Fail data collection is carried out for faulty units to allow defect diagnosis to be performed.
After passing scan chain tests, fail data collection targets faults in the functional logic …

In the early stages of yield improvement, a faulty unit may produce a large volume of fail data
because of the presence of multiple defects in the functional logic. Earlier solutions that …

Failure diagnosis is a software-based, data-driven procedure. Collecting an excessive
amount of fail data not only increases the overall test cost but can also potentially reduce …

The pursuit of accurate diagnosis with good resolution is driven by yield learning during both
early bring-up and production excursions. Unfortunately, fault callouts from diagnosis tools …

When LBIST is used for test application in-field, and the presence of a defect is detected by
the LBIST test set, it is advantageous if the same LBIST test set is used for diagnosis, either …

Volume diagnosis and debug play a key role in identifying systematic test failures caused by
manufacturing defectivity, design marginalities, and test overkill. However, diagnosis tools …

Failure diagnosis is a software-based data-driven procedure. Collecting an excessive
amount of fail data not only increases the overall test cost, but may also lead to degradation …

Software diagnosis is the process of locating and characterizing a defect in a failing chip. It is
the cornerstone of failure analysis that consequently enables yield learning and monitoring …

Diagnosis of faults in logic circuit is essential to improve the yield of semiconductor circuit
production. However, accurate diagnosis of adjacent multiple faults is difficult. In this paper …

Yield improvement requires information about the defects present in faulty units. This
information is derived by applying a logic diagnosis procedure to the fail data collected by a …