METHODS AND COMPUTER PROGRAM PRODUCTS FOR EXCLUDING VARIATIONS ATTRIBUTABLE TO EQUIPMENT FROM SPLIT ANALYSIS PROCEDURES

• US 20090150811A1
• Filed: 12/10/2007
• Published: 06/11/2009
• Est. Priority Date: 12/10/2007
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Song, Yunsheng, Ouyang, Xu
• Granted Patent: US 7,953,680 B2
• Time in Patent Office: Days
• US Class Current: 715/764
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 50/04 Manufacturing
• CPC Class Codes: Y02P 90/30 Computing systems specially adapted for manufacturing

Abstract

Excluding variations attributable to equipment from split analysis is performed by identifying dependent variables related to at least one of the split analysis or an experiment to be performed. A test is performed to ascertain whether or not a variation attributable to equipment exists with respect to any of the identified dependent variables. If such a variation exists, a target data set and a training data set are constructed. A signature is identified for the variation. A statistical model is selected based upon the identified signature. The selected statistical model is constructed using the training data set to generate a statistical output. The target data set is joined with the statistical output. The identified dependent variables in the target data set are adjusted using the statistical output. The target data set including the adjusted identified dependent variables is loaded to an application for performing split analysis.

Claims

1. A method for excluding variations attributable to equipment from split analysis procedures, the method including:

identifying one or more dependent variables related to at least one of a split analysis or an experiment to be performed;

performing a test to ascertain whether or not a variation attributable to equipment exists with respect to any of the one or more identified dependent variables;

if a variation attributable to equipment exists, constructing a target data set and a training data set, the target data set including the identified dependent variables and the training data set comprising any data set from which a statistical model may be constructed;

identifying a signature for the variation attributable to equipment;

selecting a statistical model based upon the identified signature;

constructing the selected statistical model using the training data set to generate a statistical output;

joining the target data set with the statistical output; and

adjusting the identified dependent variables in the target data set using the statistical output.

2. The method of claim 1 further comprising loading the target data set including the adjusted identified dependent variables into a data mining or data analysis application for performing split analysis.

3. The method of claim 1 wherein performing the test to ascertain whether or not a variation attributable to equipment exists with respect to any of the one or more identified dependent variables is performed in conjunction with a tool chamber commonality analysis program or a wafer randomization analysis program.

4. The method of claim 1 wherein the target data set contains a plurality of numerical variables which can be used to implement split definition variables in split analysis, as well as a plurality of equipment related variables.

5. The method of claim 4 wherein the plurality of equipment related variables include any of a tool identifier, a chamber identifier, a track identifier, a front opening unified pod (FOUP) slot location, or one or more process step identifiers.

6. The method of claim 1 wherein the target data set is a subset of the training data set, or wherein the target data set is the training data set.

7. The method of claim 1 wherein the selected statistical model is capable of implementing an analysis of means.

8. The method of claim 1 wherein the selected statistical model is capable of implementing a linear regression.

9. The method of claim 1 wherein, if the variation attributable to equipment is a tool variation or a chamber variation or both, the selected statistical model is capable of implementing an analysis of means and wherein, if the variation attributable to equipment shows a linear correlation with any parameter, the selected statistical model is capable of implementing a linear regression.

10. The method of claim 2 further comprising providing a graphical user interface that visually displays a first magnitude indicative of an extent to which tool variation will impact the split analysis relative to a second magnitude indicative of an extent to which chamber variation will impact the split analysis.

11. A computer program product for excluding variations attributable to equipment from split analysis procedures, the computer program product including a storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for facilitating a method including:

identifying one or more dependent variables related to at least one of a split analysis or an experiment to be performed;

performing a test to ascertain whether or not a variation attributable to equipment exists with respect to any of the one or more identified dependent variables;

if a variation attributable to equipment exists, constructing a target data set and a training data set, the target data set including the identified dependent variables and the training data set comprising any data set from which a statistical model may be constructed;

identifying a signature for the variation attributable to equipment;

selecting a statistical model based upon the identified signature;

constructing the selected statistical model using the training data set to generate a statistical output;

joining the target data set with the statistical output; and

adjusting the identified dependent variables in the target data set using the statistical output.

12. The computer program product of claim 11 further comprising instructions for loading the target data set including the adjusted identified dependent variables into a data mining or data analysis application for performing split analysis.

13. The computer program product of claim 11 wherein performing the test to ascertain whether or not a variation attributable to equipment exists with respect to any of the one or more identified dependent variables is performed in conjunction with a tool chamber commonality analysis program or a wafer randomization analysis program.

14. The computer program product of claim 11 wherein the target data set contains a plurality of numerical variables which can be used to implement split definition variables in split analysis, as well as a plurality of equipment related variables.

15. The computer program product of claim 14 wherein the plurality of equipment related variables include any of a tool identifier, a chamber identifier, a track identifier, a front opening unified pod (FOUP) slot location, or one or more process step identifiers.

16. The computer program product of claim 11 wherein the target data set is a subset of the training data set, or wherein the target data set is the training data set.

17. The computer program product of claim 11 wherein the selected statistical model is capable of implementing an analysis of means.

18. The computer program product of claim 11 wherein the selected statistical model is capable of implementing a linear regression.

19. The computer program product of claim 11 wherein, if the variation attributable to equipment is a tool variation or a chamber variation or both, the selected statistical model is capable of implementing an analysis of means and wherein, if the variation attributable to equipment shows a linear correlation with any parameter, the selected statistical model is capable of implementing a linear regression.

20. The computer program product of claim 12 further comprising instructions for providing a graphical user interface that visually displays a first magnitude indicative of an extent to which tool variation will impact the split analysis relative to a second magnitude indicative of an extent to which chamber variation will impact the split analysis.