Time-synchronized sensor clustering via rare events: A case study in semiconductor manufacturing processes

2026-04-21

PRE

Mahya Qorbani*

Myunggeun Song

Kamran Paynabar*

DOI：10.1080/00224065.2026.2645966

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In semiconductor manufacturing, accurate and timely anomaly detection is critical. However, the presence of a large number of tightly controlled sensors often results in excessive false alarms, and sensor clustering has emerged as one of the practical approaches to mitigate this issue. This case study develops and validates a synchronization-based similarity framework, referred to as the Sync Ratio, for clustering sensor signals based on the temporal co-occurrence of rare events. Rather than relying on magnitude-based correlations such as Pearson correlation, the proposed framework emphasizes shared anomalous behavior, enabling the identification of physically related sensors with numerically dissimilar responses—without requiring detailed domain knowledge. The contribution lies in reframing inter-sensor similarity through rare-event synchronization and demonstrating its effectiveness at scale in a real-world manufacturing environment. Using real data from a dry etcher, the approach demonstrates improved capability in distinguishing true process anomalies from false alarms and outperforms several benchmark clustering methods in practical fault analysis tasks. Our findings indicate that synchronization-focused sensor clustering can provide an intuitive and effective tool for enhancing process monitoring and quality improvement in semiconductor manufacturing.

Keywords:

dry etching process

false alarm filtering

semiconductor manufacturing

sensor clustering

time series data

Time-synchronized sensor clustering via rare events: A case study in semiconductor manufacturing processes

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