Dynamic offset and feedback threshold

• US 6,961,626 B1
• Filed: 05/28/2004
• Issued: 11/01/2005
• Est. Priority Date: 05/28/2004
• Status: Expired due to Fees

• Current Assignee: Applied Materials Incorporated
• Original Assignee: Applied Materials Incorporated
• Inventors: Paik, Young Jeen
• Primary Examiner(s): Picard, Leo
• Assistant Examiner(s): JARRETT, RYAN A
• Application Number:
  US10/855,711
• Time in Patent Office: 522 Days
• Field of Search: 700 28- 34, 700/44, 700/45, 700/51, 700108-110, 702 81- 84
• US Class Current: 700/31
• 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 determining a process threshold for updating a process model in a manufacturing process, comprising the steps of:

(a) inputting a model for a manufacturing process having at least one control parameter that predicts a value for a product characteristic and that sets a process threshold for modifying the model;

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

(c) using the variance to calculate a first portion of the variance caused by white noise and a second portion of the variance caused by a systematic variance over the plurality of products; and

(d) using either the first or second portion of the variance to update the process threshold of the model.

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

3. The method of claim 1, wherein the first portion represents the variance caused by white noise.

4. The method of claim 1, wherein the first portion of the variance caused by white noise is used to update the process threshold of the model.

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

using the first portion of the variance caused by white noise to update the at least one control parameter of the model.

6. The method of claim 5, wherein the calculated white noise 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.

7. The method of claim 1, wherein the model comprises two or more control parameters.

8. The method of claim 1, wherein first and second portions of the variance are determined using an auto-regressive stochastic sequence.

9. The method of claim 1, wherein the relationship between the first and second portions of the variance is defined as:

V_x=ρ

₁²*V_x+V_w

where w=white noise

x=systematic error

V_x=variance of the systematic error

V_w=variance of the white noise.

10. The method of claim 1, wherein the relationship between the first and second portions of the variance is defined 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.

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

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

13. The method of claim 1, wherein the manufacturing process includes at least one device on which the plurality of products 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 a step of controlling the at least one control parameter includes affecting the at least one device.

14. The method of claim 13, wherein the manufacturing 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.

15. The method of claim 3, wherein the white noise includes at least one of random variance, normal deviation, and an ambient fluctuation.

27. The method of claim 1, further comprising the steps of:

observing the value for each product of the plurality of products; and

determining whether or not to perform the controlling step for each product when the observed value is outside the process threshold.

16. A computer program product for determining a process threshold for updating a process recipe in a manufacturing process for a plurality of products, 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 inputting a model for a manufacturing process having at least one control parameter that predicts a value for a product characteristic and that sets a process threshold for modifying the model;

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

(d) instructions, provided on the at least one computer readable medium, for using the variance in an auto-regressive stochastic sequence to calculate a first portion of the variance caused by white noise and a second portion of the variance caused by a systematic variance over the plurality of products; and

(e) instructions, provided on the at least one computer readable medium, for updating the process threshold based upon the first portion of the variance caused by white noise.

17. The computer program product of claim 16, wherein the values are observed for products including semi-conductor wafers, and the computer readable medium is readable by a manufacturing process including an automated semi-conductor manufacturing process.

18. The computer program product of claim 16, wherein the 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.

19. The computer program product of claim 16, wherein the manufacturing 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 a manufacturing process based on the calculated white noise portion of the variance.

20. The computer program product of claim 16, further comprising instructions, on the computer readable medium, for observing the value for each product of the plurality of products;

determining at least the white noise portion of the variance for each product of the plurality of products and utilizing the white noise portion of the variance as a threshold; and

determining whether or not to execute the controlling instructions for each product when the measured value is outside the threshold.

21. The computer program product of claim 20, wherein the manufacturing 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.

22. The computer program product of claim 21, wherein the manufacturing 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.

23. The computer program product of claim 16, wherein the white noise includes at least one of random variance, normal deviation, and an ambient fluctuation.

24. The computer program product of claim 16, wherein the relationship between the first and second portions of the variance is defined as:

V_x=ρ

₁²*V_x+V_w

where w=white noise

x=systematic error

V_x=variance of the systematic error

V_w=variance of the white noise.

25. The computer program product of claim 16, wherein the relationship between the first and second portions of the variance is defined 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.

26. A system for determining a process threshold for updating a process model in a manufacturing process, comprising the steps of:

(a) means for modeling a manufacturing process having at least one control parameter that predicts a value for a product characteristic and that sets a process threshold for modifying the model;

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

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

(d) calculating means for updating the process threshold of the model using either the first or second portion of the variance.