1. Effect of measurement error budgets and hybrid metrology on qualification metrology sampling
- Author
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Matthew Sendelbach, Paul Isbester, Carmen Osorio, Kazuto Matsuki, Masafumi Asano, Hyang Kyun (Helen) Kim, Niv Sarig, Chas Archie, and Koichi Wakamoto
- Subjects
Observational error ,Computer science ,Mechanical Engineering ,Experimental data ,Total measurement ,Sampling (statistics) ,Condensed Matter Physics ,Atomic and Molecular Physics, and Optics ,Electronic, Optical and Magnetic Materials ,Reliability engineering ,Metrology ,Error analysis ,Electrical and Electronic Engineering ,Critical dimension ,Uncertainty analysis - Abstract
Until now, metrologists had no statistics-based method to determine the sampling needed for an experiment before the start that accuracy experiment. We show a solution to this problem called inverse total measurement uncertainty (TMU) analysis, by presenting statistically based equations that allow the user to estimate the needed sampling after providing appropriate inputs, allowing him to make important “risk versus reward” sampling, cost, and equipment decisions. Application examples using experimental data from scatterometry and critical dimension scanning electron microscope tools are used first to demonstrate how the inverse TMU analysis methodology can be used to make intelligent sampling decisions and then to reveal why low sampling can lead to unstable and misleading results. One model is developed that can help experimenters minimize sampling costs. A second cost model reveals the inadequacy of some current sampling practices—and the enormous costs associated with sampling that provides reasonable levels of certainty in the result. We introduce the strategies on how to manage and mitigate these costs and begin the discussion on how fabs are able to manufacture devices using minimal reference sampling when qualifying metrology steps. Finally, the relationship between inverse TMU analysis and hybrid metrology is explored.
- Published
- 2014