IPC분류정보
국가/구분 |
United States(US) Patent
등록
|
국제특허분류(IPC7판) |
|
출원번호 |
US-0791026
(2004-03-01)
|
발명자
/ 주소 |
- Chen,Wayne
- Zeng,Andrew
- Akbulut,Mustafa
|
출원인 / 주소 |
|
대리인 / 주소 |
|
인용정보 |
피인용 횟수 :
5 인용 특허 :
11 |
초록
▼
Two or more defect maps may be provided for the same sample surface at different detection sensitivities and/or processing thresholds. The defect maps may then be compared for better characterization of the anomalies as scratches, area anomalies or point anomalies. This can be done without conceali
Two or more defect maps may be provided for the same sample surface at different detection sensitivities and/or processing thresholds. The defect maps may then be compared for better characterization of the anomalies as scratches, area anomalies or point anomalies. This can be done without concealing the more significant and larger size defects amongst numerous small and immaterial defects. One or more defect maps can be used to report the anomalies with classified information; the results from this map(s) can be used to monitor the process conditions to obtain better yield.
대표청구항
▼
What is claimed is: 1. A method for detecting and classifying anomalies of a surface of a sample of a material suitable for use as a substrate for storage, display or electronic devices, comprising: supplying radiation to an area of the surface; detecting radiation from the anomalies associated wit
What is claimed is: 1. A method for detecting and classifying anomalies of a surface of a sample of a material suitable for use as a substrate for storage, display or electronic devices, comprising: supplying radiation to an area of the surface; detecting radiation from the anomalies associated with the area of the surface to provide an output corresponding to the area by means of a detector; and analyzing the detector output for anomalies and classifying the anomalies; wherein the analyzing uses more than one threshold to analyze the detector output and to arrive at at least one classification of the anomalies, said analyzing and classifying comprising processing the output with a first threshold, and classifying the anomalies in a first classification and analyzing the output with a second threshold different from the first threshold, said classifying including applying algorithm(s) to test relationship between anomalies, if any, wherein the output is analyzed with a second threshold without applying the algorithm(s) to test relationship between anomalies. 2. The method of claim 1, said analyzing and classifying comprising using the first classification and the output analyzed with a second threshold to characterize anomalies in the output analyzed with a second threshold. 3. The method of claim 1, said analyzing and classifying comprising characterizing anomalies in the at least one classification as elongated anomalies, area anomalies or point anomalies. 4. The method of claim 3, wherein the elongated anomalies include macroscratches and-microscratches. 5. The method of claim 1, wherein the first threshold is lower than the second threshold, wherein one or more anomalies are classified as scratches when they are classified as scratches at the first threshold whether or not they are classified as scratches at the second threshold. 6. The method of claim 1, wherein the analyzing is performed by means of a processing system and wherein a first threshold used in analyzing anomalies is the lowest practical threshold of the system. 7. The method of claim 1, further comprising displaying only anomalies of sizes that result in detector outputs that exceed the second threshold. 8. The method of claim 1, further comprising displaying only anomalies of sizes that exceed a predetermined value. 9. The method of claim 1, wherein said classifying classifies the anomalies by means of their distribution over the surface. 10. The method of claim 9, wherein said classifying classifies the anomalies detected into two or more of the following three categories: elongated group of anomalies, area group of anomalies or point group of anomalies. 11. The method of claim 10, wherein the elongated group of anomalies comprise macroscratches and microscratches. 12. The method of claim 9, wherein said classifying comprises determining distances between the anomalies detected and grouping into groups the anomalies detected that are within a predetermined distance from one another. 13. The method of claim 12, wherein said classifying classifies the anomalies detected by grouping anomalies into a group only when the number of anomalies in the group exceeds a preset value. 14. The method of claim 12, wherein said determining also determines length and width of a boundary on the surface enclosing at least one group of anomalies detected, and said classifying classifies the anomalies in said at least one group as those forming an elongated group when ratio of the length to the width of the boundary exceeds a preset value, and classifies the anomalies in said at least one group as those forming an area group when ratio of the length to the width of the boundary does not exceed a preset value. 15. The method of claim 14, wherein said classifying classifies the anomalies in an elongated group as those forming a microscratch when the length of the boundary is greater than a preset value. 