# Characterizing mixed-signal ICs for production

-February 20, 2013

An integrated circuit entering production must be available in high volume to meet the potential demand. Even though bench testing may show good results, in production the device must be tested with automated test equipment (ATE). What’s more, before the design’s release to production, it must undergo thorough characterization to verify that every tested device will fully meet its electrical specifications, as well as to uncover any process defects that could appear during the fabrication process. Here, we focus on mixed-signal device characterization, examining statistical techniques that ensure repeatability of test results, including testing over an IC’s rated temperature extremes.

Repeatability

Before releasing a device and its test solution to production, you must verify that the test solution itself is both accurate and repeatable. Gauge repeatability and reproducibility (GR&R) is a measure of the capability of a gauge or tester to obtain the same measurement reading every time the measurement is taken, and thus indicates the consistency and stability of the measuring equipment. Mathematically, it is a measure of the variation of a gauge’s measurement. Engineers must try to minimize the GR&R numbers of the measuring equipment, since a high GR&R number indicates instability and is thus unwanted. Reduced GR&R is a means of checking the repeatability of the test program.

Part of the procedure is to test multiple sites on a wafer and to do so multiple times. It’s important to note that only the actual active site should be powered. This avoids possible influence from other sites, such as crosstalk or interference from RF signals that could adversely affect test results.

Let’s assume you will test each site 50 times. With this approach, a 16-site test solution produces 800 (16×50) test results. The overall results can show a possible discrepancy between sites. You can then calculate the standard deviation and process capability index (Cpk) across all sites. The goal is to ensure a good, repeatable test program.

Figure 1 shows a comparison between one part tested 100 times on ATE and 300 parts tested one time on ATE. A reduced-repeatability-and-reproducibility report such as the one shown in the figure can provide you with the data to judge a measurement’s accuracy (of the measurement range, for example) and stability.

Figure 1
A reduced-repeatability-and-reproducibility report such as this one can provide the data to judge a measurement’s accuracy and stability. Here, one part is tested 100 times on ATE (a), and 300 parts are tested one time on ATE (b). LSL is the lower spec limit; USL is the upper spec limit.

To summarize the test sequence:

• One site is tested 50 times. All other sites are disabled (not powered) during test.
• Approximately 300 parts are tested with ATE to provide a comparison of lot variance with device repeatability.
• Statistical tools are used to analyze the data.

The measurements on the tester must correlate with the measurement results in the lab. In each situation, the project team must define the number of correlation devices and the parameters to be tested. This correlation can be started as soon as the test program is debugged and stable. At least 10 devices should be tested to guarantee that the data is correlating.

Cpk calculation

The process-capability-index value— defined from the mean value, the standard deviation (sigma), and the upper and lower specification limit—is an indication of how well the test parameter is under control with respect to its limits. For many devices, a desirable Cpk value would be 1.33, which would indicate a repeatability value of three times sigma. For automotive devices, however, a Cpk value of 2.00 would be preferable because of the Six Sigma rule.

Temperature testing

Testing at room temperature is necessary and important, but it’s even more important to test key parameters over a device’s fully specified operating temperature range. Temperature characterization shows the stability of the device over the specified operating temperature range. You should test approximately 300 parts at three previously defined temperatures; in addition, one part must be tested 100 times at three temperatures in order to calculate drift over temperature. You can use the resultant data to calculate temperature guard bands.

More on guard bands

In general, test engineering uses two kinds of guard banding: one for repeatability and one for temperature. There are both pros and cons to their use.

Engineers turn to repeatability guard bands to deal with the uncertainty of each measurement. The following example looks at drive current:

Segment drive source current: 37 mA (min), 47 mA (max)
LGB limit = lower spec limit + ε => 37.37 mA
UGB limit = upper spec limit − ε => 46.53 mA

Here, LGB is the lower guard band, UGB is the upper guard band, and ε is the uncertainty of the measurement.

The disadvantage of using repeatability guard bands is that a good device can be rejected as a bad device because of the uncertainty that the example demonstrates. The ideal case would be to use a guard-band limit of zero so that no good parts would be rejected. To reduce the impact of the guard band, you can improve the stability if the resulting measurement is a smaller repeatability guard band. The disadvantage would be a much longer test time due to device-settling time, an inherent issue with analog and mixed-signal ICs.

Every device, meanwhile, has a specified drift over temperature, which may be a typical or a guaranteed minimum/ maximum specification. Temperature guard bands have tighter test limits than the IC’s data-sheet specifications and need to be calculated based on the drift of the measurement over temperature. The advantage of using temperature guard bands is that you can skip test stages at other temperatures and instead calculate, based on the test results seen at room temperature, whether a device would fail at temperature extremes.

Figure 2 shows a temperature-characterization report with guard bands included. The plots demonstrate that there is drift over temperature. From this data, you must be able to predict that when testing production parts at +25°C, the drift at the temperature extremes will be within specification limits. That is the point of temperature guard bands for production testing.

Figure 2 This sample temperature-characterization report includes guard bands. The Y axis represents the number of parts; the X axis shows the measured value. Characterization is shown for 100 parts tested at +25°C (a), 1072 parts tested at +25°C (b), 1073 parts tested at +125°C (c), and 462 parts tested at −40°C (d).

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