Improving the Consistency of the Failure Mode Effect Analysis (FMEA) Documents in Semiconductor Manufacturing

Houssam Razouk*, Roman Kern

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Digitalization of causal domain knowledge is crucial. Especially since the inclusion of causal domain knowledge in the data analysis processes helps to avoid biased results. To extract such knowledge, the Failure Mode Effect Analysis (FMEA) documents represent a valuable data source. Originally, FMEA documents were designed to be exclusively produced and interpreted by human domain experts. As a consequence, these documents often suffer from data consistency issues. This paper argues that due to the transitive perception of the causal relations, discordant and merged information cases are likely to occur. Thus, we propose to improve the consistency of FMEA documents as a step towards more efficient use of causal domain knowledge. In contrast to other work, this paper focuses on the consistency of causal relations expressed in the FMEA documents. To this end, based on an explicit scheme of types of inconsistencies derived from the causal perspective, novel methods to enhance the data quality in FMEA documents are presented. Data quality improvement will significantly improve downstream tasks, such as root cause analysis and automatic process control
Original languageEnglish
Article number1840
JournalApplied Sciences
Issue number4
Publication statusPublished - 1 Feb 2022


  • Causal data science
  • Consistency improvement
  • Digitalization
  • FMEA
  • NLP
  • Semiconductor manufacturing industry

ASJC Scopus subject areas

  • Engineering(all)
  • Instrumentation
  • Materials Science(all)
  • Fluid Flow and Transfer Processes
  • Process Chemistry and Technology
  • Computer Science Applications


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