Data mining for improving a cleaning process in the semiconductor industry

Dan Braha, Armin Shmilovici

Research output: Contribution to journalArticlepeer-review

88 Scopus citations

Abstract

As device geometry continues to shrink, micro-contaminants have an increasingly negative impact on yield. By diminishing the contamination problem, semiconductor manufacturers will significantly improve the wafer yield. This paper presents a comprehensive and successful application of data mining methodologies to the refinement of a new dry cleaning technology that utilizes a laser beam for the removal of micro-contaminants. Experiments with three classification-based data mining methods (decision tree induction, neural networks, and composite classifiers) have been conducted. The composite classifier architecture has been shown to yield higher accuracy than the accuracy of each individual classifier on its own. The paper suggests that data mining methodologies may be particularly useful when data is scarce, and the various physical and chemical parameters that affect the process exhibit highly complex interactions. Another implication is that on-line monitoring of the cleaning process using data mining may be highly effective.

Original languageEnglish
Pages (from-to)91-101
Number of pages11
JournalIEEE Transactions on Semiconductor Manufacturing
Volume15
Issue number1
DOIs
StatePublished - 1 Feb 2002

Keywords

  • Composite classifiers
  • Data mining
  • Laser cleaning
  • Machine learning

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