Process control by distinguishing a white noise component of a process variance

• US 7,096,085 B2
• Filed: 05/28/2004
• Issued: 08/22/2006
• Est. Priority Date: 05/28/2004
• Status: Expired due to Fees

• Current Assignee: Applied Materials Incorporated
• Original Assignee: Applied Materials (Holdings) (Applied Materials Incorporated)
• Inventors: Paik, Young Jeen
• Primary Examiner(s): Gandhi, Jayprakash N.
• Application Number:
  US10/856,016
• Publication Number:
  US 20050278051A1
• Time in Patent Office: 816 Days
• Field of Search: 700 28- 34, 700/44, 700/45, 700/51, 700108-110, 702/81, 702/84, 702182-184
• US Class Current: 700/108
• CPC Class Codes: G05B 13/041 in which a variable is automatically adjusted to optimise the performance

Abstract

A method, system and medium is provided for enabling improved control systems. An error, or deviation from a target result, is observed for example during manufacture of semiconductor 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, e.g., random noise and therefore is relatively non-controllable. The systematic error component, 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 systematic 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 the effects of run-to-run deviations, and provides a mechanism that can extract the white noise component from the statistical process variance in real time. This results in an ability to provide tighter control, for example in feedback and feedforward variations of process control.

Claims

1. A system, implemented on at least one computer, for compensating for a variance between a measured characteristic of at least one product produced by a process and a target result of the characteristic, the system comprising:

(a) means for receiving an observed value for at least one product, and receiving a target value for the at least one product;

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

(c) means for determining a first portion of the variance caused by white noise;

(d) means for determining a second portion of the variance caused by a systematic component; and

(e) means, in the at least one computer, for using the determined first and/or second portions to adjust the process.

2. The system of claim 1, wherein the at least one product is a semi-conductor wafer, and the process is an automated semiconductor manufacturing process.

3. The system of claim 1, wherein the target 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.

4. The system of claim 1, wherein the process has at least one control parameter capable of being controlled, further comprising means for controlling the at least one control parameter during the process based on the second portion of the variance.

5. The system of claim 4, further comprising means for determining the observed value for a plurality of products including the at least one product;

determining at least the second portion of the variance for the plurality of products and utilizing the second portion of the variance for the plurality of products as a threshold; and

determining whether or not to control the plurality of products when the observed value is outside the threshold.

6. The system of claim 4, wherein the process includes at least one device on which the plurality of products including the at least one product is processed, the observed value being relative to the at least one device, the at least one device including the at least one control parameter, wherein controlling the at least one control parameter includes affecting the at least one device.

7. The system of claim 4, wherein the process includes a plurality of devices including a first device and a second device on which the plurality of products including the at least one product are processed, the observed value being relative to the first device, the second device including the at least one control parameter, wherein controlling the at least one control parameter includes affecting at least the second device.

8. The system of claim 1, wherein the first portion and second portion are determined over a plurality of products.

9. The system of claim 1, wherein the first portion and second portion of the variance are calculated using an autoregressive stochastic sequence.

10. The system of claim 1, wherein the first portion and second portion of the variance are substantially determined by:

δ

_x=y/(1+z)

where

δ

_x=a value representing the variation that a system can control

y=calculated value from previous values

z=estimated gain adjustment.

11. The system of claim 1, wherein the target 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.

12. The system of claim 1, wherein the white noise includes at least one of random variance, normal deviation, and an ambient fluctuation.

20. The method of claim 3, wherein the first portion and second portion are determined over a plurality of products.

13. A computer-implemented method for compensating for a variance between a measured characteristic of at least one product produced by a process and a target result of the characteristic, comprising the steps of:

(a) receiving an observed value for at least one product, and receiving a target value for the at least one product;

(b) determining a variance between the observed value of at least one product and the target value of the at least one product;

(c) determining a first portion of the variance caused by white noise;

(d) determining a second portion of the variance caused by a systematic component; and

(e) using the determined first and/or second portions to adjust the process.

14. The method of claim 13, wherein the at least one product is a semiconductor wafer, and the process is an automated semiconductor manufacturing process.

