Various methods of estimating the fault probabilities based on defect data of
random defects seen in integrated circuit manufacturing are examined. Estimates of
fault probabilities based on defect data are less costly than those based on critical area
analysis and are potentially more reliable because they are based on actual
manufacturing data. Due to limited sample size, means of estimating the confidence
interval associated with these estimates are also examined. Because the mathematical
expressions associated with defect data-based estimates of the fault probabilities are
not amenable to analytical means of obtaining confidence intervals, bootstrapping
was employed.
The results show that one method of estimating the fault probabilities based
on defect data proposed previously is not applicable when using typical in-line data.
Furthermore, the results indicate that under typical fab conditions, the assumption of a
Poisson random defect distribution gives accurate fault probabilities. The yields as
predicted by the fault probabilities estimated from the limited yield concept and kill
ratio and those estimated from critical area simulation are shown to be comparable to
actual yields observed in the fab. It is also shown that with in-line data, the FP
estimated for a given inspection step is a weighted average of the fault probabilities of
the defect mechanisms operating at that inspection step.
Four bootstrapped based methods of confidence interval estimation for fault
probabilities of random defects are examined. The study is based on computer
simulation of randomly distributed defects with pre-assigned fault probabilities on
dice and the resulting count of different categories of die. The results show that all
four methods perform well when the number of fatal defects is reasonably high but
deteriorate in performance as the number of fatal defects decrease. The results also
show that the BCA (bias-corrected and accelerated) method is more likely to
succeed with a smaller number of fatal defects. This success is attributed to its ability
to account for change of the standard deviation of the sampling distribution of the FP
estimates with the PP of the population, and to account for median bias in the
sampling distribution. / Graduation date: 2004
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/30818 |
| Date | 11 September 2003 |
| Creators | Hu, David T. |
| Contributors | Koretsky, Milo D. |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
Page generated in 0.0014 seconds