16. The method of claim 9, wherein said classifying classifies the anomalies in a group as point group of anomalies when the number of anomalies in the group does not exceed a preset value. 17. The method of claim 1, wherein said supplying comprises directing a beam of radiation along a direction to the surface. 18. The method of claim 17, wherein said detecting detects radiation scattered by the anomalies. 19. The method of claim 18, wherein said detecting detects radiation scattered by the anomalies along a direction away from a specular reflection direction of the beam by the surface. 20. The method of claim 19, further comprising controlling a sample processing parameter in response to the at least one classification. 21. A method for detecting and classifying anomalies of a surface of a sample of a material suitable for use as a substrate for storage, display or electronic devices, comprising: supplying radiation to an area of the surface; detecting radiation from the anomalies associated with the area of the surface to provide an output corresponding to the area by means of a detector; analyzing the detector output for anomalies and classifying the anomalies; and providing classification information concerning classification of anomalies of the surface, said providing comprising processing the detector output with a first threshold, and classifying the anomalies in a first classification; wherein the analyzing and classifying analyzes the detector output and uses the classification information to arrive at at least one classification of the anomalies, said analyzing and classifying also analyzing the output with a second threshold different from the first threshold, said providing including applying algorithm(s) to test relationship between the anomalies, if any, wherein said analyzing and classifying analyze the detector output with a second threshold without applying the algorithm(s) to test relationship between anomalies. 22. The method of claim 21, said analyzing and classifying comprising using the first classification and the output analyzed with a second threshold to characterize anomalies in the detector output analyzed with a second threshold. 23. A method for detecting and classifying anomalies of a surface of a sample of a material suitable for use as a substrate for storage, display or electronic devices, comprising: supplying a beam of radiation to an area of the surface; causing relative motion between the surface and the beam so that the beam traces a spiral path on the surface; detecting radiation from the anomalies associated with the area of the surface to provide an output corresponding to the area by means of a detector; and analyzing the detector output for anomalies and classifying the anomalies; wherein the analyzing uses more than one threshold to analyze the detector output and to arrive at at least one classification of the anomalies, said analyzing and classifying comprising processing the output with a first threshold, and classifying the anomalies in a first classification and analyzing the output with a second threshold different from the first threshold, and wherein the processing of the output with the first threshold and the analyzing of the output with a second threshold are performed independently of one another. 24. The method of claim 23, said analyzing and classifying comprising using the first classification and the output analyzed with a second threshold to characterize anomalies in the output analyzed with a second threshold. 25. The method of claim 23, said analyzing and classifying comprising characterizing anomalies in the at least one classification as elongated anomalies, area anomalies or point anomalies. 26. The method of claim 25, wherein the elongated anomalies include macroscratches and-microscratches. 27. The method of claim 23, wherein the first threshold is lower than the second threshold, wherein one or more anomalies are classified as scratches when they are classified as scratches at the first threshold whether or not they are classified as scratches at the second threshold. 28. The method of claim 23, wherein the analyzing is performed by means of a processing system and wherein a first threshold used in analyzing anomalies is the lowest practical threshold of the system. 29. The method of claim 23, further comprising displaying only anomalies of sizes that result in detector outputs that exceed the second threshold. 30. The method of claim 23, further comprising displaying only anomalies of sizes that exceed a predetermined value. 31. The method of claim 23, wherein said supplying comprises directing a beam of radiation along a direction to the surface. 32. The method of claim 23, further comprising controlling a sample processing parameter in response to the at least one classification. 