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.

Abstract: A method and apparatus for modeling and cross correlation of design predicted criticalities include a feedback loop where information from the manufacturing process is provided to cross correlation engine for optimization of semiconductor manufacturing. The information may include parametric information, functional information, and hot spots determination. The sharing of information allows for design intent to be reflected in manufacturing metrology space; thus, allowing for more intelligent metrology and reduces cycle time.

Abstract: A method and apparatus for mask optimization is provided. Mask design and production is optimized by providing proper weighting parameters for critical features. The parameters may include information such as parametric information, functional information, and hot spots determination.

Abstract: A method and apparatus for process optimization is provided. Process optimization improves parametric and functional yield post mask manufacturing.

Abstract: A method and apparatus for modeling and cross correlation of design predicted criticalities include a feedback loop where information from the manufacturing process is provided to cross correlation engine for optimization of semiconductor manufacturing. The information may include parametric information, functional information, and hot spots determination. The sharing of information allows for design intent to be reflected in manufacturing metrology space; thus, allowing for more intelligent metrology and reduces cycle time.

Abstract: A method and apparatus for inspection optimization is provided. Inspection optimization improves the parametric and functional yield using optimized inspection lists for in-line semiconductor manufacturing metrology and inspection equipment.

Abstract: A method and apparatus for mask optimization is provided. Mask design and production is optimized by providing proper weighting parameters for critical features. The parameters may include information such as parametric information, functional information, and hot spots determination.

Abstract: A method and apparatus for process optimization is provided. Process optimization improves parametric and functional yield post mask manufacturing.

Abstract: A method and apparatus for modeling and cross correlation of design predicted criticalities include a feedback loop where information from the manufacturing process is provided to cross correlation engine for optimization of semiconductor manufacturing. The information may include parametric information, functional information, and hot spots determination. The sharing of information allows for design intent to be reflected in manufacturing metrology space; thus, allowing for more intelligent metrology and reduces cycle time.

Abstract: A method and apparatus for inspection optimization is provided. Inspection optimization improves the parametric and functional yield using optimized inspection lists for in-line semiconductor manufacturing metrology and inspection equipment.