Inline defect analysis for sampling and SPC

• US 8,799,831 B2
• Filed: 05/22/2008
• Issued: 08/05/2014
• Est. Priority Date: 05/24/2007
• Status: Active Grant

• Current Assignee: Applied Materials Incorporated
• Original Assignee: Applied Materials Incorporated
• Inventors: Nehmadi, Youval, Shimshi, Rinat, Svidenko, Vicky, Schwarm, Alexander T., Jawaharlal, Sundar
• Primary Examiner(s): Siek, Vuthe
• Assistant Examiner(s): Lin, Aric
• Application Number:
  US12/154,629
• Publication Number:
  US 20080295048A1
• Time in Patent Office: 2,266 Days
• Field of Search: 716 50- 56
• US Class Current: 716/52
• CPC Class Codes: G01R 31/318511 Wafer Test

Abstract

In one embodiment, an inline defect analysis method includes receiving geometric characteristics of individual defects and design data corresponding to the individual defects, determining which of the individual defects are likely to be nuisance defects using the geometric characteristics and the corresponding design data, and refraining from sampling the defects that are likely to be nuisance defects.

Claims

1. A computer-implemented method comprising:

receiving, with a system having an inline defect analyzer, geometric characteristics of individual defects and design data corresponding to the individual defects;

separating the individual defects into systematic defects and random defects;

calculating a criticality factor (CF) value for the individual random defects using the geometric characteristics and design data of the individual random defects;

refraining, with the system having the inline defect analyzer, from sampling the defects that are likely to be nuisance defects, wherein the individual defects are separated into systematic defects and random defects using design-based binning in which a bin size threshold is set for separating the systematic and random defects with the bin size threshold being based on a random probability of the individual defects to be located on a given structure.

2. The computer-implemented method of claim 1, further comprising:

determining, with the system having the inline defect analyzer, which of the individual random defects are characterized as being nuisance defects having a first range of CF values, regular defects having a second range of CF values, and critical defects having a third range of CF values using the CF values for the individual random defects and two predetermined thresholds for the CF values; and

determining which systematic defects are likely to be nuisance defects, wherein the nuisance defects have a low probability of affecting a functionality of a design, the regular defects have an increased probability of affecting the functionality of the design, and the critical defects have a high probability of affecting the functionality of the design.

3. The computer-implemented method of claim 2, wherein the random defects are grouped into design-based bins that are smaller than the bin size threshold and systematic defects are grouped into designed-based bins that are larger than the bin size threshold.

4. The computer-implemented method of claim 3, further comprising:

selecting critical defects with a CF value that exceeds the at least one predefined threshold for sampling.

5. The computer-implemented method of claim 3, wherein calculating the CF value comprises:

identifying an open area, wherein the open area is a total area of all locations inside a design layout area to cause an open failure, and identifying a short area, wherein the short area is a total area of all locations inside the design layout to cause a short failure; and

calculating a probability of the random defect to cause an open failure and a probability of the random defect to cause a short failure.

6. The computer-implemented method of claim 2, wherein determining which systematic defects are likely to be nuisance defects comprises:

comparing systematic defects assigned to a structural bin to historical data for defects of previous structural bins; and

determining whether the systematic defects assigned to the structural bin are systematic defects already known to be nuisance defects based on the historical data.

7. The computer-implemented method of claim 1, wherein the geometric characteristics of the individual defects include a size of the individual defect, a volume of the individual defect, and a location of the individual defect within a design.

8. The computer-implemented method of claim 1, wherein the design data includes at least one of:

a layout of a design on which the defect is found and routing information for the design on which the defect is found.

9. A computer-implemented system comprising:

a design data database, to store design data for individual defects located within a design, wherein the design data includes a layout of the design on which the defect is found and routing information for the design on which the defect is found; and

an inline defect analyzer, coupled to the design data database, to separate the individual defects into systematic defects and random defects;

to calculate a criticality factor (CF) value for the random defects using the geometric characteristics and design data of the random defects, wherein the individual defects are separated into systematic defects and random defects using design-based binning in which a bin size threshold is set for separating the systematic and random defects with the bin size threshold being based on a random probability of the individual defects to be located on a given structure.

10. The computer-implemented system of claim 9, further comprising:

a defect reporter, to provide geometric characteristics of the individual defects, wherein the geometric characteristics include at least one of;

a size of the individual defect, a volume of the individual defect, and a location of the individual defect within a design.