33. A method for detecting and classifying anomalies of a surface of a sample of a material suitable for use as a substrate for storage, display or electronic devices, comprising: supplying a beam of radiation to an area of the surface; causing relative motion between the surface and the beam so that the beam traces a spiral path on the surface; detecting radiation from the anomalies associated with the area of the surface to provide an output corresponding to the area by means of a detector; and analyzing the detector output for anomalies and classifying the anomalies; wherein the analyzing uses more than one threshold to analyze the detector output and to arrive at at least one classification of the anomalies, wherein said classifying classifies the anomalies by means of their distribution over the surface. 34. The method of claim 33, wherein said classifying classifies the anomalies detected into two or more of the following three categories: elongated group of anomalies, area group of anomalies or point group of anomalies. 35. The method of claim 34, wherein the elongated group of anomalies comprise macroscratches and microscratches. 36. The method of claim 33, wherein said classifying comprises determining distances between the anomalies detected and grouping into groups the anomalies detected that are within a predetermined distance from one another. 37. The method of claim 36, wherein said classifying classifies the anomalies detected by grouping anomalies into a group only when the number of anomalies in the group exceeds a preset value. 38. The method of claim 36, wherein said determining also determines length and width of a boundary on the surface enclosing at least one group of anomalies detected, and said classifying classifies the anomalies in said at least one group as those forming an elongated group when ratio of the length to the width of the boundary exceeds a preset value, and classifies the anomalies in said at least one group as those forming an area group when ratio of the length to the width of the boundary does not exceed a preset value. 39. The method of claim 38, wherein said classifying classifies the anomalies in an elongated group as those forming a microscratch when the length of the boundary is greater than a preset value. 40. The method of claim 33, wherein said classifying classifies the anomalies in a group as point group of anomalies when the number of anomalies in the group does not exceed a preset value. 41. The method of claim 31, wherein said detecting detects radiation scattered by the anomalies. 42. The method of claim 41, wherein said detecting detects radiation scattered by the anomalies along a direction away from a specular reflection direction of the beam by the surface. 43. A method for detecting and classifying anomalies of a surface of a sample of a material suitable for use as a substrate for storage, display or electronic devices, comprising: supplying a beam of radiation to an area of the surface; causing relative motion between the surface and the beam so that the beam traces a spiral path on the surface; detecting radiation from the anomalies associated with the area of the surface to provide an output corresponding to the area by means of a detector; analyzing the detector output for anomalies and classifying the anomalies; and providing classification information concerning classification of anomalies of the surface; wherein the analyzing and classifying analyzes the detector output and uses the classification information to arrive at at least one classification of the anomalies, said providing comprising processing the detector output with a first threshold, and classifying the anomalies in a first classification, and said analyzing and classifying analyzing the output with a second threshold different from the first threshold, said analyzing and classifying comprising using the first classification and the output analyzed with a second threshold to characterize anomalies in the detector output analyzed with a second threshold. 44. The method of claim 43, said providing including applying algorithm(s) to test relationship between the anomalies, if any, wherein said analyzing and classifying analyze the detector output with a second threshold without applying the algorithm(s) to test relationship between anomalies. 45. A method for detecting and classifying anomalies of a surface of a sample of a material suitable for use as a substrate for storage, display or electronic devices, comprising: supplying beam of radiation to an area of the surface; causing relative motion between the surface and the beam so that the beam traces paths on the surface having lengths that are smaller than dimensions of the surface, said paths substantially covering the entire surface; detecting radiation from the anomalies associated with the area of the surface to provide an output corresponding to the area by means of a detector; and analyzing the detector output for anomalies and classifying the anomalies; wherein the analyzing uses more than one threshold to analyze the detector output and to arrive at at least one classification of the anomalies, said analyzing and classifying comprising processing the output with a first threshold, and classifying the anomalies in a first classification and analyzing the output with a second threshold different from the first threshold and wherein the processing of the output with the first threshold and the analyzing of the output with a second threshold are performed independently of one another. 