Method and apparatus for evaluating processing apparatus status and predicting processing result

• US 20050125090A1
• Filed: 11/10/2004
• Published: 06/09/2005
• Est. Priority Date: 05/16/2002
• Status: Active Grant

• Current Assignee: Tokyo Electron Limited
• Original Assignee: Tokyo Electron Limited
• Inventors: Sakano, Shinji, Harada, Satoshi
• Granted Patent: US 7,689,028 B2
• Time in Patent Office: Days
• US Class Current: 700/108
• CPC Class Codes: G05B 13/048 using a predictor
• CPC Class Codes: G05B 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
• CPC Class Codes: H01J 37/32862 In situ cleaning of vessels and/or internal parts

Abstract

A method for predicting a processing result includes a process of performing a principal component analysis on a plurality of detected data obtained during a first standard processing, to construct a principal component analysis model; a process of obtaining residuals of the principal component analysis model as first residuals; a process of performing a second standard processing; and a process of obtaining a plurality of detected data from a plurality of detectors during the second standard processing. And, the detected data obtained during the second standard processing are applied to the principal component analysis model, to obtain second residuals. The method further includes a process of weighting the second residuals based on weighting references, and constructing a new multivariate analysis model with use of the weighted second residuals; and a process of predicting a processing result of the second standard processing with use of the multivariate analysis model.

Claims

1. A method for predicting a processing result, comprising:

a process of performing a predetermined first standard processing on a plurality of objects to be processed with use of a first processing apparatus set in a certain condition;

a process of obtaining a plurality of detected data from a plurality of detectors provided at the first processing apparatus during the first standard processing;

a process of performing a certain multivariate analysis with use of the plurality of detected data obtained during the first standard processing, to set weighting references for the plurality of detected data;

a process of performing a second standard processing, which is similar to the first standard processing, on a plurality of objects to be processed, which are similar to said the plurality of objects to be processed, with use of the first processing apparatus in another condition or another second processing apparatus of the same kind;

a process of obtaining a plurality of detected data from the plurality of detectors provided at the first processing apparatus or from a plurality of detectors provided at the second processing apparatus during the second standard processing;

a process of weighting the plurality of detected data obtained during the second standard processing based on the weighting references, and applying the weighted plurality of detected data to the multivariate analysis; and

a process of predicting a processing result of the second standard processing based on a result of the application to the multivariate analysis.

2. The processing result prediction method of claim 1, wherein the weighting references are binary references indicating whether or not each of the detected data should be adopted.

3. A method for predicting a processing result, comprising:

a process of performing a predetermined first standard processing on a plurality of objects to be processed with use of a first processing apparatus set in a certain condition;

a process of obtaining a plurality of detected data from a plurality of detectors provided at the first processing apparatus during the first standard processing;

a process of performing a principal component analysis on the plurality of detected data obtained during the first standard processing, to construct a principal component analysis model;

a process of obtaining residuals of the principal component analysis model as first residuals;

a process of performing a second standard processing, which is similar to the first standard processing, on a plurality of objects to be processed, which are similar to said the plurality of objects to be processed, with use of the first processing apparatus in another condition or another second processing apparatus of the same kind;

a process of obtaining a plurality of detected data from the plurality of detectors provided at the first processing apparatus or from a plurality of detectors provided at the second processing apparatus during the second standard processing;

a process of applying the plurality of detected data obtained during the second standard processing to the principal component analysis model, to obtain second residuals;

a process of comparing the first residuals and the second residuals, to set weighting references for the respective second residuals;

a process of weighting the second residuals based on the weighting references, and constructing a new multivariate analysis model with use of the weighted second residuals; and

a process of predicting a processing result of the second standard processing with use of the multivariate analysis model.

4. The processing result prediction method of claim 3, wherein the weighting references are binary references indicating whether or not each of the second residuals should be adopted.

5. The processing result prediction method of claim 4, wherein the weighting references are references to adopt second residuals with small variations.

6. The processing result prediction method of claim 5, wherein the weighting references are references to determine second residuals with small variations based on the maximum value and the minimum value of the first residuals.

