Effective Maintenance by Reducing Failure-Cause Misdiagnosis in Semiconductor Industry (SI)
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Abstract
Increasing demand diversity and volume in semiconductor industry (SI) have resulted in shorter product life cycles. This competitive environment, with high-mix low-volume production,
requires sustainable production capacities that can be achieved by reducing unscheduled equipment breakdowns. The fault detection and classification (FDC) is a well-known approach, used in the SI, to improve and stabilize the production capacities. This approach models equipment as a single unit and uses sensors data to identify equipment failures against product and process drifts. Besides its successful deployment for years, recent increase in unscheduled equipment breakdown needs an improved methodology to ensure sustainable capacities. The analysis on equipment utilization, using data collected from a world reputed semiconductor manufacturer, shows that failure durations as well as number of repair actions in each failure have significantly increased. This is an evidence of misdiagnosis in the identification of failures and prediction of its likely causes. In this paper, we propose two lines of defense against unstable and reducing production capacities. First, equipment should be stopped only if it is suspected as a source for product and process drifts whereas second defense line focuses on more accurate identification of failures and detection of associated causes. The objective is to facilitate maintenance engineers for more accurate decisions about failures and repair actions, upon an equipment stoppage. In the proposed methodology, these two lines of defense are modeled as Bayesian network (BN) with unsupervised learning of structure using data collected from the variables (classified as symptoms) across production, process, equipment and maintenance databases. The proofs of concept demonstrate that contextual or statistical information other than FDC sensor signals, used as symptoms, provide reliable information (posterior probabilities) to find the source of product/process quality drifts, a.k.a. failure modes (FM), as well as potential failure and causes. The reliability and learning curves concludes that modeling equipment at module level than equipment offers 45% more accurate diagnosis. The said approach contributes in reducing not only the failure durations but also the number of repair actions that has resulted in recent increase in unstable production capacities and unscheduled equipment breakdowns.
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unscheduled maintenance, Bayesian networks, maintenance actions effectiveness, Semiconductor Industry, diagnostic
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