Advanced electronic design automation (EDA) tools, with their simulation, modeling, design rule checking, and optical proximity correction capabilities, have facilitated the improvement of first pass wafer yields. While the data produced by these tools may have been processed for optimal wafer manufacturing, it is possible for the same data to be far from ideal for photomask manufacturing, particularly at lithography and inspection stages, resulting in production delays and increased costs. The same EDA tools used to produce the data can be used to detect potential problems for photomask manufacturing in the data.
In the previous paper, it was shown how photomask MRC is used to uncover data related problems prior to automated defect inspection. It was demonstrated how jobs which are likely to have problems at inspection could be identified and separated from those which are not. The use of photomask MRC in production was shown to reduce time lost to aborted runs and troubleshooting due to data issues.
In this paper, the effectiveness of this photomask MRC program in a high volume photomask factory over the course of a year as applied to more than ten thousand jobs will be shown. Statistics on the results of the MRC runs will be presented along with the associated impact to the automated defect inspection process. Common design problems will be shown as well as their impact to mask manufacturing throughput and productivity. Finally, solutions to the most common and most severe problems will be offered and discussed.