Defect control using an automatic killer defect selection method
In LSI mass production lines, it is very important to improve the detection accuracy of fatal excursions in defect trend charts in order to ramp up quickly and maintain good yield. It is for this reason that killer defects which could cause device faults should be monitored. We have developed an automatic killer defect selection (AKIDS) method to assist and improve the accuracy of this process. This method judges defects as killer or nonkiller by comparing defect sizes against killer defect judgement criteria. We applied AKIDS in the defect QC and sampling review processes, and found it to be effective in both detecting fatal excursions and selecting good review points for identifying the source of device faults.
The semiconductor integrated circuit (IC) industry in Taiwan plays a significant role in the world, and accounts for a major market share of packaging and testing. Past studies have discussed job-releasing models for upstream IC manufacturing industries, but only few have discussed from an IC turnkey perspective for IC testing and assembly processes. This article proposes a job-releasing strategy for an IC turnkey factory that achieves the following objectives under limited resources: (1) increasing the overall equipment efficiency of IC test equipments, (2) reducing IC fabrication cycle time and (3) increasing on-time delivery rate. To balance capacity utilisation between IC assembly and testing operations, this study uses a genetic algorithm with workload regulation heuristics to find a good job-releasing plan. The proposed strategy is implemented in an IC turnkey factory and is found to outperform the conventional strategy.