METHOD AND SYSTEM FOR DETECTION OF TOOL PERFORMANCE DEGRADATION AND MISMATCH

• US 20090240366A1
• Filed: 03/31/2009
• Published: 09/24/2009
• Est. Priority Date: 03/08/2008
• Status: Active Grant

• Current Assignee: Tokyo Electron Limited
• Original Assignee: Tokyo Electron Limited
• Inventors: Kaushal, Sanjeev, Patel, Sukesh Janubhai, Sugishima, Kenji
• Granted Patent: US 8,725,667 B2
• Time in Patent Office: Days
• US Class Current: 700/110
• CPC Class Codes: G05B 13/0265 the criterion being a learning criterion
• CPC Class Codes: G05B 19/4065 Monitoring tool breakage, life or condition
• CPC Class Codes: G05B 2219/33041 Structure optimization and learning of artificial neural network by genetic algorithm
• CPC Class Codes: G05B 2219/37252 Life of tool, service life, decay, wear estimation
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: Y02P 90/80 Management or planning

Abstract

Autonomous biologically based learning tool system(s) and method(s) that the tool system(s) employs for learning and analysis of performance degradation and mismatch are provided. The autonomous biologically based learning tool system includes (a) one or more tool systems that perform a set of specific tasks or processes and generate assets and data related to the assets that characterize the various processes and associated tool performance; (b) an interaction manager that receives and formats the data, and (c) an autonomous learning system based on biological principles of learning. Objectively generated knowledge gleaned from synthetic or production data can be utilized to determine a mathematical relationship among a specific output variable and a set of associated influencing variables. The generated relationship facilitates assessment of performance degradation of a set of tools, and performance mismatch among tools therein.

Claims

1. An autonomous biologically based learning system, comprising:

a component that selects a set of variables and data associated with a set of units in an equipment that produces an asset, wherein the set of variables includes a reference variable and a set of influencing variables thereof,

an objective learning engine that learns a relationship among the reference variable and the set of influencing variables for the selected data; and

a performance assessment component that compares a measured value of the reference variable and a predicted value thereof based on the learned relationship to determine a performance degradation of at least one unit in the set units in the equipment.

2. The system of claim 1, wherein the asset includes one or more of semiconductor devices, the devices comprising plasma-discharge based flat panel displays (FPDs), organic light emitting diode (OLED)-based FPDs, liquid crystal display (LCD)-based FPDs, or elements thereof which comprise one or more of thin-film-transistor active matrix arrays, color filters, or polarizers.

3. The system of claim 1, the performance assessment component compares a measured value of the reference variable and a predicted value thereof based on the relationship to determine a performance mismatch among disparate equipment units.

4. The system of claim 3, wherein the data includes at least one of manufacturing run data, synthetic manufacturing data, and test manufacturing data.

5. The system of claim 1, wherein the data selector selects the reference variable via at least one of an automated analysis or an actor input.

6. The system of claim 5, wherein the automated analysis includes an arrangement of candidate reference variables in accordance with a variation magnitude of each of the candidate reference variables in a manufacturing process of the asset.

7. The system of claim 6, wherein the data selector selects at least one of a low variation variable, a medium variation variable, or a high variation variable.

8. The system of claim 1, wherein the data selector selects a set of influencing variables based at least on one of a universal selection, a semi-universal selection, a knowledge based selection, an empirical selection, or an actor-driven selection.

9. The system of claim 4, wherein the data selector includes a formatter component that conveys selected data in a matrix representation.

10. The system of claim 9, wherein the matrix representation includes a reference variable and at least one variable in the set of influencing variables associated with the reference variable.

11. The system of claim 10, wherein the data selector includes a component that selects reference data from the data associated with a set of units in equipment that produces an asset, the reference data is at least a portion of the data.

12. The system of claim 11, wherein the data selector selects reference data via an actor input.

13. The system of claim 12, wherein the formatter component generates a training matrix based at least in part on the selected reference data.

14. The system of claim 13, wherein the reference data includes data from at least one of a reference chamber unit, a set of tool units, or a measurement unit that collects data at a predetermined sampling rate.

15. The system of claim 14, wherein the reference data includes data from one of a single step or multiple steps in a recipe to produce the asset, the multiple steps can be contiguous or disjointed.

16. The system of claim 15, wherein the reference data includes average data that originates from at least one of a step level, a wafer level, a lot level, an active matrix array manufacturing, or a wet-clean cycle.

17. The system of claim 16, wherein the reference data includes average data that originates in a predetermined cycle within a process to manufacture an asset, the asset comprises a flat panel display device.

18. The system of claim 13, wherein the apply matrix is generated for a one or more instances of a process to manufacture the asset, the one or more instances include at least one of a step in the process recipe, a wafer run in a set of tools, a lot run in a set of tools, or a set of cycles within the process.

19. The system of claim 3, wherein the relationship among the reference variable and the set of influencing variables for the selected data is a mathematical functional relationship.

20. The system of claim 19, wherein the mathematical functional relationship computes a difference among a measured value of the reference variable and a predicted value thereof.

21. The system of claim 20, wherein the mathematical function relationship computes a variation of the difference among a measured value of the reference variable and a predicted value thereof.

22. The system of claim 19, wherein the selected data is production data for at least one instance in a process to manufacture the asset.

23. The system of claim 19, wherein the performance assessment component includes an analysis component that summarizes a distribution of differences between a measured and predicted value through a set of statistical-mechanics functions.

