Criticality analysis augmented process window qualification sampling

• US 10,503,078 B2
• Filed: 02/23/2018
• Issued: 12/10/2019
• Est. Priority Date: 09/01/2017
• Status: Active Grant

• Current Assignee: KLA-Tencor Corporation (KLA Corporation)
• Original Assignee: KLA-Tencor Corporation (KLA Corporation)
• Inventors: Saraswatula, Jagdish Chandra, Banerjee, Saibal, Kulkarni, Ashok
• Primary Examiner(s): Chawan, Sheela C
• Application Number:
  US15/903,841
• Publication Number:
  US 20190072858A1
• Time in Patent Office: 655 Days
• Field of Search: 382100, 382141, 382145, 382149, 382168, 382170, 382276, 382286, 382291, 382305, 702 81, 702 33, 702 35, 702121, 702181, 702 82, 702 83, 702127, 702182, 702183, 702185, 700 90, 700 95, 700108, 700109, 700110
• CPC Class Codes: G01N 21/95607 using a comparative method
• CPC Class Codes: G03F 1/84 Inspecting
• CPC Class Codes: G03F 7/7065 Defects, e.g. optical inspection of patterned layer for defects
• CPC Class Codes: G03F 7/70666 Aerial image, i.e. measuring the image of the patterned exposure light at the image plane of the projection system
• CPC Class Codes: H01L 22/12 for structural parameters, e.g. thickness, line width, refractive index, temperature, warp, bond strength, defects, optical inspection, electrical measurement of structural dimensions, metallurgic measurement of diffusions electrical measurement of diffusions H01L22/14

Abstract

Techniques are provided that can select defects based on criticality of design pattern as well as defect attributes for process window qualification (PWQ). Defects are sorted into categories based on process conditions and similarity of design. Shape based grouping can be performed on the random defects. Highest design based grouping scores can be assigned to the bins, which are then sorted. Particular defects can be selected from the bins. These defects may be reviewed.

Claims

1. A method comprising:

sorting, using a processor, defects from a plurality of design based grouping bins into a plurality of categories based on a plurality of process conditions;

sorting, using the processor, the defects based on similarity of design into a plurality of bins, wherein each of the bins includes at least one of the design based grouping bins;

selecting, using the processor, a random defect from the defects in each of the design based grouping bins;

performing, using the processor, shape based grouping on each of the random defects;

for each of the bins, selecting, using the processor, one of the design based grouping bins with a score on an end of a range of scores after the shape based grouping, wherein the score is a highest score or a lowest score;

assigning, using the processor, the respective score that was selected to each of the bins;

sorting, using the processor, the bins in order by the respective scores; and

selecting, using the processor, one of the defects with a highest defect attribute value from each of the bins for each of the plurality of process conditions.

2. The method of claim 1, further comprising performing a review of a wafer using a scanning electron microscope after selecting the defects with the highest defect attribute value.

3. The method of claim 1, further comprising:

re-ordering, using the processor, the defects, the bins, and the design based grouping bins; and

repeating assigning the respective score, sorting of the bins in order starting with the score, and selecting one of the defects with the highest defect attribute value.

4. The method of claim 3, further comprising performing a review of a wafer using a scanning electron microscope after selecting the defects with the highest defect attribute value.

5. The method of claim 1, wherein the plurality of categories includes four of the categories.

6. The method of claim 1, wherein the plurality of process conditions include focus and exposure.

7. The method of claim 1, wherein sorting the defects based on similarity of the design uses a bin merge algorithm.

8. The method of claim 1, wherein prior to sorting the defects from the plurality of design based grouping bins the method further comprises:

grouping, using the processor, the defects into the design based grouping bins;

consolidating, using the processor, the design based grouping bins into the bins;

grouping, using the processor, the bins based on the plurality of process conditions; and

determining, using the processor, dies that indicate an inflection in defect count.

9. A system comprising:

a processor in electronic communication with an electronic data storage unit and a wafer inspection tool, wherein the processor is configured to;

sort defects from a plurality of design based grouping bins into a plurality of categories based on a plurality of process conditions;

sort the defects based on similarity of design into a plurality of bins, wherein each of the bins includes at least one of the design based grouping bins;

select a random defect from the defects in each of the design based grouping bins;

perform shape based grouping on each of the random defects;

for each of the bins, select one of the design based grouping bins with a score on an end of a range of scores after the shape based grouping, wherein the score is a highest score or a lowest score;

assign the respective score that was selected to each of the bins;

sort the bins in order by the respective scores; and

select one of the defects with a highest defect attribute value from each of the bins for each of the plurality of process conditions.

10. The system of claim 9, wherein the wafer inspection tool is a scanning electron microscope.

11. The system of claim 9, wherein the processor is further configured to:

re-order the defects, the bins, and the design based grouping bins; and

repeat assigning the respective score, sorting of the bins in order starting with the score, and selecting one of the defects with the highest defect attribute value.

12. The system of claim 9, wherein prior to sorting the defects from the plurality of design based grouping bins the processor is further configured to:

group the defects into the design based grouping bins;

consolidate the design based grouping bins into the bins;

group the bins based on the plurality of process conditions; and

determine dies that indicate an inflection in defect count.

13. A non-transitory computer-readable storage medium, comprising one or more programs for executing the following steps on one or more computing devices:

sorting defects from a plurality of design based grouping bins into a plurality of categories based on a plurality of process conditions;

sorting the defects based on similarity of design into a plurality of bins using a bin merge algorithm, wherein each of the bins includes at least one of the design based grouping bins;

selecting a random defect from the defects in each of the design based grouping bins;

performing shape based grouping on each of the random defects;

for each of the bins, selecting one of the design based grouping bins with a score on an end of a range of scores after the shape based grouping, wherein the score is a highest score or a lowest score;

assigning the respective score that was selected to each of the bins;

sorting the bins in order by the respective scores; and

selecting one of the defects with a highest defect attribute value from each of the bins for each of the plurality of process conditions.

14. The non-transitory computer-readable storage medium of claim 13, wherein the steps further include:

re-ordering the defects, the bins, and the design based grouping bins; and

repeating assigning the respective score, sorting of the bins in order starting with the score, and selecting one of the defects with the highest defect attribute value.

15. The non-transitory computer-readable storage medium of claim 13, wherein the plurality of categories includes four of the categories.

16. The non-transitory computer-readable storage medium of claim 13, wherein the plurality of process conditions include focus and exposure.

17. The non-transitory computer-readable storage medium of claim 13, wherein sorting the defects based on similarity of the design uses a bin merge algorithm.

18. The non-transitory computer-readable storage medium of claim 13, wherein prior to sorting the defects from the plurality of design based grouping bins the steps further include:

grouping the defects into the design based grouping bins;

consolidating the design based grouping bins into the bins;

grouping the bins based on the plurality of process conditions; and

determining dies that indicate an inflection in defect count.