Improving Diagnosis Resolution with Population Level Statistical Diagnosis

In this paper, we present a diagnosis resolution improvement methodology for scan-based tests. We achieve 89% reduction in the number of suspect diagnosis locations and a 2.4X increase in the number of highly resolved diagnosis results. We suffer a loss in accuracy of 1.5%. These results were obtained from an extensive silicon study. We use data from pilot wafers and 11 other wafers at the leading-edge technology node and check against failure analysis results from 203 cases. This resolution improvement is achieved by considering the diagnosis problem at the level of a population (e.g. a wafer) of failing die instead of analyzing each failing die completely independently as has been done traditionally. Higher diagnosis resolution is critical for speeding up the yield learning from manufacturing test and failure analysis flows.

Improving Quality of Manufacturing Test using Cell-Aware ATPG

The existing fault models like stuck-at, small delay defect, transition, and bridge fault models and their associated patterns are becoming less efficient, as the technology moves to increasingly smaller geometries. It is because traditional defect models target the faults only on IC gate boundaries, and the interconnects between the cells, but a significant population of defects (perhaps up to 50%) occurs within the cells or gates which are not specifically targeted by existing ATPG fault models. In this paper, a new ATPG methodology known as the Cell-aware test is implemented explicitly to target the defects caused by cell-internal open and short faults and improve the manufacturing test quality by minimizing the test escapes. This work explains how a Cell-Aware ATPG method performs a characterization on the GDSII data of library cell`s to produce a CAT library view (UDFM), test Pattern generation, and comparison between Traditional and Cell-Aware ATPG. The Cell-Aware ATPG is implemented using Tessent Testkompress, traditional ATPG is also developed to study and analyze both ATPG methodologies comparatively. Experiment results show a significant improvement in faults being targeted at an expense of an increase in pattern count and run-time. Obtained 71.28% and 59.38% test coverage for UDFM static and UDFM delay respectively. Achieved significant improvement in the test escapes with Cell-Aware Patterns when compared to traditional ATPG patterns.