Dynamic offset and feedback threshold

• US 20050267607A1
• Filed: 04/08/2005
• Published: 12/01/2005
• Est. Priority Date: 05/28/2004
• Status: Active Grant

• Current Assignee: Applied Materials Incorporated
• Original Assignee: Applied Materials Incorporated
• Inventors: Paik, Young Jeen
• Granted Patent: US 7,349,753 B2
• Time in Patent Office: Days
• 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 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 variable;

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

(d) using the value for uncontrollable error 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, further comprising the step of:

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

4. The method of claim 3, 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.

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

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

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

8. 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 the step of controlling the at least one control parameter includes affecting the at least one device.

9. The method of claim 8, further comprising determining whether or not to perform the controlling step for each product when the observed value is outside the process threshold.

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

15. The computer program product of claim 10, further comprising instructions, on the computer readable medium, for observing the value for each product of the plurality of products;

determining the uncontrollable error of the variance for each product of the plurality of products and utilizing the uncontrollable error 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.

16. The computer program product of claim 15, 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.

17. The computer program product of claim 15, 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.

11. A computer program product for determining a process threshold for 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 variable;

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

(e) instructions, provided on the at least one computer readable medium, for updating the process threshold based upon the uncontrollable error of the variance.

12. The computer program product of claim 11, 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.

13. The computer program product of claim 11, 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.

14. The computer program product of claim 11, 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 uncontrollable error of the variance.

18. The computer program product of claim 11, wherein the uncontrollable error of the variance includes at least one of random variance, normal deviation, and an ambient fluctuation.

19. A system for determining a process threshold for a process model in a manufacturing process, comprising:

(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 variable;

(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; and

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

20. The system of claim 19, wherein the variance is determined from the observed values for N previously processed products.

21. The system of claim 19, wherein the uncontrollable error of the variance includes at least one of random variance, normal deviation, and an ambient fluctuation.

22. The system of claim 19, wherein the calculating means defines a relationship between the first and second portions of the variance as:

V_xρ

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

23. The system of claim 19, wherein the calculating means 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.

24. The system of claim 19, wherein the calculating means determines the first and second portions of the variance using an auto-regressive stochastic sequence.

25. The system of claim 19, further comprising:

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

26. The system of claim 24, 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.

27. The system of claim 19, further comprising:

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.

28. The system of claim 19, further comprising:

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.