Auto-correlation of wafer characterization data and generation of composite wafer metrics during semiconductor device fabrication

A system, comprising:

• one or more characterization tools;
• and
• a controller, wherein the controller includes one or more processors and memory configured to store one or more sets of program instructions wherein the one or more sets of program instructions are configured to cause the one or more processors to:
• receive a plurality of patterned wafer geometry metrics for a wafer, wherein the plurality of patterned wafer geometry metrics includes at least two different patterned wafer geometry metrics;
• receive wafer characterization data for the wafer from one or more characterization tools;
• determine a correlation between the plurality of patterned wafer geometry metrics and the wafer characterization data with respect to die failure of the wafer;
• generate an automatic ranking of the plurality of patterned wafer geometry metrics based on the determined correlation via at least one machine learning algorithm;
• construct a composite metric model for the wafer from a subset of the plurality of patterned wafer geometry metrics based on the automatic ranking of the plurality of patterned wafer geometry metrics, wherein the subset of the plurality of patterned wafer geometry metrics includes a first metric with a highest ranking and at least a second metric with a second-highest ranking;
• generate one or more composite wafer metrics for the wafer from the composite metric model, wherein the one or more composite wafer metrics have a correlation to die failure of the wafer that is greater than the correlation to die failure of the wafer of the plurality of patterned wafer geometry metrics;
• generate a statistical process control output based on the one or more composite wafer metrics;
• and
• adjust one or more characteristics of one or more process tools based on the statistical process control output to improve one or more fabrication processes.

A method comprising:

• receiving a plurality of patterned wafer geometry metrics for a wafer, wherein the plurality of patterned wafer geometry metrics includes at least two different patterned wafer geometry metrics;
• receiving wafer characterization data for the wafer from one or more characterization tools;
• determining a correlation between the plurality of patterned wafer geometry metrics and the wafer characterization data with respect to die failure of the wafer;
• generating an automatic ranking of the plurality of patterned wafer geometry metrics based on the determined correlation via at least one machine learning algorithm;
• constructing a composite metric model for the wafer from a subset of the plurality of patterned wafer geometry metrics based on the automatic ranking of the plurality of patterned wafer geometry metrics, wherein the subset of the plurality of patterned wafer geometry metrics includes a first metric with a highest ranking and at least a second metric with a second-highest ranking;
• generating one or more composite wafer metrics for the wafer from the composite metric model, wherein the one or more composite wafer metrics have a correlation to die failure of the wafer that is greater than the correlation to die failure of the wafer of the plurality of patterned wafer geometry metrics;
• generating a statistical process control output based on the one or more composite wafer metrics;
• and
• adjusting one or more characteristics of one or more process tools based on the statistical process control output to improve one or more fabrication processes.