Adjusting manufacturing process control parameter using updated process threshold derived from uncontrollable error

• US 7,349,753 B2
• Filed: 04/08/2005
• Issued: 03/25/2008
• Est. Priority Date: 05/28/2004
• Status: Expired due to Fees

• Current Assignee: Applied Materials Incorporated
• Original Assignee: Applied Materials Incorporated
• Inventors: Paik, Young J.
• Primary Examiner(s): Jarrett; Ryan A.
• Application Number:
  US11/102,438
• Publication Number:
  US 20050267607A1
• Time in Patent Office: 1,082 Days
• Field of Search: 700/31, 700/33, 700/34, 700/51, 700/44, 700/45, 700108-110, 702/84
• US Class Current: 700/110
• CPC Class Codes: G05B 19/41875 characterised by quality surveillance of production
• CPC Class Codes: G05B 2219/31103 Configure parameters of controlled devices
• CPC Class Codes: G05B 2219/32187 Correlation between controlling parameters for influence on quality parameters
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

A method, system and medium are provided for enabling improved feedback and feedforward control. An error, or deviation from target result, is observed during manufacture of semi conductor chips. The error within standard deviation is caused by two components: a white noise component and a signal component (such as systematic errors). The white noise component is random noise and therefore is relatively non-controllable. The systematic error, in contrast, may be controlled by changing the control parameters. A ratio between the two components is calculated autoregressively. Based on the ratio and using the observed or measured error, the actual value of the error caused by the signal component is calculated utilizing an autoregressive stochastic sequence. The actual value of the error is then used in determining when and how to change the control parameters. The autoregressive stochastic sequence addresses the issue of real-time control of the effects of run-to-run deviations, and provides a mechanism that can extract white noise from the statistical process variance in real time. This results in an ability to provide tighter control of feedback and feedforward variations.

Claims

1. A computer-implemented method for controlling a manufacturing process, comprising the steps of:

(a) inputting a model for the manufacturing process and obtaining a manufacturing recipe based on the model, wherein the model predicts at least one value for a product characteristic of at least one product processed by the manufacturing process;

(b) receiving at least one observed value for the product characteristic of the at least one product and determining a variance between the at least one observed value and the at least one predicted value;

(c) determining a value for uncontrollable error from the variance;

(d) using the value for uncontrollable error to update a process threshold; and

(e) adjusting at least one control parameter of the manufacturing process using the updated process threshold,

wherein the manufacturing process includes at least one device on which the at least one product is processed, the at least one device being affected by the at least one control parameter.

2. The method of claim 1, wherein the variance is determined from the at least one observed value observed for N previously processed products.

3. The method of claim 2, wherein N is in the range of 5 to 100.

4. The method of claim 2, wherein N is in the range of 10 to 40.

5. The method of claim 1, further comprising the step of:

using the value for uncontrollable error to update the at least one control parameter.

6. The method of claim 5, wherein the value for uncontrollable error is used as a weighing factor to adjust an estimated gain in the updating of the at least one control parameter.

7. The method of claim 1, wherein the value for uncontrollable error is determined using an auto-regressive stochastic sequence.

8. The method of claim 1, wherein the uncontrollable error includes at least one of random variance, normal deviation, and an ambient fluctuation.

9. The method of claim 1, wherein the model predicts the at least one value for the product characteristic based on a specification, the specification being selected from at least one of a predetermined specification, and a real-time calculation taken from a plurality of prior observed values.

10. The method of claim 1, wherein determining a value for uncontrollable error from the variance comprises determining a first portion of the variance caused by uncontrollable error and a second portion of the variance caused by controllable error.

11. The method of claim 10, wherein determining the first and second portions of the variance comprises defining a relationship between the first and second portions of the variance as:

V_x=ρ

₁²*V_x+V_w

where w=white noise

x=systematic error

V_x=variance of the controllable error

V_w=variance of the uncontrollable error

ρ

₁ is an autocorrelation factor for a lag of 1.

12. The method of claim 10, wherein determining the first and second portions of the variance comprises defining a relationship between the first and second portions of the variance as:

δ

_x=y/(1+z),

where is δ

_x represents the second portion of the variance,

y=calculated standard deviation from N previous products, and

z=(1−

ρ

₁²)^0.5, where ρ

₁ is an autocorrelation factor for a lag of 1.