7. The processing result prediction method of claim 3, wherein the process of performing the principal component analysis on the plurality of detected data obtained during the first standard processing, to construct the principal component analysis model includes a process of obtaining a degree of principal components with use of a cross-validation method.

8. The processing result prediction method of claim 3, wherein the process of obtaining the residuals of the principal component analysis model as the first residuals includes:

a process of dividing a result of the principal component analysis into principal components with high contribution rates and principal components with low contribution rates; and

a process of merging residuals of the principal components with low contribution rates into a residual matrix.

9. The processing result prediction method of claim 3, wherein the multivariate analysis model is a multiple regression analysis model.

10. The processing result prediction method of claim 3, wherein said a certain condition is a condition before a maintenance, and said another condition is a condition after the apparatus is changed due to the maintenance.

11. The processing result prediction method of claim 10, wherein said a condition in the apparatus includes attachment statuses of the respective detectors.

12. The processing result prediction method of claim 10, wherein said a condition in the apparatus includes a surface condition in the apparatus.

13. The processing result prediction method of claim 10, wherein the maintenance includes a cleaning of the apparatus.

14. The processing result prediction method of claim 3, wherein the processing apparatus is a plasma processing apparatus.

15. The processing result prediction method of claim 14, wherein both the detected data include data for plasma emission intensities.

16. A method for evaluating a processing apparatus condition, comprising:

a process of performing a predetermined first standard processing on a plurality of objects to be processed with use of a first processing apparatus set in a certain condition;

a process of obtaining a plurality of detected data from a plurality of detectors provided at the first processing apparatus during the first standard processing;

a process of performing a certain multivariate analysis with use of the plurality of detected data obtained during the first standard processing, to set weighting references for the plurality of detected data;

a process of performing a second standard processing, which is similar to the first standard processing, on a plurality of objects to be processed, which are similar to said the plurality of objects to be processed, with use of the first processing apparatus in another condition or another second processing apparatus of the same kind;

a process of obtaining a plurality of detected data from the plurality of detectors provided at the first processing apparatus or from a plurality of detectors provided at the second processing apparatus during the second standard processing;

a process of weighting the plurality of detected data obtained during the second standard processing based on the weighting references, and applying the weighted plurality of detected data to the multivariate analysis; and

a process of evaluating an apparatus condition of the first processing apparatus or the second processing apparatus during the second standard processing based on a result of the application to the multivariate analysis.

17. The processing apparatus condition evaluation method of claim 16, wherein the weighting references are binary references indicating whether or not each of the detected data should be adopted.

18. A method for evaluating a processing apparatus condition, comprising:

a process of performing a predetermined first standard processing on a plurality of objects to be processed with use of a first processing apparatus set in a certain condition;

a process of obtaining a plurality of detected data from a plurality of detectors provided at the first processing apparatus during the first standard processing;

a process of performing a principal component analysis on the plurality of detected data obtained during the first standard processing, to construct a principal component analysis model;

a process of obtaining residuals of the principal component analysis model as first residuals;

a process of performing a second standard processing, which is similar to the first standard processing, on a plurality of objects to be processed, which are similar to said the plurality of objects to be processed, with use of the first processing apparatus in another condition or another second processing apparatus of the same kind;

a process of obtaining a plurality of detected data from the plurality of detectors provided at the first processing apparatus or from a plurality of detectors provided at the second processing apparatus during the second standard processing;

a process of applying the plurality of detected data obtained during the second standard processing to the principal component analysis model, to obtain second residuals;

a process of comparing the first residuals and the second residuals, to set weighting references for the respective second residuals;

a process of weighting the second residuals based on the weighting references, and constructing a new multivariate analysis model with use of the weighted second residuals; and

a process of evaluating an apparatus condition of the first processing apparatus or the second processing apparatus during the second standard processing with use of the multivariate analysis model.

19. The processing apparatus condition evaluation method of claim 18, wherein the weighting references are binary references indicating whether or not each of the second residuals should be adopted.

