On-the-fly automatic defect classification for substrates using signal attributes

• US 6,256,093 B1
• Filed: 06/25/1998
• Issued: 07/03/2001
• Est. Priority Date: 06/25/1998
• Status: Expired due to Term

• Current Assignee: Applied Materials Incorporated
• Original Assignee: Applied Materials Incorporated
• Inventors: Holcman, Ido, Mikolinsky, Vladimir, Ravid, Erez
• Primary Examiner(s): Pham, Hoa Q.
• Application Number:
  US09/104,226
• Time in Patent Office: 1,104 Days
• Field of Search: 356/237.1, 356/237.2, 356/237.4, 356/237.3, 356/237.5, 250/559.42, 250/559.48, 382/149, 382/159, 348/125, 348/126
• US Class Current: 356/237.2
• CPC Class Codes: G01N 21/94 Investigating contamination, e.g. dust G01N21/85 takes precedence
• CPC Class Codes: G01N 21/9501 Semiconductor wafers manufacturing processes per se of semiconductor devices implementing a measuring step H01L22/10

Abstract

A system for on-the-fly automatic defect classification (ADC) in a scanned wafer. The system includes a light source illuminating the scanned wafer so as to generate an illuminating spot incident on the wafer. Sensor collecting light scattered from the spot by the at least two spaced apart detectors, and processor analyzing the collected light so as to detect defects in the wafer and classifying the defects into distinct defect types.

Claims

1. A method for on-the-fly automatic defect classification (ADC) in a scanned substrate, comprising:

(a) providing at least two spaced apart detectors;

(b) illuminating the scanned substrate so as to generate an illuminating spot incident on the substrate;

(c) collecting light scattered from the spot by at least two spaced apart detectors;

(d) analyzing said collected light so as to detect defects in said substrate; and

(e) classifying said defects into distinct defect types by analyzing scattered light volume attribute and at least one other attribute of said collected light;

wherein said step for analysis for classifying defects utilizing scattered light attributes includes at least one of the following attributes;

scattered light intensity, scattered light linearity and scattered light asymmetry.

2. A system for on-the-fly automatic defect classification (ADC) in a inspected substrate, comprising:

(a) light source illuminating the inspected substrate;

(b) at least two spaced apart detectors situated to receive light scattered from the substrate and generating corresponding signals;

(c) a processor analyzing said signals so as to detect defects in said substrate; and

(d) a defect classifier analyzing scattered light volume attribute and at least one other attribute of said signals and classifying said defects into distinct defect types;

wherein said attributes correspond to at least one of the following attributes;

scattered light intensity, scattered light linearity and reflected light asymmetry.

3. The system of claim 2, wherein said intensity and volume attributes are utilized for defining a first threshold;

said classifier classifying said defects into particle type or pattern type per said first threshold.

4. The system of claim 3, wherein said intensity and volume attributes are further utilized for defining a second threshold;

said classifier of utilizing said second threshold for classifying said pattern defects into pattern type defect or scratch type defect.

5. The system of claim 3, wherein said intensity and volume attributes are further utilized for defining a second threshold;

said classifier utilizing said second threshold for classifying said particle defects into big particle type or scratch\particle type defect.

6. The system of claim 2, wherein said classifier utilizes said asymmetry and linearity attributes for classifying pattern type defect into scratch type defect or pattern type defect;

said classifier further utilizes said asymmetry and linearity attributes for classifying particle type defect into particle type or scratch type defect.

7. A method for on-the-fly automatic defect classification (ADC) in a scanned substrate, comprising:

(a) providing at least two spaced apart detectors;

(b) illuminating the scanned substrate so as to generate an illuminating spot incident on the substrate;

(c) collecting light scattered from the spot by the at least two spaced apart detectors;

(d) analyzing said collected light so as to detect defects in said substrate and classifying said defects into distinct defect types; and

(e) classifying said defects into defect types by analyzing a scattered light volume attribute and at least one other attribute of said collecting light.

8. The method of claim 7, wherein said step (a) comprises providing at least four spaced apart dark field detectors.

9. The method of claim 8, wherein said step (a) comprises arranging said detectors such that each detector faces another detector located substantially opposite thereto.

10. The method of claim 8, wherein said step (a) comprises arranging said detectors in a substantially uniform distribution around the scanned substrate.

11. The method of claim 8, further comprising the steps of providing at least one additional bright field detector;

reflected light being collected by said additional at least one bright field detector according to step (c) and said analysis, stipulated in said step (d), being applied to the reflected light collected by said additional at least one detector.

12. The method according to claim 8, wherein said step of classifying includes at least two of the following types:

small particle, big particle;

small pattern, big pattern;

small scratch, big scratch.

13. The method of claim 7, wherein said step (a) comprises arranging said detectors such that the optical axis of each detector is at a grazing angle to the surface of the inspected substrate.

14. The method of claim 7, wherein said step (b) comprising illuminating the substrate such that said spot is incident on the substrate at a grazing angle to the substrate surface.

