Fault detection using random forest similarity distance

Luca Puggini, John Doyle, Seán McLoone

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


To maintain the pace of development set by Moore's law, semiconductor manufactures continue to shrink and redesign transistor architectures delivering better device performance. This has led to an increase in the complexity of the manufacturing process, where new technologies typically consist of several hundred processing steps. In this context, the impact of an incorrectly processed wafer progressing through the manufacturing process from start to finish can have a serious negative impact on profitability. In this paper, we demonstrate an unsupervised method based on random forests which can identify faulty wafers from the chemical signatures observed during a plasma etching process. The method is evaluated using both a simulated example and a real industrial dataset. Results show the correct identification of faulty wafers in both studies. The paper is concluded with a summary of research findings and a discussion on future work.

Original languageEnglish
Pages (from-to)583-588
Number of pages6
Issue number21
Early online date15 Oct 2015
Publication statusPublished - 2015


  • Fault detection
  • Optical emission spectroscopy
  • Plasma etching
  • Random forests
  • Semiconductor manufacturing

ASJC Scopus subject areas

  • Control and Systems Engineering


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