Self-correcting multivariate analysis for use in monitoring dynamic parameters in process environments

• US 7,809,450 B2
• Filed: 07/05/2006
• Issued: 10/05/2010
• Est. Priority Date: 07/07/2005
• Status: Active Grant

• Current Assignee: MKS Instruments AB (Sartorius AG)
• Original Assignee: MKS Instruments Incorporated
• Inventors: Lev-Ami, Uzi Josef, Hendler, Lawrence
• Primary Examiner(s): Von Buhr; M. N.
• Application Number:
  US11/481,140
• Publication Number:
  US 20070021859A1
• Time in Patent Office: 1,553 Days
• Field of Search: 700 28- 34, 700 47- 54, 700108-111
• US Class Current: 700/30
• 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

Abstract

A method and apparatus for process monitoring are provided. Process monitoring includes (i) generating a multivariate analysis reference model of a process environment from data corresponding to monitored parameters of the process environment; (ii) designating at least one of the monitored parameters as being correlated to maturation of the process environment; (iii) collecting current process data corresponding to the monitored parameters, including the at least one designated parameter; and (iv) scaling the multivariate reference model based on the current process data of the at least one designated parameter to account for maturation of the process environment. The method further includes generating one or more current multivariate analysis process metrics that represent a current state of the process environment from the current process data; and comparing the current process metrics to the scaled reference model to determine whether the current state of the process environment is acceptable.

Claims

1. A method of process monitoring, comprising:

generating a multivariate analysis reference model of a process environment from data corresponding to monitored parameters of the process environment;

identifying at least one, but less than all, of the monitored parameters having an expected maturation path that correlates to an expected maturation path of the process environment;

collecting current process data corresponding to the monitored parameters, including the at least one identified parameter; and

mathematically manipulating the multivariate reference model based on the current process data limited to the at least one identified parameter to account for the expected maturation path of the process environment.

2. The method of claim 1 further comprising:

generating one or more current multivariate analysis process metrics that represent a current state of the process environment from the current process data; and

comparing the one or more current process metrics to the mathematically manipulated reference model to determine whether the current state of the process environment is acceptable.

3. The method of claim 1 wherein generating the reference model comprises:

generating a metric value and a set of threshold values about the metric that represent the state of the process environment, the set of threshold values defining a range of acceptable metric values associated with subsequent states of the process environment.

4. The method of claim 3 wherein mathematically manipulating the reference model comprises:

scaling the set of threshold values of the reference model based on the current process data of the at least one identified parameter to account for the expected maturation path of the process environment.

5. The method of claim 4 further comprising:

generating one or more current multivariate analysis process metrics that represent a current state of the process environment from the current process data;

comparing the one or more current multivariate analysis process metrics to the mathematically manipulated set of threshold values to determine whether the current state of the process environment is acceptable.

16. The method of claim 1 further comprising weighting the at least one identified parameter used in mathematically manipulating the multivariate reference model.

6. A method of semiconductor process monitoring, comprising:

generating a multivariate analysis reference model of a semiconductor process chamber from data corresponding to monitored parameters of the semiconductor process chamber;

identifying at least one, but less than all, of the monitored parameters having an expected maturation path that correlates to an expected maturation path of the semiconductor process chamber;

collecting current process data corresponding to the monitored parameters of the process chamber, including the at least one identified parameter; and

mathematically manipulating the multivariate reference model based on the current process data limited to the at least one identified parameter to account for the expected maturation path of the semiconductor process chamber.

7. The method of claim 6 further comprising:

generating one or more current multivariate analysis process metrics that represent a current state of the semiconductor process chamber from the current process data; and

comparing the one or more current multivariate analysis process metrics to the mathematically manipulated reference model to determine whether the current state of the process environment is acceptable.

17. The method of claim 6 further comprising weighting the at least one identified parameter used in mathematically manipulating the multivariate reference model.

8. An apparatus for process monitoring, comprising:

a multivariate analysis module capable of generating a multivariate analysis reference model of a process environment from data corresponding to monitored parameters of the process environment;

the multivariate analysis module being capable of receiving an identification of at least one, but less than all, of the monitored parameters having an expected maturation path that correlates to an expected maturation path of the process environment;

a process interface capable of collecting current process data corresponding to the monitored parameters, including the at least one identified parameter; and

the multivariate analysis module being capable of mathematically manipulating the multivariate reference model based on the current process data limited to the at least one identified parameter to account for the expected maturation path of the process environment.

9. The apparatus of claim 8 wherein the multivariate analysis module is capable of generating one or more current multivariate analysis process metrics that represent a current state of the process environment from the current process data and comparing the one or more current process metrics to the mathematically manipulated reference model to determine whether the current state of the process environment is acceptable.

10. The method of claim 8 wherein the multivariate analysis module is capable of generating a metric value and a set of threshold values about the metric that represents the state of the process environment, the set of threshold values defining a range of acceptable metric values associated with subsequent states of the process environment.

11. The method of claim 10 wherein the multivariate analysis module is capable of mathematically manipulating the set of threshold values of the reference model based on the current process data of the at least one identified parameter to account for the expected maturation path of the process environment.

12. The method of claim 11 wherein the multivariate analysis module is capable of generating one or more current multivariate analysis process metrics that represent a current state of the process environment from the current process data and comparing the one or more current multivariate analysis process metrics to the mathematically manipulated set of threshold values to determine whether the current state of the process environment is acceptable.

18. The apparatus of claim 8 wherein the multivariate analysis module is capable of weighting the at least one identified parameter used in mathematically manipulating the multivariate reference model.

13. An apparatus for semiconductor process monitoring, comprising:

a multivariate analysis module capable of generating a multivariate analysis reference model of a semiconductor process chamber from data corresponding to monitored parameters of the semiconductor process chamber;

the multivariate analysis module being capable of receiving an identification of at least one, but less than all, of the monitored parameters having an expected maturation path that correlates to an expected maturation path of the semiconductor process chamber;

a process interface capable of collecting current process data corresponding to the monitored parameters of the process chamber, including the at least one identified parameter; and

the multivariate analysis module being capable of mathematically manipulating the multivariate reference model based on the current process data limited to the at least one identified parameter to account for the expected maturation path of the semiconductor process chamber.

14. The apparatus of claim 13 wherein the multivariate analysis module is capable of generating one or more current multivariate analysis process metrics that represent a current state of the semiconductor process chamber from the current process data and comparing the one or more current multivariate analysis process metrics to the mathematically manipulated reference model to determine whether the current state of the process environment is acceptable.

19. The apparatus of claim 13 wherein the multivariate analysis module is capable of weighting the at least one identified parameter used in mathematically manipulating the multivariate reference model.

15. A process monitor, comprising:

means for generating a multivariate analysis reference model of a desired process environment from data corresponding to monitored parameters of the desired process environment;

means for identifying at least one, but less than all, of the monitored parameters having an expected maturation path that correlates to an expected maturation path of the process environment;

means for collecting current process data corresponding to the monitored parameters, including the at least one identified parameter; and

means for mathematically manipulating the multivariate reference model based on the current process data limited to the at least one identified parameter to account for the expected maturation path of the process environment.

20. The process monitor of claim 15 further comprising means for weighting the at least one identified parameter used in mathematically manipulating the multivariate reference model.