15. The method of claim 7, further comprising the steps of providing at least one additional bright field detector;

reflected light being collected by said additional at least one bright field detector according to step (c) and said analysis, stipulated in said step (d), being applied to the reflected light collected by said additional at least one detector.

16. The method of claim 7, wherein said dark field detectors are arranged symmetrically about at least one axis.

17. The method of claim 7, wherein said steps for analyzing and classifying defects utilizes the scattered light volume attribute and comprises at least one of the following attributes:

scattered light intensity, scattered light linearity and scattered light asymmetry.

18. The method according to claim 7, wherein said step of classifying includes at least two of the following types:

small particle, big particle;

small pattern, big pattern;

small scratch, big scratch.

19. A system for on-the-fly automatic defect classification (ADC) in an inspected substrate, comprising:

(a) light source illuminating the inspected substrate;

(b) at least two spaced apart detectors situated to receive light scattered from the substrate and generating corresponding signals;

(c) a processor analyzing said signals so as to detect defects in said substrate; and

(d) a defect classifier analyzing a scattered light volume attribute and at least one other attribute of said signals and classifying said defects into distinct defect types.

20. The system of claim 19, wherein said at least two spaced apart detectors are arranged symmetrically about at least one axis.

21. The system of claim 20, wherein said classifier classifies said defects into at least two of the following types:

small particle, big particle;

small pattern, big pattern;

small scratch, big scratch.

22. The system of claim 19, wherein said detectors are arranged such that each detector faces another detector located substantially opposite thereto.

23. The system of claim 19, wherein said detectors are arranged in a substantially uniform distribution around the scanned substrate.

24. The system of claim 19, wherein said detectors are arranged such that the optical axis of each detector is at a grazing angle to the surface of the inspected substrate.

25. The system of claim 19, wherein said light source illuminates the substrate at a grazing angle.

26. The system of claim 25, further comprising at least one bright field detector.

27. The system of claim 19, further comprising at least one bright field detector.

28. The system of claim 19, further comprising a first and a second thresholds, and wherein said defect classifier analyzes the signals with respect to said first threshold to differentiate between small and large defects, and analyzes the signals with respect to said second threshold to differentiate between pattern and particle defects.

29. The system of claim 19, wherein said at least one other attribute corresponds to at least one of the following attributes:

scattered light intensity, scattered light linearity and reflected light asymmetry.

30. The system of claim 29, wherein said intensity and volume attributes are utilized for defining a first threshold;

said classifier classifying said defects into particle type or pattern type per said first threshold.

31. The system of claim 30, wherein said intensity and volume attributes are further utilized for defining a second threshold;

said classifier utilizing said second threshold for classifying said pattern defects into pattern type defect or scratch type defect.

32. The system of claim 30, wherein said intensity and volume attributes are further utilized for defining a second threshold;

said classifier utilizing said second threshold for classifying said particle defects into big particle type or scratch\particle type defect.

33. The system of claim 29, wherein said classifier utilizes said asymmetry and linearity attributes for classifying pattern type defect into scratch type defect or pattern type defect;

said classifier further utilizes said asymmetry and linearity attributes for classifying particle type defect into particle type or scratch type defect.

34. The system according to claim 19, wherein said classifier classifies said defects into at least two of the following types:

small particle, big particle;

small pattern, big pattern;

small scratch, big scratch.

35. In a system for on-the-fly automatic defect classification (ADC) in a scanned wafer, a processor programmed to perform the steps comprising:

receiving signals from at least two spaced apart detectors, which signals are indicative of light scattered from the wafer and detected by said detectors; and

analyzing a scattered light volume and at least one other attribute of said signals so as to detect defects in said wafer and classifying said defects into distinct defect types.

36. An inspection system for inspecting substrates for defects, comprising:

a light source illuminating said substrate;

at least two detectors situated to detect light scattered from the substrate and generate at least a first and second inspection signals;

a comparator receiving and comparing said at least first and second inspection signals to at least a first and second reference signals, respectively, to detect defects in said substrate and identify defect locations;

a classifier receiving said at least said first and second inspection signals and analyzing a scattered light volume and at least one other attribute of said at least first and second inspection signals corresponding to said defect locations, and classifying said defects according to said attributes.

37. The inspection system of claim 36, wherein said at least one other attribute is selected from:

intensity, linearity, and asymmetry.

38. The inspection system of claim 36, wherein intensity is obtained by summing intensities of said at least first and second inspection signals.

39. The inspection system of claim 36, wherein the scattered light volume is obtained by summing a number of pixels of said at least first and second inspection signals which correspond to a defect location.

40. The inspection system of claim 36, wherein linearity is obtained by determining alignment of pixels in said at least first and second inspection signals corresponding to defect locations.

41. The inspection system of claim 36, wherein asymmetry is obtained by comparing intensity levels of said at least first and second inspection signals corresponding to defect locations.