Method to calibrate, predict, and control stochastic defects in euv lithography

Patent metadata

Field	Value
Publication number	US-2025104215-A1
Application number	US-202418894596-A
Country	US
Kind code	A1
Filing date	Sep 24, 2024
Priority date	Sep 26, 2023
Publication date	Mar 27, 2025
Grant date	—

Abstract

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.

First claim

What is claimed is:

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.

Assignees

• Kla Corp

Inventors

• Vukkadala Pradeep
• Zhang Cao
• Burov Anatoly
• Parsey Guy
• Ko Kyeongeun
• Bakarian Sergei G
• Krek Janez
• Bai Kunlun
• Higgins Craig
• Graves John S
• Smith Mark D
• Biafore John J

Classifications

• G06T7/0004 Primary

  Industrial image inspection · CPC title

• G06T2207/30242

  Counting objects in image · CPC title

• G06T2207/30148

  Semiconductor; IC; Wafer · CPC title

• G06T2207/20081

  Training; Learning · CPC title

• G06T2207/30164

  Workpiece; Machine component · CPC title