Novel Methods To Enhance Data Quality in FMEA Documents In Semiconductor Manufacturing

Researchers propose novel methods to enhance the data quality in FMEA documents.


New research paper from Graz University of Technology & others.

“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.”

Find the open access technical paper link here. Published Feb. 2022.

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.

Visit Semiconductor Engineering’s Technical Paper library here and discover many more chip industry academic papers.

Leave a Reply

(Note: This name will be displayed publicly)