APT: Optimal Tree for Diagnosis Simulation

Performing scan test failure diagnosis is necessary for advanced semiconductor manufacturing technologies to improve yield. A pre-silicon diagnosis simulation is essential to avoid low diagnosis resolution during volume production. Like fault simulation to identify test quality problems, diagnosis simulation identifies diagnosis quality problems. The issues can be classified into three areas: (1) Failing data collected from the automatic test equipment (ATE) is insufficient for diagnosis. To fix this problem, ATE needs to use larger fail data storage; (2) Production scan test patterns are not adequate for diagnosis, such that better test pattern selection or new diagnosis-specific test patterns should be used; (3) Designs are not friendly for diagnosis, so design-for-diagnosis enhancements should be used.A fault dictionary with complete failing bit information on all faults can be used for diagnosis simulation. However, a fault dictionary requires lots of memory to store the failing bits and a long time to query the dictionary. We propose using an Asymmetric Partition Tree (APT) to extract the information stored in a fault dictionary for diagnosis simulation usage. APT can be used to calculate diagnosis coverage and analyze the impact of ATE failed data storage limit on diagnosis coverage with and without ATE pin-based storage limit. The information stored in APT also benefits other applications, such as diagnosis pattern sampling. APT can be proven to have the minimum tree size for diagnosis simulation.