Automated diagnosis of anomalies via sensor-step data outlier detection: An application in semiconductors
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Authors
Al Iqbal, MD Ridwan
Vargas, Andrés
Erickson, John S.
Bennett, Kristin P.
Issue Date
2018-08-20
Type
Article
Language
Keywords
Alternative Title
Abstract
The diagnosis of potential mechanical defects and sources of
variation impacting wafer quality is an essential task in semiconductor manufacturing. During manufacturing, semiconductor fabrication tools are monitored by sensors that capture critical environmental information including temperature, power, and pressure. Our goal is to create an online
and unsupervised anomaly detection system able to detect
potential mechanical faults and other significant anomalies
by monitoring sensor data from semiconductor process tools
and then automatically generating a comprehensive report
to help define putative causes of these anomalies for process
engineers. Our framework represents the raw multivariate
time series data as a tensor of sensor-step pairs. Exploiting
information at recipe step granularity enables the framework
to be easily deployed to new tools and recipes. The system
finds instances that significantly differ from a normal model
of the sensor-step pairs. A moving window Principal Component Analysis (PCA) model is used to capture the normal
model, and departures from this model indicate potential
anomalies. The system is modular, so different anomaly detection algorithms can be used. The dynamically-generated
reports produced by the system contain lists of influential
sensor-step pairs and also include visualizations and statistical tests explaining and validating the detected anomalies.
The generated report includes the processed data and sample analysis code process engineers need to expedite further
exploration of the anomalies. The anomaly detection and diagnosis system has been demonstrated by experimenting on
wafer trace data generated by three different process nodes
or types, with data from 13 different chambers. Our experimental results show that the detected sensor-step anomalies
are indeed of engineering significance.
Description
Full Citation
Automated diagnosis of anomalies via sensor-step data outlier detection: An application in semiconductors MR Al Iqbal, A Vargas, JS Erickson, KP Bennett - SIGKDD ODD v5. 0 Workshop, 2018
Publisher
ACM