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Exploiting defect clustering for yield and reliability prediction

Exploiting defect clustering for yield and reliability prediction

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An integrated yield–reliability model is verified using burn-in data from 77 000 microprocessor units manufactured by IBM Microelectronics. The model is based on the fact that defects over semiconductor wafers are not randomly distributed but have a tendency to cluster. It is shown that this fact can be exploited to produce dies of varying reliability by sorting dies into bins based on how many of their neighbours test faulty. Dies that test as good at the wafer probe, yet come from regions with many faulty dies, have a higher incidence of infant mortality failure than dies from regions with few faulty dies. The yield–reliability model is used to predict the fraction of good dies in each bin following a wafer probe as well as the fraction of failures in each bin following stress testing (e.g. burn-in). Results show excellent agreement between model predictions and observed data.

Inspec keywords: microprocessor chips; integrated circuit reliability; life testing; integrated circuit modelling; logic testing; integrated circuit testing

Other keywords: infant mortality failure; burn-in data; integrated yield-reliability model; microprocessor units; defect clustering; stress testing; fault testing; bins; IBM Microelectronics; semiconductor wafers; dies; wafer probe

Subjects: Reliability; Maintenance and reliability; Microprocessor chips; Digital circuit design, modelling and testing; Testing; Semiconductor industry; Microprocessors and microcomputers; Logic design methods; Semiconductor integrated circuit design, layout, modelling and testing

References

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