Failure Detection and Primary Cause Identification of Multivariate Time Series Data in Semiconductor Equipment

Minjae Baek, Seoung Bum Kim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Downtime caused by equipment failure is the biggest productivity problem in the 24-hour a day operations of the semiconductor industry. Although some equipment failures are inevitable, increases in productivity can be gained if the causes of failures can be detected quickly and repaired, thus reducing downtime. Univariate control charts are commonly used to detect failures. However, because of the complexity of the process and the structural characteristics of the equipment, detection and identification of the causes of failures may be difficult. The purpose of this study is to use correlations of variables to detect failures in semiconductor equipment, to predict the parts to be replaced and to identify the primary causes of failures. The proposed method consists of four steps: (1) conversion of the multivariate time series data of the equipment into signature matrixes, (2) detection of anomalies through a convolutional autoencoder, (3) learning classification models with supervised learning methods that use the residual matrixes of fault sections, and (4) application of an explainable algorithm to interpret the classification model. The effectiveness and applicability of the proposed method are demonstrated by the actual multivariate time series data obtained from 8-inch ashing process equipment that produces semiconductors on 8-inch silicon wafers.

Original languageEnglish
Pages (from-to)54363-54372
Number of pages10
JournalIEEE Access
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • Anomaly detection
  • explainable artificial intelligence
  • multivariate time series
  • semiconductor process
  • signature matrix

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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