To accurately control critical dimension (CD) metrology in a standard real-time solution across a multi-site operation there is a need to collect measure-to-measure and day-to-day variation across all sites. Each individual site's needs, technologies, and resources can affect the final solution. A preferred statistical process control (SPC) solution for testing measure-to-measure and day-to-day variation is the traditional Mean and Range chart. However, replicating the full measurement process needed for the Mean and Range chart in real-time can strain resources. To solve this problem, an initially proposed measurement methodology was to isolate a point of interest, measure the CD feature n number of times, and continue to the next feature; however, the interdependencies in measure-to-measure variation caused by this methodology resulted in exceedingly narrow control limits.
This paper explains how traditional solutions to narrow control limits are statistically problematic and explores the approach of computing control limits for the Mean chart utilizing the moving range of sample means to estimate sigma instead of the traditional range method. Tool monitoring data from multiple CD metrology tools are reported and compared against control limits calculated by the traditional approach, engineering limits, and the suggested approach. The data indicate that the suggested approach is the most accurate of the three solutions.