15. The method of claim 13, wherein the target 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 method of claim 13, wherein the process has at least one control parameter capable of being controlled, further comprising the step of controlling the at least one control parameter during the process based on the second portion of the variance.

17. The method of claim 16, further comprising the steps of determining the observed value for a plurality of products including the at least one product;

determining at least the second portion of the variance for the plurality of products and utilizing the second portion of the variance for the plurality of products as a threshold; and

determining whether or not to perform the controlling step for the plurality of products when the observed value is outside the threshold.

18. The method of claim 16, wherein the process includes at least one device on which the plurality of products including the at least one product is processed, the observed value being relative to the at least one device, the at least one device including the at least one control parameter, wherein the step of controlling the at least one control parameter includes affecting the at least one device.

19. The method of claim 16, wherein the process includes a plurality of devices including a first device and a second device on which the plurality of products including the at least one product are processed, the observed value being relative to the first device, the second device including the at least one control parameter, wherein the step of controlling the at least one control parameter includes affecting at least the second device.

21. The method of claim 13, wherein the first portion and second portion of the variance are calculated using an autoregressive stochastic sequence.

22. The method of claim 13, wherein the first portion and second portion of the variance are substantially determined by:

δ

_x=y/(1+z)

where

δ

_x=a value representing the variation that a system can control

y=calculated value from previous values

z=estimated gain adjustment.

23. The method of claim 13, wherein the white noise includes at least one of random variance, normal deviation, and an ambient fluctuation.

24. A computer program product for use in compensating for a variance between a measured characteristic of at least one product produced by a process and a target result of the characteristic by differentiating a white noise component of the variance from a systematic component of the variance, the computer program product comprising:

(a) at least one computer readable medium, readable by the manufacturing process;

(b) instructions, provided on the at least one computer readable medium, for receiving an observed value for at least one product, and receiving a target value for the at least one product;

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

(d) instructions, provided on the at least one computer readable medium, for determining a first portion of the variance caused by white noise; and

(e) instructions, provided on the at least one computer readable medium, for determining a second portion of the variance caused by a systematic component; and

(f) instructions, provided on the at least one computer readable medium, for using the determined first and/or second portions to adjust the process.

25. The computer program product of claim 24, wherein the at least one product includes a semi-conductor wafer, and the computer readable medium is readable by a process including an automated semiconductor manufacturing process.

26. The computer program product of claim 24, wherein the target 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.

27. The computer program product of claim 24, wherein the process has at least one control parameter capable of being controlled, further comprising instructions, provided on the computer readable medium, for controlling the at least one control parameter during the process based on the second portion of the variance.

28. The computer program product of claim 27, further comprising instructions, on the computer readable medium, for determining the observed value for the plurality of products including the at least one product;

determining at least the second portion of the variance for the plurality of products and utilizing the second portion of the variance for the plurality of products as a threshold; and

determining whether or not to execute the controlling instructions for the at least one product when the observed value is outside the threshold.

29. The computer program product of claim 28, wherein the process includes at least one device on which the plurality of products including the at least one product is processed, the observed value being relative to the at least one device, the at least one device including the at least one control parameter, wherein the instructions for controlling the at least one control parameter includes affecting the at least one device.

30. The computer program product of claim 28, wherein the process includes a plurality of devices including a first device and a second device on which the plurality of products including the at least one product are processed, the observed value being relative to the first device, the second device including the at least one control parameter, wherein the instructions for controlling the at least one control parameter includes affecting at least the second device.

31. The computer program product of claim 24, wherein the first portion and the second portion are determined over a plurality of products.

32. The computer program product of claim 24, wherein the first portion and second portion of the variance are calculated using an autoregressive stochastic sequence.

33. The computer program product of claim 24, wherein the first portion and second portion of the variance are substantially determined by:

δ

_x=y/(1+z)

where

δ

_x=a value representing the variation that a system can control

y=calculated value from previous values

z=estimated gain adjustment.

34. The computer program product of claim 24, wherein the white noise includes at least one of random variance, normal deviation, and an ambient fluctuation.