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Article

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

by 1,2,* and 1,3
1
Institute of Interactive Systems and Data Science, Graz University of Technology, 8010 Graz, Austria
2
Innovation Funding Management, Infineon Technologies Austria AG, 9500 Villach, Austria
3
Area of Knowledge Discovery, Know-Center GmbH, 8010 Graz, Austria
*
Author to whom correspondence should be addressed.
Academic Editor: Albert Smalcerz
Appl. Sci. 2022, 12(4), 1840; https://doi.org/10.3390/app12041840
Received: 29 December 2021 / Revised: 27 January 2022 / Accepted: 7 February 2022 / Published: 10 February 2022
(This article belongs to the Special Issue Advanced Applications of Industrial Informatic Technologies)
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. View Full-Text
Keywords: digitalization; semiconductor manufacturing industry; FMEA; NLP; consistency improvement; causal data science digitalization; semiconductor manufacturing industry; FMEA; NLP; consistency improvement; causal data science
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MDPI and ACS Style

Razouk, H.; Kern, R. Improving the Consistency of the Failure Mode Effect Analysis (FMEA) Documents in Semiconductor Manufacturing. Appl. Sci. 2022, 12, 1840. https://doi.org/10.3390/app12041840

AMA Style

Razouk H, Kern R. Improving the Consistency of the Failure Mode Effect Analysis (FMEA) Documents in Semiconductor Manufacturing. Applied Sciences. 2022; 12(4):1840. https://doi.org/10.3390/app12041840

Chicago/Turabian Style

Razouk, Houssam, and Roman Kern. 2022. "Improving the Consistency of the Failure Mode Effect Analysis (FMEA) Documents in Semiconductor Manufacturing" Applied Sciences 12, no. 4: 1840. https://doi.org/10.3390/app12041840

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