Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing

Dongil Kim, Pilsung Kang, Sungzoon Cho, Hyoung Joo Lee, Seungyong Doh

Research output: Contribution to journalArticlepeer-review

79 Citations (Scopus)


Since semiconductor manufacturing consists of hundreds of processes, a faulty wafer detection system, which allows for earlier detection of faulty wafers, is required. statistical process control (SPC) and virtual metrology (VM) have been used to detect faulty wafers. However, there are some limitations in that SPC requires linear, unimodal and single variable data and VM underestimates the deviations of predictors. In this paper, seven different machine learning-based novelty detection methods were employed to detect faulty wafers. The models were trained with Fault Detection and Classification (FDC) data to detect wafers having faulty metrology values. The real world semiconductor manufacturing data collected from a semiconductor fab were tested. Since the real world data have more than 150 input variables, we employed three different dimensionality reduction methods. The experimental results showed a high True Positive Rate (TPR). These results are promising enough to warrant further study.

Original languageEnglish
Pages (from-to)4075-4083
Number of pages9
JournalExpert Systems With Applications
Issue number4
Publication statusPublished - 2012 Mar
Externally publishedYes


  • Dimensionality reduction
  • Faulty wafer detection
  • Novelty detection
  • Semiconductor manufacturing
  • Virtual metrology

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence


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