Multivariate statistical methods for modeling and analysis of wafer probe test data

Katina R. Skinner, Douglas Montgomery, George Runger, John Fowler, Daniel R. McCarville, Teri Reed Rhoads, James D. Stanley

Research output: Contribution to journalArticle

40 Scopus citations

Abstract

Probe testing following wafer fabrication can produce extremely large amounts of data, which is often used to inspect a final product to determine if the product meets specifications. This data can be further utilized in studying the effects of the wafer fabrication process on the quality or yield of the wafers. Relationships among the parameters may provide valuable process information that can improve future production. This paper compares many methods of using the probe test data to determine the cause of low yield wafers. The methods discussed include two classes of traditional multivariate statistical methods, clustering and principal component methods and regression-based methods. These traditional methods are compared to a classification and regression tree (CART) method. The results for each method are presented. CART adequately fits the data and provides a "recipe" for avoiding low yield wafers and because CART is distribution-free there are no assumptions about the distributional properties of the data. CART is strongly recommended for analyzing wafer probe data.

Original languageEnglish (US)
Pages (from-to)523-530
Number of pages8
JournalIEEE Transactions on Semiconductor Manufacturing
Volume15
Issue number4
DOIs
StatePublished - Nov 1 2002

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Keywords

  • CART
  • Multivariate statistical methods
  • Tree regression
  • Yield analysis

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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