13. A computer program product for controlling a manufacturing process, the computer program product comprising:

(a) at least one computer readable medium;

(b) instructions, provided on the at least one computer readable medium, for inputting a model for the manufacturing process and obtaining a manufacturing recipe based on the model, wherein the model predicts at least one value for a product characteristic of at least one product processed by the manufacturing process;

(c) instructions, provided on the at least one computer readable medium, for receiving at least one observed value for the product characteristic of the at least one product and calculating a variance between the at least one observed value and the at least one predicted value;

(d) instructions, provided on the at least one computer readable medium, for calculating value for uncontrollable error from the variance;

(e) instructions, provided on the at least one computer readable medium, for updating a process threshold based upon the value for uncontrollable error; and

(f) instructions, provided on the at least one computer readable medium, for adjusting at least one control parameter of the manufacturing process using the undated process threshold,

wherein the manufacturing process includes at least one device on which the at least one product is processed, the at least one device being affected by the at least one control parameter.

14. The computer program product of claim 13, wherein the at least one observed value is observed for the at least one products including at least one semi-conductor wafers, and the computer readable medium is readable by the manufacturing process including an automated semi-conductor manufacturing process.

15. The computer program product of claim 13, wherein the at least one predicted value is derived from a specification, the specification being selected from at least one of a predetermined specification, and a real-time calculation taken from a plurality of prior observed values of products.

16. The computer program product of claim 13, wherein the uncontrollable error includes at least one of random variance, normal deviation, and an ambient fluctuation.

17. The computer program product of claim 13, wherein the instructions for calculating the variance comprise instructions for calculating the variance from the at least one observed value observed for N previously processed products.

18. The computer program product of claim 17, wherein N is in the range of 5 to 100.

19. The computer program product of claim 17, wherein N is in the range of 10 to 40.

20. The computer program product of claim 13, wherein the instructions for calculating the uncontrollable error comprises instructions for calculating the uncontrollable error using an auto-regressive stochastic sequence.

21. The computer program product of claim 13, wherein the instructions for calculating the value for uncontrollable error comprises instructions for calculating a first portion of the variance caused by uncontrollable error and a second portion of the variance caused by controllable error.

22. The computer program product of claim 21, wherein the instructions for calculating the first and second portions of the variance comprises instructions for defining a relationship between the first and second portions of the variance as:

V_x=ρ

₁²*V_x+V_w

where w=white noise

x=systematic error

V_x=variance of the controllable error

V_w=variance of the uncontrollable error

ρ

₁ is an autocorrelation factor for a lag of 1.

23. The computer program product of claim 21, wherein the instructions for calculating the first and second portions of the variance comprises instructions for defining a relationship between the first and second portions of the variance as:

δ

_x=y/(1+z),

where is δ

_x represents the second portion of the variance,

y=calculated standard deviation from N previous products, and

z=(1−

ρ

₁²)^0.5, where ρ

₁ is an autocorrelation factor for a lag of 1.

24. A system for controlling a manufacturing process, comprising:

(a) means for inputting a model for the manufacturing process and obtaining a manufacturing recipe based on the model, wherein the model predicts at least one value for a product characteristic of at least one product processed by the manufacturing process;

(b) means for receiving at least one observed value for the product characteristic of the at least one product and determining a variance between the at least one observed value and the at least one predicted value;

(c) calculating means for determining a first portion of the variance caused by uncontrollable error and a second portion of the variance caused by controllable error;

(d) calculating means for updating a process threshold using either the first or second portion of the variance; and

(e) means for adjusting at least one control parameter of the manufacturing process using the updated process threshold;

wherein the manufacturing process comprises at least one device on which the at least one product is processed, the at least one device being affected by the at least one control parameter.

25. The system of claim 24, wherein the variance is determined from the at least one observed values for N previously processed products.

26. The system of claim 24, wherein the first portion of the variance includes at least one of random variance, normal deviation, and an ambient fluctuation.

27. The system of claim 24, wherein the calculating means for determining a first and second portions of the variance defines a relationship between the first and second portions of the variance as:

V_x=ρ

₁²*V_x+V_w

where w=white noise

x=systematic error

V_x=variance of the controllable error

V_w=variance of the uncontrollable error

ρ

₁ is an autocorrelation factor for a lag of 1.

28. The system of claim 24, wherein the calculating means for determining a first and second portions of the variance defines a relationship between the first and second portions of the variance as:

δ

_x=y/(1+z),

where is δ

_x represents the second portion of the variance,

y=calculated standard deviation from N previous products, and

z=(1−

ρ

₁²)^0.5, where ρ

₁ is an autocorrelation factor for a lag of 1.

29. The system of claim 24, wherein the calculating means for determining a first and second portions of the variance determines the first and second portions of the variance using an auto-regressive stochastic sequence.

30. The system of claim 24, further comprising:

calculating means for updating the at least one control parameter of the manufacturing process using the first portion of the variance.

31. The system of claim 30, wherein the calculated first portion of the variance is used as a weighing factor to adjust an estimated gain in the updating of the at least one control parameter.