Abstract: The present disclosure is directed to a method of tool matching that employs an auto-learning feedback loop to update a library of key parameters. According to the method, measurements are performed on a control wafer to collect a set of parameters associated with the process/analysis tool that is being matched. When deviated parameters correlate to a correctable tool condition (i.e. a tool matching event), the parameters are added to the library of key parameters. These key or critical parameters may be monitored on a more frequent basis to identify deviations that have a strong likelihood of matching with a correctable tool condition. The tool matching methodology advantageously allows for monitoring of an automatically updated list of key parameters instead of needing to look at the full set of parameters collected from a control wafer each time. As such, tool matching can be performed on a more frequent basis.

Abstract: The present disclosure is directed to a method of tool matching that employs an auto-learning feedback loop to update a library of key parameters. According to the method, measurements are performed on a control wafer to collect a set of parameters associated with the process/analysis tool that is being matched. When deviated parameters correlate to a correctable tool condition (i.e. a tool matching event), the parameters are added to the library of key parameters. These key or critical parameters may be monitored on a more frequent basis to identify deviations that have a strong likelihood of matching with a correctable tool condition. The tool matching methodology advantageously allows for monitoring of an automatically updated list of key parameters instead of needing to look at the full set of parameters collected from a control wafer each time. As such, tool matching can be performed on a more frequent basis.