11. The computer-implemented system of claim 9, further comprising:

a defect review tool, to refrain from sampling the defects likely to be nuisance defects.

12. The computer-implemented system of claim 9, wherein the inline defect analyzer comprises:

a nuisance defect detector, to determine which of the individual defects are likely to be nuisance defects; and

a defect review selector, to select the defects likely to be nuisance.

13. The computer-implemented system of claim 12, wherein the nuisance defect detector determines which defects are likely to be nuisance defects by:

separating the individual defects into systematic defects and random defects;

determining which random defects are likely to be nuisance defects; and

determining which systematic defects are likely to be nuisance defects.

14. The computer-implemented system of claim 13, wherein the nuisance defect detector includes a defect separator to separate the individual defects into systematic defects and random defects using design-based binning.

15. The computer-implemented system of claim 13, wherein the nuisance defect detector includes the criticality factor calculator to calculate a criticality factor (CF) value for the individual random defects using the geometric characteristics and design data of the individual random defects to determine which random defects are likely to be nuisance defects.

16. The computer-implemented system of claim 15, wherein the defect review selector selects random defects likely to be nuisance defects by comparing the CF value for the individual random defects to a predefined threshold.

17. The computer-implemented system of claim 15, wherein the criticality factor calculator calculates the CF value by:

identifying an open area, wherein the open area is a total area of all locations inside a design layout area to cause an open failure, and identifying a short area, wherein the short area is a total area of all locations inside the design layout to cause a short failure; and

calculating a probability of the random defect to cause an open failure and a probability of the defect to cause a short failure.

18. The computer-implemented system of claim 13, wherein the nuisance defect detector includes a defect comparator to determine which systematic defects are likely to be nuisance defects by:

comparing systematic defects assigned to a structural bin to historical data for defects of previous structural bins; and

determining whether the systematic defects assigned to the structural bin are systematic defects already known to be nuisance defects.

19. A computer-implemented apparatus comprising:

means for receiving geometric characteristics of individual defects and design data corresponding to the individual defects;

means for separating the individual defects into systematic defects and random defects;

means for calculating a criticality factor (CF) value for the individual random defects using the geometric characteristics and design data of the individual random defects;

and

means for refraining from sampling the defects that are likely to be nuisance defects, wherein the individual defects are separated into systematic defects and random defects using design-based binning in which a bin size threshold is set for separating the systematic and random defects with the bin size threshold being based on a random probability of the individual defects to be located on a given structure.

20. The computer-implemented apparatus of claim 19, further comprising:

means for determining which of the individual random defects are characterized as being nuisance defects having a first range of CF values, regular defects having a second range of CF values, and critical defects having a third range of CF values using the CF values for the individual random defects and two predetermined thresholds for the CF values;

means for determining which systematic defects are likely to be nuisance defects, wherein the nuisance defects have a low probability of affecting a functionality of a design, the regular defects have an increased probability of affecting the functionality of the design, and the critical defects have a high probability of affecting the functionality of the design.

21. The computer-implemented apparatus of claim 20, wherein the means for determining which random defects are likely to be nuisance defects, regular defects, and critical defects comprises:

means for comparing the CF value for the individual random defects to at least one predefined threshold.

22. The computer-implemented apparatus of claim 20, wherein the means for determining which systematic defects are likely to be nuisance defects is by:

means for comparing systematic defects assigned to a structural bin to historical data for defects of previous structural bins; and

means for determining whether the systematic defects assigned to the structural bin are systematic defects already known to be nuisance defects based on the historical data.

23. A computer readable non-transitory storage medium, comprising executable instructions which when executed on a processing system cause the processing system to perform a method comprising:

receiving geometric characteristics of individual defects and design data corresponding to the individual defects;

separating the individual defects into systematic defects and random defects;

calculating a criticality factor (CF) value for the individual random defects using the geometric characteristics and design data of the individual random defects;

and

refraining, with the system having the inline defect analyzer, from sampling the defects that are likely to be nuisance defects, wherein the individual defects are separated into systematic defects and random defects using design-based binning in which a bin size threshold is set for separating the systematic and random defects with the bin size threshold being based on a random probability of the individual defects to be located on a given structure.

24. The computer readable non-transitory storage medium of claim 23, further comprising:

determining which systematic defects are likely to be nuisance defects, wherein the nuisance defects have a low probability of affecting a functionality of a design, the regular defects have an increased probability of affecting the functionality of the design, and the critical defects have a high probability of affecting the functionality of the design.