Method to calibrate, predict, and control stochastic defects in EUV lithography
An initial probability of occurrence of a stochastic defect over an inspection area of a workpiece is received. All locations of the stochastic defects are sorted by the initial probability of occurrence. A cumulative expected defect count is determined and the cumulative expected defect count is normalized to be a fraction of a total expected defect count. A number of defect locations is determined to capture potential stochastic defects above a threshold of total stochastic defects.
1 . A method comprising:
receiving, at a processor, an initial probability of occurrence of a stochastic defect over an inspection area of a workpiece, wherein the initial probability of occurrence of a stochastic defect is generated using a model;
sorting, using the processor, all locations of the stochastic defects by the initial probability of occurrence;
determining, using the processor, a cumulative expected defect count;
normalizing, using the processor, the cumulative expected defect count to be a fraction of a total expected defect count thereby determining a normalized cumulative expected defect count; and
determining, using the processor, a number of defect locations to capture potential stochastic defects above a threshold of total stochastic defects.
2 . The method of claim 1 , further comprising associating the normalized cumulative expected defect count with a total count for the inspection area.
3 . The method of claim 1 , further comprising associating the normalized cumulative expected defect count with the inspection area.
4 . The method of claim 1 , further comprising determining the initial probability of occurrence of a stochastic defect with the model using the processor.
5 . The method of claim 1 , further comprising:
selecting a subset of locations above the threshold using the processor;
grouping the subset of locations by pattern shape using the processor thereby forming pattern shape groups; and
sorting the pattern shape groups by an expected defect count using the processor;
wherein the cumulative expected defect count is based on the pattern shape groups.
6 . The method of claim 1 , further comprising selecting a subset of locations using the processor by probability, wherein the cumulative expected defect count is based on the subset of locations, and wherein the threshold is a fraction of defectivity.
7 . The method of claim 1 , further comprising selecting a subset of locations using the processor by probability, wherein the cumulative expected defect count is based on the subset of locations, and wherein the threshold is a number of locations.
8 . The method of claim 1 , further comprising selecting a subset of locations using the processor by probability, wherein the cumulative expected defect count is based on the subset of locations, and wherein the threshold is a probability value.
9 . The method of claim 1 , further comprising:
selecting, using the processor, one or more of the defect locations above the threshold thereby generating selected defect locations;
grouping, using the processor, the selected defect locations by pattern shapes on the workpiece;
determining, using the processor, pattern sensitivity based on at least one geometric distance in the pattern shapes; and
determining, using the processor, an expected defect count for each of the pattern shapes.
10 . The method of claim 9 , further comprising inspecting a subset of the pattern shapes.
11 . The method of claim 1 , further comprising:
selecting, using the processor, one or more of the defect locations above the threshold thereby generating selected defect locations;
grouping, using the processor, the selected defect locations by pattern shapes on the workpiece;
determining, using the processor, pattern sensitivity based on at least one topological description in the pattern shapes; and
determining, using the processor, an expected defect count for each of the pattern shapes.
12 . The method of claim 11 , further comprising inspecting a subset of the pattern shapes.
13 . A non-transitory computer readable medium storing a program configured to instruct a processor to execute the method of claim 1 .
14 . A system comprising:
an inspection tool configured to image a workpiece; and
a processor in electronic communication with the metrology tool, wherein the processor is configured to:
receive an initial probability of occurrence of a stochastic defect over an inspection area of a workpiece, wherein the initial probability of occurrence of a stochastic defect is generated using a model;
sort all locations of the stochastic defects by the initial probability of occurrence;
determine a cumulative expected defect count;
normalize the cumulative expected defect count to be a fraction of a total expected defect count thereby determining a normalized cumulative expected defect count; and
determine a number of defect locations to capture potential stochastic defects above a threshold of total stochastic defects.
15 . The system of claim 14 , wherein the processor is further configured to associate the normalized cumulative expected defect count with a total count for the inspection area or with the inspection area.
16 . The system of claim 14 , wherein the processor is further configured to determine the initial probability of occurrence of a stochastic defect with the model.
17 . The system of claim 14 , wherein the processor is further configured to:
select a subset of locations above the threshold;
group the subset of locations by pattern shape thereby forming pattern shape groups; and
sort the pattern shape groups by an expected defect count;
wherein the cumulative expected defect count is based on the pattern shape groups.
18 . The system of claim 14 , wherein the processor is further configured to select a subset of locations using the processor by probability, wherein the cumulative expected defect count is based on the subset of locations, and wherein the threshold is a fraction of defectivity, a number of locations, or a probability value.
19 . The system of claim 14 , wherein the processor is further configured to:
select one or more of the defect locations above the threshold thereby generating selected defect locations;
group the selected defect locations by pattern shapes on the workpiece;
determine pattern sensitivity based on at least one geometric distance in the pattern shapes; and
determine an expected defect count for each of the pattern shapes.
20 . The system of claim 14 , wherein the processor is further configured to:
select one or more of the defect locations above the threshold thereby generating selected defect locations;
group the selected defect locations by pattern shapes on the workpiece;
determine pattern sensitivity based on at least one topological description in the pattern shapes; and
determine an expected defect count for each of the pattern shapes.