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

Abstract

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.

Improving FMEA Comprehensibility via Common-Sense Knowledge Graph Completion Techniques

“…More recent works on FMEA explore integration with other techniques such as decision trees and fuzzy groups [27], bayesian networks [28] and deep learning [9]. However, to the best of our knowledge, no previous scholars considered the use of the FMEA data set as domain specific commonsense knowledge base.…”

“…FMEA documents often contain short text with domainspecific language and abbreviations [9]. In these documents, conceptually related nodes are often represented as distinct nodes.…”

Reliability calculation with error tree analysis and breakdown effect analysis for a Quadcopter power distribution system

“…Table 5 shows the failure effects analysis for a quadcopter, in which we examined some of the most common quadcopter failures and identified and noted the effect of each failure on quadcopter performance. By doing this, if we observe any of these effects, we can quickly find the cause of the failure and fix it [14][15][16][17].…”

Reliability calculation with error tree analysis and breakdown effect analysis for a quadcopter power distribution system

“…Table 7 shows the failure effects analysis for a quadcopter, in which we examined some of the most common quadcopter failures and identified and noted the effect of each failure on quadcopter performance. By doing this, if we observe any of these effects, we can quickly find the cause of the failure and fix it [28][29][30][31]. Failure modes that have a severity number of 4 or a critical number higher than 6 are critical.…”