Cross-Correlation Approach to Detecting Issue Test Sites in Massive Parallel Testing

Abstract

Parallel (multi-site) testing has become one of the semiconductor industry’s standards for testing chips. The method tests multiple chips in parallel, increasing throughput and cutting test time and costs. In digital IC testing, the number of sites has currently reached the level of thousands. However, the site count is still significantly lower when accurate analog testing is required. Due to the increased complexity involved in managing multiple test sites, variations are now being observed in the measurements from site to site for analog and mixed-signal testing. Some test sites’ measurements no longer reflect the true performance of the device under test (DUT) and can lead to yield loss and possible test escapes. As it is a recent issue, very little work has been done on robust and accurate detection of issue sites. We project that it will be an important issue in the future, especially as the number of test sites in multi-site analog testing continues to increase. A method capable of effectively detecting test sites exhibiting pronounced multi-site variation is presented. The proposed method utilizes the cross-correlation similarity between the distribution of each test site and a reference distribution to detect issue sites. Boundary conditions are derived for the method using a significance level, and a site is considered an issue site if the proposed method scores for that site fall outside the derived boundary for each measured specification. The method is applied to real test data from the industry. The presented results demonstrate the effectiveness of the approach.