20. The processing apparatus condition evaluation method of claim 19, wherein the weighting references are references to adopt second residuals with small variations.

21. The processing apparatus condition evaluation method of claim 20, wherein the weighting references are references to determine second residuals with small variations based on the maximum value and the minimum value of the first residuals.

22. The processing apparatus condition evaluation method of claim 18, wherein the process of performing the principal component analysis on the plurality of detected data obtained during the first standard processing, to construct the principal component analysis model includes a process of obtaining a degree of principal components with use of a cross-validation method.

23. The processing apparatus condition evaluation method of claim 18, wherein the process of obtaining the residuals of the principal component analysis model as the first residuals includes:

a process of dividing a result of the principal component analysis into principal components with high contribution rates and principal components with low contribution rates; and

a process of merging residuals of the principal components with low contribution rates into a residual matrix.

24. The processing apparatus condition evaluation method of claim 18, wherein the multivariate analysis model is a multiple regression analysis model.

25. The processing apparatus condition evaluation method of claim 18, wherein said a certain condition is a condition before a maintenance, and said another condition is a condition after the apparatus is changed due to the maintenance.

26. The processing apparatus condition evaluation method of claim 25, wherein said a condition in the apparatus includes attachment statuses of the respective detectors.

27. The processing apparatus condition evaluation method of claim 25, wherein said a condition in the apparatus includes a surface condition in the apparatus.

28. The processing apparatus condition evaluation method of claim 25, wherein the maintenance includes a cleaning of the apparatus.

29. The processing apparatus condition evaluation method of claim 18, wherein the processing apparatus is a plasma processing apparatus.

30. The processing apparatus condition evaluation method of claim 29, wherein both the detected data include data for plasma emission intensities.

31. An apparatus for predicting a processing result, comprising:

a unit for, while performing a predetermined first standard processing on a plurality of objects to be processed with use of a first processing apparatus set in a certain condition, performing a certain multivariate analysis, with use of a plurality of detected data obtained from a plurality of detectors provided at the first processing apparatus, to set weighting references for the plurality of detected data;

a unit for, while performing a second standard processing, which is similar to the first standard processing, on a plurality of objects to be processed, which are similar to said the plurality of objects to be processed, with use of the first processing apparatus in another condition or another second processing apparatus of the same kind, weighting a plurality of detected data, obtained from the plurality of detectors provided at the first processing apparatus or from a plurality of detectors provided at the second processing apparatus, and applying the weighted plurality of detected data to the multivariate analysis; and

a unit for predicting a processing result of the second standard processing based on a result of the application to the multivariate analysis.

32. The processing result prediction apparatus of claim 31, wherein the weighting references are binary references indicating whether or not each of the detected data should be adopted.

33. An apparatus for predicting a processing result, comprising:

a unit for, while performing a predetermined first standard processing on a plurality of objects to be processed with use of a first processing apparatus set in a certain condition, performing a principal component analysis on a plurality of detected data obtained from a plurality of detectors provided at the first processing apparatus, to construct a principal component analysis model;

a unit for obtaining residuals of the principal component analysis model as first residuals;

a unit for, while performing a second standard processing, which is similar to the first standard processing, on a plurality of objects to be processed, which are similar to said the plurality of objects to be processed, with use of the first processing apparatus in another condition or another second processing apparatus of the same kind, applying a plurality of detected data, obtained from the plurality of detectors provided at the first processing apparatus or from a plurality of detectors provided at the second processing apparatus, to the principal component analysis model, to obtain second residuals;

a unit for comparing the first residuals and the second residuals, to set weighting references for the respective second residuals;

a unit for weighting the second residuals based on the weighting references, and constructing a new multivariate analysis model with use of the weighted second residuals; and

a unit for predicting a processing result of the second standard processing with use of the multivariate analysis model.

34. The processing result prediction apparatus of claim 33, wherein the weighting references are binary references indicating whether or not each of the second residuals should be adopted.

35. The processing result prediction apparatus of claim 34, wherein the weighting references are references to adopt second residuals with small variations.

