Abstract: The present invention relates generally to methods and apparatuses for test pattern selection for computational lithography model calibration. According to some aspects, the pattern selection algorithms of the present invention can be applied to any existing pool of candidate test patterns. According to some aspects, the present invention automatically selects those test patterns that are most effective in determining the optimal model parameter values from an existing pool of candidate test patterns, as opposed to designing optimal patterns. According to additional aspects, the selected set of test patterns according to the invention is able to excite all the known physics and chemistry in the model formulation, making sure that the wafer data for the test patterns can drive the model calibration to the optimal parameter values that realize the upper bound of prediction accuracy imposed by the model formulation.

Abstract: The present invention relates generally to methods and apparatuses for test pattern selection for computational lithography model calibration. According to some aspects, the pattern selection algorithms of the present invention can be applied to any existing pool of candidate test patterns. According to some aspects, the present invention automatically selects those test patterns that are most effective in determining the optimal model parameter values from an existing pool of candidate test patterns, as opposed to designing optimal patterns. According to additional aspects, the selected set of test patterns according to the invention is able to excite all the known physics and chemistry in the model formulation, making sure that the wafer data for the test patterns can drive the model calibration to the optimal parameter values that realize the upper bound of prediction accuracy imposed by the model formulation.

Abstract: Systems and methods for tuning photolithographic processes are described. A model of a target scanner is maintained defining sensitivity of the target scanner with reference to a set of tunable parameters. A differential model represents deviations of the target scanner from the reference. The target scanner may be tuned based on the settings of the reference scanner and the differential model. Performance of a family of related scanners may be characterized relative to the performance of a reference scanner. Differential models may include information such as parametric offsets and other differences that may be used to simulate the difference in imaging behavior.

Abstract: The present invention relates generally to methods and apparatuses for test pattern selection for computational lithography model calibration. According to some aspects, the pattern selection algorithms of the present invention can be applied to any existing pool of candidate test patterns. According to some aspects, the present invention automatically selects those test patterns that are most effective in determining the optimal model parameter values from an existing pool of candidate test patterns, as opposed to designing optimal patterns. According to additional aspects, the selected set of test patterns according to the invention is able to excite all the known physics and chemistry in the model formulation, making sure that the wafer data for the test patterns can drive the model calibration to the optimal parameter values that realize the upper bound of prediction accuracy imposed by the model formulation.

Abstract: Systems and methods for tuning photolithographic processes are described. A model of a target scanner is maintained defining sensitivity of the target scanner with reference to a set of tunable parameters. A differential model represents deviations of the target scanner from the reference. The target scanner may be tuned based on the settings of the reference scanner and the differential model. Performance of a family of related scanners may be characterized relative to the performance of a reference scanner. Differential models may include information such as parametric offsets and other differences that may be used to simulate the difference in imaging behavior.