Application of an ordinal optimization algorithm to the wafer testing process

Shin Yeu Lin*, Shih Cheng Horng

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

17 Scopus citations

Abstract

In this correspondence, we have formulated a stochastic optimization problem to find the optimal threshold values to reduce the overkills of dies under a tolerable retest level in wafer testing process. The problem is a hard optimization problem with a huge solution space. We propose an ordinal optimization theory-based two-level algorithm to solve for a vector of good enough threshold values and compare with those obtained by others using a set of 521 real test wafers. The test results confirm the feature of controlling the retest level in our formulation, and the pairs of overkills and retests resulted from our approach are almost Pareto optimal. In addition, our approach spends only 6.05 min in total in a Pentium IV personal computer to obtain the good enough threshold values.

Original languageEnglish
Pages (from-to)1229-1234
Number of pages6
JournalIEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans
Volume36
Issue number6
DOIs
StatePublished - 11 2006
Externally publishedYes

Keywords

  • Genetic algorithm (GA)
  • Neural network
  • Ordinal optimization (OO)
  • Overkill
  • Retest
  • Stochastic optimization
  • Water probing

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