graphs. The sufficient cause is added to the original causal directed acyclic graphs as an artificial node to describe the interaction between the causes. In addition, the individual concepts are also represented in the graph and connected to the artificial node. The interaction of such concepts happens only when these concepts fulfill the role of causes in causal relations. Thus, in the FMEA documents, such merged cells of cases are only occurring as failure mode or root cause. We also observed cases of merged cells that contain an entire causal chain of multiple causes and effects (e.g., _\"layer thickness \(A\) violation causes layer thickness \(B\) violation\"_). We assume that this is mainly attributed to the manual creation of FMEA documents by human domain experts, in combination with a broad definition of FMEA's columns. For example, domain experts may try to document causal chains, which stretch across multiple cause and effect pairs. To summarize, in the category of FMEA documents' cell consistency impairments, we noticed cases of merged cells, for which we further categorized into four categories, based on our observation of real-world FMEA documents. These four categories of merged cell types are depicted in Figure 2 and can be described as:1. [label=()]
2. Merged cells containing a causal chain;
3. Merged cells as a result of confounding bias;
4. Merged cells as a result of bias caused by conditioning on a collider;
5. Merged cells caused by describing the interaction between concepts. As a result of merged cells, the usability of the FMEA document for further processing is seriously limited.