36. The processing result prediction apparatus of claim 35, wherein the weighting references are references to determine second residuals with small variations based on the maximum value and the minimum value of the first residuals.

37. The processing result prediction apparatus of claim 33, wherein the unit to construct the principal component analysis model includes a unit for obtaining a degree of principal components with use of a cross-validation method.

38. The processing result prediction apparatus of claim 33, wherein the unit for obtaining the residuals of the principal component analysis model as the first residuals includes:

a unit for dividing a result of the principal component analysis into principal components with high contribution rates and principal components with low contribution rates; and

a unit for merging residuals of the principal components with low contribution rates into a residual matrix.

39. The processing result prediction apparatus of claim 33, wherein the multivariate analysis model is a multiple regression analysis model.

40. An apparatus for evaluating a processing apparatus condition, comprising:

a unit for, while performing a predetermined first standard processing on a plurality of objects to be processed with use of a first processing apparatus set in a certain condition, performing a certain multivariate analysis, with use of a plurality of detected data obtained from a plurality of detectors provided at the first processing apparatus, to set weighting references for the plurality of detected data;

a unit for, while performing a second standard processing, which is similar to the first standard processing, on a plurality of objects to be processed, which are similar to said the plurality of objects to be processed, with use of the first processing apparatus in another condition or another second processing apparatus of the same kind, weighting a plurality of detected data, obtained from the plurality of detectors provided at the first processing apparatus or from a plurality of detectors provided at the second processing apparatus, and applying the weighted plurality of detected data to the multivariate analysis; and

a unit for evaluating an apparatus condition of the first processing apparatus or the second processing apparatus during the second standard processing based on a result of the application to the multivariate analysis.

41. The processing apparatus condition evaluation apparatus of claim 40, wherein the weighting references are binary references indicating whether or not each of the detected data should be adopted.

42. An apparatus for evaluating a processing apparatus condition, comprising:

a unit for, while performing a predetermined first standard processing on a plurality of objects to be processed with use of a first processing apparatus set in a certain condition, performing a principal component analysis on a plurality of detected data obtained from a plurality of detectors provided at the first processing apparatus, to construct a principal component analysis model;

a unit for obtaining residuals of the principal component analysis model as first residuals;

a unit for, while performing a second standard processing, which is similar to the first standard processing, on a plurality of objects to be processed, which are similar to said the plurality of objects to be processed, with use of the first processing apparatus in another condition or another second processing apparatus of the same kind, applying a plurality of detected data, obtained from the plurality of detectors provided at the first processing apparatus or from a plurality of detectors provided at the second processing apparatus, to the principal component analysis model, to obtain second residuals;

a unit for comparing the first residuals and the second residuals, to set weighting references for the respective second residuals;

a unit for weighting the second residuals based on the weighting references, and constructing a new multivariate analysis model with use of the weighted second residuals; and

a unit for evaluating an apparatus condition of the first processing apparatus or the second processing apparatus during the second standard processing with use of the multivariate analysis model.

43. The processing apparatus condition evaluation apparatus of claim 42, wherein the weighting- references are binary references indicating whether or not each of the second residuals should be adopted.

44. The processing apparatus condition evaluation apparatus of claim 43, wherein the weighting references are references to adopt second residuals with small variations.

45. The processing apparatus condition evaluation apparatus of claim 44, wherein the weighting references are references to determine second residuals with small variations based on the maximum value and the minimum value of the first residuals.

46. The processing apparatus condition evaluation apparatus of claim 42, wherein the unit to construct the principal component analysis model includes a unit for obtaining a degree of principal components with use of a cross-validation method.

47. The processing apparatus condition evaluation apparatus of claim 42, wherein the unit for obtaining the residuals of the principal component analysis model as the first residuals includes:

a unit for dividing a result of the principal component analysis into principal components with high contribution rates and principal components with low contribution rates; and

a unit for merging residuals of the principal components with low contribution rates into a residual matrix.

48. The processing apparatus condition evaluation apparatus of claim 42, wherein the multivariate analysis model is a multiple regression analysis model.