User configurable multivariate time series reduction tool control method

• US 6,678,569 B2
• Filed: 11/06/2001
• Issued: 01/13/2004
• Est. Priority Date: 03/19/1999
• Status: Expired due to Term

• Current Assignee: GlobalFoundries, Inc.
• Original Assignee: International Business Machines Corporation
• Inventors: Surendra, Maheswaran, Ghias, Asif, Colt, John Z. Jr., Pascoe, Nancy T., Bunkofske, Raymond J., McGill, James J, Taubenblatt, Marc A.
• Primary Examiner(s): Picard, Leo
• Assistant Examiner(s): Garland, Steven R.
• Application Number:
  US10/013,070
• Publication Number:
  US 20020062162A1
• Time in Patent Office: 798 Days
• Field of Search: 700/108, 700/109, 700/51, 700/28, 700/33, 700/34, 700/52, 700/67, 700/73, 700/79, 700/80, 700/121, 700/174, 700/175, 700/177, 702/179, 702/181, 702/182, 702/185, 216/60
• US Class Current: 700/108
• CPC Class Codes: G05B 2219/45031 Manufacturing semiconductor wafers
• CPC Class Codes: G05B 23/0221 Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
• CPC Class Codes: H01L 22/20 Sequence of activities consisting of a plurality of measurements, corrections, marking or sorting steps

Abstract

A method and structure for controlling a manufacturing tool includes measuring different manufacturing parameters of the tool, transforming a plurality of time series of the manufacturing parameters into intermediate variables based on restrictions and historical reference statistics, generating a surrogate variable based on the intermediate variables, if the surrogate variable exceeds a predetermined limit, identifying a first intermediate variable, of the intermediate variables, that caused the surrogate variable to exceed the predetermined limit and identifying a first manufacturing parameter associated with the first intermediate variable, and inhibiting further operation of the tool until the first manufacturing parameter has been modified to bring the surrogate value within the predetermined limit.

Claims

1. A computerized method of determining corrections for a manufacturing process, comprising:

measuring a set of manufacturing parameters;

performing a first T² score computation using said manufacturing parameters;

providing a first inverse matrix using intermediate variables obtained from said first T² score computation;

using said first inverse matrix to create a generalized formulation for such matrices;

using said generalized formulation to compute a second T² score for said inverse matrix with one variable missing; and

subtracting said second T² score from said first T² score.

2. The method according to claim 1, wherein:

$T^2={\left(\frac{\left(x-\stackrel{\_}{x}\right)}{\mathrm{\sigma <br><br>}}\right)}^T<br><br>R^{-1}<br><br>\left(\frac{\left(x-\stackrel{\_}{x}\right)}{\mathrm{\sigma <br><br>}}\right)=z^T<br><br>R^{-1}<br><br>z,$

where x comprises said intermediate variables, {overscore (x)} comprises a historical sensor value, σ

comprises a historical standard deviation sensor value, R<sup>−

1</sup> comprises an inverse correlation matrix and z comprises mean and standard deviation normalized values.

3. The method according to claim 2, wherein x and σ

are user adjustable.

4. The method in claim 1, further comprising transforming a plurality of time series of said manufacturing parameters into said intermediate variables based on restrictions and historical reference statistics.

5. The method according to claim 1, further comprising:

repeating said computing and said subtracting for each of said intermediate variables; and

ranking results of said subtracting to identify an out-of-control intermediate variable.

6. The method in claim 5, wherein said ranking determines which missing variable causes a largest difference between said first T² score and said second T² score.

7. The method in claim 6, wherein a missing variable causing said largest difference is an out-of-control intermediate variable.

8. A computerized method of determining corrections for a manufacturing process, comprising:

measuring a set of manufacturing parameters;

performing a first surrogate score computation using said manufacturing parameters;

providing a first inverse matrix using intermediate variables obtained from said first surrogate score computation;

using said first inverse matrix to create a generalized formulation for such matrices;

using said generalized formulation to compute a second surrogate score for said inverse matrix with one variable missing; and

subtracting said second surrogate score from said first surrogate score.

9. The method according to claim 8, wherein said first surrogate score comprises:

$T^2={\left(\frac{\left(x-\stackrel{\_}{x}\right)}{\mathrm{\sigma <br><br>}}\right)}^T<br><br>R^{-1}<br><br>\left(\frac{\left(x-\stackrel{\_}{x}\right)}{\mathrm{\sigma <br><br>}}\right)=z^T<br><br>R^{-1}<br><br>z,$

where x comprises said intermediate variables, {overscore (x)} comprises a historical sensor value, σ

comprises a historical standard deviation sensor value, R<sup>−

1</sup> comprises an inverse correlation matrix and z comprises mean and standard deviation normalized values.

10. The method according to claim 9, wherein x and σ

are user adjustable.

11. The method in claim 8, further comprising transforming a plurality of time series of said manufacturing parameters into said intermediate variables based on restrictions and historical reference statistics.

12. The method according to claim 8, further comprising:

repeating said computing and said subtracting for each of said intermediate variables; and

ranking results of said subtracting to identify an out-of-control intermediate variable.

13. The method in claim 12, wherein said ranking determines which missing variable causes a largest difference between said first surrogate score and said second surrogate score.

14. The method in claim 13, wherein a missing variable causing said largest difference is an out-of-control intermediate variable.

15. A computerized method of determining corrections for a manufacturing process, comprising:

measuring a set of manufacturing parameters;

performing a first T² score computation using said manufacturing parameters;

providing a first inverse matrix using intermediate variables obtained from said first T² score computation;

using said first inverse matrix to create a generalized formulation for such matrices;

using said generalized formulation to compute a second T² score for said inverse matrix with one variable missing;

subtracting said second T² score from said first T² score;

repeating said computing and said subtracting for each of said intermediate variables; and

ranking results of said subtracting to identify an out-of-control intermediate variable.

16. The method according to claim 15, wherein:

$T^2={\left(\frac{\left(x-\stackrel{\_}{x}\right)}{\mathrm{\sigma <br><br>}}\right)}^T<br><br>R^{-1}<br><br>\left(\frac{\left(x-\stackrel{\_}{x}\right)}{\mathrm{\sigma <br><br>}}\right)=z^T<br><br>R^{-1}<br><br>z,$

where x comprises said intermediate variables, {overscore (x)} comprises a historical sensor value, σ

comprises a historical standard deviation sensor value, R<sup>−

1</sup> comprises an inverse correlation matrix and z comprises mean and standard deviation normalized values.

17. The method according to claim 16, wherein x and σ

are user adjustable.

18. The method in claim 15, further comprising transforming a plurality of time series of said manufacturing parameters into said intermediate variables based on restrictions and historical reference statistics.

19. The method in claim 18, wherein said ranking determines which missing variable causes a largest difference between said first T² score and said second T² score.

20. The method in claim 19, wherein a missing variable causing said largest difference is an out-of-control intermediate variable.