24. The system of claim 23, wherein a set of actor-supplied key performance indicator thresholds determine at least one of an abnormal performance degradation or an abnormal performance mismatch.

25. The system of claim 23, wherein at least one of the abnormal performance degradation or the abnormal performance mismatch is determined for at least one of a reference tool or an instrument thereof.

26. The system of claim 24, wherein the analysis component includes a formal analysis component that determines a ranking of influential variables through unsupervised theorem proving algorithms and expression analysis of the mathematical functional relationship.

27. The system of claim 26, wherein the analysis component includes a numerical analysis component that establishes a ranking of influential variables through computation of a gradient vector of the mathematical functional relationship for the set of influencing variables.

28. The system of claim 27, wherein at least one of a performance degradation of a set of tools or a performance mismatch among tools in a set of tools can be profiled according to the ranking of influencing variables, wherein the relevancy profile arises from the ranking of influential variables.

29. A method for establishing a performance degradation or mismatch of one or more tools that manufacture one or more assets, the method comprising:

measuring a value of a reference variable;

measuring a value for each variable in a set of influencing variables associated with the reference variable;

predicting a value for the reference variable based on a learned formal relationship among the reference variable and the set of influencing variables; and

reporting at least one of performance degradation or mismatch when a difference among prediction and measurement is above a performance metric threshold.

30. The method of claim 29, further comprising generating and retaining a relevancy profile for the set of influencing variables and learned functions when a difference among prediction and measurement is above a performance metric threshold.

31. The method of claim 30, wherein reporting at least one of performance degradation or mismatch includes conveying performance information to an actor, the performance information comprises at least one of a relevancy profile or a set of influencing variables ordered according to a ranking criterion with a time stamp associated with a performance degradation or mismatch event.

32. The method of claim 31, wherein generating a relevancy profile for the set of influencing variables and learned functions includes performing a sensitivity analysis based at least in part on at least one of a percentage impact of the set of influencing variables on a predicted value for the reference variable, or a numeric derivative assessment of the learned function of the set of influencing variables.

33. The method of claim 29, wherein predicting a value for the reference variable based on a learned formal relationship among the reference variable and the set of influencing variables as measured further comprises:

receiving a set of data associated at least in part with the manufacture of the one or more assets;

selecting a set of reference data for a set of variables;

determining a reference variable in the set of variables and selecting a set of influencing variables; and

learning the formal functional relationship among the reference variable and the set of influencing variables.

34. The method of claim 33, wherein the one or more assets include semiconductor devices, the devices comprising plasma-discharge based flat panel displays (FPDs), OLED-based FPDs, LCD-based FPDs.

35. The method of claim 34, wherein the set of data includes at least one of production run data, synthetic manufacturing data, or test manufacturing data.

36. The method of claim 35, further comprising formatting the received set of data, wherein formatting the received data includes generating a matrix representation for at least one of test manufacturing data or production run data.

37. The method of claim 36, wherein the matrix representation of test manufacturing data is generated at one or more instants during operation of at least one of a reference tool or production tool.

38. The method of claim 37, wherein the matrix representation of production run data is generated at one or more instants during operation of a production tool.

39. The method of claim 36, wherein generating a matrix representation for at least one of test manufacturing data or production run data is generated at one or more production instances, the one or more production instances include at least one of a step level in one or more recipes, or a plurality of steps either contiguous or disjointed in one or more recipes.

40. The method of claim 36, wherein generating a matrix representation for at least one of test manufacturing data or production run data is generated via received data at the asset level.

41. The method of claim 33, wherein selecting the set of reference data includes at least one of selecting a portion of the received data or receiving input from an actor.

42. The method of claim 41, wherein the set of reference data includes data from at least one of a reference tool, a set of tool, or a measurement unit in a tool or set of tools that collects data at a predetermined sampling rate.

43. The method of claim 42, wherein the set of reference data includes data from one of a single step or multiple steps in one or more recipes to produce the asset, the multiple steps are one of contiguous or disjointed.

44. The method of claim 43, wherein the set of reference data includes average data that originates from at least one of a step level, a wafer level, a lot level, an active matrix array manufacturing, or a wet-clean cycle.

45. The method of claim 33, wherein selecting set of influencing variables is based at least on one of a universal selection, a semi-universal selection, a knowledge based selection, an empirical selection, or an actor-driven selection.

46. The method of claim 45, wherein learning the formal functional relationship among the reference variable and the set of influencing variables includes removing one or more influencing variables that weakly affect a predicted output for the reference variable with respect to a disparate predicted output when the formal functional relationship includes the removed one or more influencing variables.

47. A computer program product including a computer-readable storage medium comprising a set of instructions that, when executed by a computer cause the computer to perform the following acts:

measuring a set of values of a reference variable in an instance of manufacturing process of an asset;

measuring a set of values for each variable in a set of influencing variables associated with the reference variable;

learning a formal relationship among the reference variable and the set of influencing variables as measured;

predicting a set of values for the reference variable based on the learned formal relationship; and

reporting performance degradation or mismatch when a difference among prediction and measurement is above a key performance indicator threshold.

48. An apparatus, comprising:

means for identifying a set of variables and data associated with a set of units in an equipment that produces an asset, wherein the set of variables includes a reference variable and a set of influencing variables thereof,

means for autonomously learning a functional relationship among the reference variable and the set of influencing variables for the selected data; and

means for assessing at least one of performance degradation or performance mismatch of at least one unit in the set units in the equipment based at least in part on values of the reference variable predicted through the learned functional relationship.