Gage R&R has been used for many years in the semiconductor industry to measure variation during test for repeatability and reproducibility. These measurements are then used to assess the suitability of the test for production. But often the focus is on qualifying tests on one site only leaving issues for production when multi-site testing is used. See an example below of a parameter where the site 2 measurements are different to the other sites and give many false fails. This would fail Gage R&R.
Many Factors at Once
Gage R&R typically requires that tests are qualified with only one test environment factor at a time, so it is often the case that not all factors are assessed. This is simply due to time and resource constraints. It is rare that test engineers will have a few weeks to do many studies. So why not consider more factors at once? The main reason is the complexity of interpreting the results for multi-factor analysis. However with modern analysis systems this is no longer an issue. The new version 3 of Gage R&R in yieldHUB for example will support the entire process from debug of tests, to qualification of tests, to qualification of the test environment.
So back to the multi-site problem shown above. The efficient test development route is to assess repeatability on one or two units and fine-tune tests as needed. Then carry out a study with more devices and a key factor of variation. And finally vary everything in a single study and analyse the data. By being able to select data and run reports in a few mouse clicks generating tens of fields of statistics makes it much more efficient to analyse the data. Data collection can be ambitious to prove that the entire environment is stable and can be qualified. But if a problem is seen the cause can be traced and only if necessary are more detailed gage studies carried out.
If your yield ramp-up of multi-site testing is not looking smooth in initial production then yieldHUB’s Gage R&R V3 is there to help you. The unique combination of MSA Gage R&R analysis along with additional max range analysis brings more insight into the data and the causes of issues in test. It is possible to launch multi-site testing into production with high yield from day one and this can be achieved with less effort than ever before.
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