46. The method of claim 45, said analyzing and classifying comprising using the first classification and the output analyzed with a second threshold to characterize anomalies in the output analyzed with a second threshold. 47. The method of claim 45, said analyzing and classifying comprising characterizing anomalies in the at least one classification as elongated anomalies, area anomalies or point anomalies. 48. The method of claim 47, wherein the elongated anomalies include macroscratches and-microscratches. 49. The method of claim 45, wherein the first threshold is lower than the second threshold, wherein one or more anomalies are classified as scratches when they are classified as scratches at the first threshold whether or not they are classified as scratches at the second threshold. 50. The method of claim 45, wherein the analyzing is performed by means of a processing system and wherein a first threshold used in analyzing anomalies is the lowest practical threshold of the system. 51. The method of claim 45, further comprising displaying only anomalies of sizes that result in detector outputs that exceed the second threshold. 52. The method of claim 45, further comprising displaying only anomalies of sizes that exceed a predetermined value. 53. The method of claim 45, wherein said classifying classifies the anomalies by means of their distribution over the surface. 54. The method of claim 53, wherein said classifying classifies the anomalies detected into two or more of the following three categories: elongated group of anomalies, area group of anomalies or point group of anomalies. 55. The method of claim 53, wherein the elongated group of anomalies comprise macroscratches and microscratches. 56. The method of claim 53, wherein said classifying comprises determining distances between the anomalies detected and grouping into groups the anomalies detected that are within a predetermined distance from one another. 57. The method of claim 56, wherein said classifying classifies the anomalies detected by grouping anomalies into a group only when the number of anomalies in the group exceeds a preset value. 58. The method of claim 56, wherein said determining also determines length and width of a boundary on the surface enclosing at least one group of anomalies detected, and said classifying classifies the anomalies in said at least one group as those forming an elongated group when ratio of the length to the width of the boundary exceeds a preset value, and classifies the anomalies in said at least one group as those forming an area group when ratio of the length to the width of the boundary does not exceed a preset value. 59. The method of claim 58, wherein said classifying classifies the anomalies in an elongated group as those forming a microscratch when the length of the boundary is greater than a preset value. 60. The method of claim 53, wherein said classifying classifies the anomalies in a group as point group of anomalies when the number of anomalies in the group does not exceed a preset value. 61. The method of claim 45, wherein said supplying comprises directing a beam of radiation along a direction to the surface. 62. The method of claim 61, wherein said detecting detects radiation scattered by the anomalies. 63. The method of claim 62, wherein said detecting detects radiation scattered by the anomalies along a direction away from a specular reflection direction of the beam by the surface. 64. The method of claim 45, further comprising controlling a sample processing parameter in response to the at least one classification. 65. A method for detecting and classifying anomalies of a surface of a sample of a material suitable for use as a substrate for storage, display or electronic devices, comprising: supplying a beam of radiation to an area of the surface; causing relative motion between the surface and the beam so that the beam traces paths on the surface having lengths that are smaller than dimensions of the surface, said paths substantially covering the entire surface; detecting radiation from the anomalies associated with the area of the surface to provide an output corresponding to the area by means of a detector; analyzing the detector output for anomalies and classifying the anomalies; and providing classification information concerning classification of anomalies of the surface; wherein the analyzing and classifying analyzes the detector output and uses the classification information to arrive at at least one classification of the anomalies, said providing comprising processing the detector output with a first threshold, and classifying the anomalies in a first classification, and said analyzing and classifying analyzing the output with a second threshold different from the first threshold, applying algorithm(s) to test relationship between the anomalies, if any, wherein said analyzing and classifying analyze the detector output with a second threshold without applying the algorithm(s) to test relationship between anomalies. 66. The method of claim 65, said analyzing and classifying comprising using the first classification and the output analyzed with a second threshold to characterize anomalies in the detector output analyzed with a second threshold.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.