Adaptive real time control of a reticle/mask system

• US 20050167514A1
• Filed: 01/30/2004
• Published: 08/04/2005
• Est. Priority Date: 01/30/2004
• Status: Active Grant

• Current Assignee: Tokyo Electron Limited
• Original Assignee: Tokyo Electron Limited
• Inventors: Kaushal, Sanjeev, Sugishima, Kenji, Pandey, Pradeep
• Granted Patent: US 7,025,280 B2
• Time in Patent Office: Days
• US Class Current: 236/1.C
• CPC Class Codes: G03F 1/68 Preparation processes not covered by groups G03F1/20 - G03F1/50
• CPC Class Codes: G03F 1/78 by charged particle beam [CPB], e.g. electron beam patterning of masks
• CPC Class Codes: H01L 21/67248 Temperature monitoring

Abstract

An adaptive real time thermal processing system is presented that includes a multivariable controller. Generally, the method includes creating a dynamic model of the thermal processing system; incorporating reticle/mask curvature in the dynamic model; coupling a diffusion-amplification model into the dynamic thermal model; creating a multivariable controller; parameterizing the nominal setpoints into a vector of intelligent setpoints; creating a process sensitivity matrix; creating intelligent setpoints using an efficient optimization method and process data; and establishing recipes that select appropriate models and setpoints during run-time.

Claims

1. A method of operating a thermal processing system comprising:

positioning a reticle/mask on a heating apparatus comprising a plurality of segments;

creating a dynamic thermal model of the system;

establishing a plurality of intelligent setpoints using the dynamic thermal model of the system, wherein at least one intelligent setpoint is created for each segment of the heating apparatus; and

controlling an actual temperature of each segment using the at least one intelligent setpoint and thereby establishing a controlled temperature profile across the reticle/mask.

2. The method as claimed in claim 1, further comprising:

receiving feed forward data that includes information of properties or parameters of a reticle/mask to be positioned on the heating apparatus;

estimating reticle/mask stresses using the feed forward data;

creating a thermal model for a gap between the reticle/mask and the heating apparatus, wherein the thermal response for the gap is predicted based on the estimated reticle/mask stresses; and

incorporating the thermal model for the gap into the dynamic thermal model of the system.

3. The method as claimed in claim 2, wherein the estimating of the reticle/mask stresses comprises using refractive index (n) data and extinction coefficient (k) data extracted from the feed forward data.

4. The method as claimed in claim 2, wherein the feed forward data comprises layer information including at least one of the number of layers, layer position, layer composition, layer uniformity, layer density, and layer thickness.

5. The method as claimed in claim 2, wherein the feed forward data includes at least one of critical dimension (CD) data, profile data, and uniformity data for the reticle/mask.

6. The method as claimed in claim 2, wherein the feed forward data includes at least one of critical dimension (CD) data for a plurality of locations on the reticle/mask, profile data for a plurality of locations on the reticle/mask, and uniformity data for a plurality of locations on the reticle/mask.

7. The method as claimed in claim 6, wherein the plurality of locations is non-radially positioned on the reticle/mask.

8. The method as claimed in claim 6, wherein the plurality of locations is radially positioned on the reticle/mask.

9. The method as claimed in claim 1, further comprising:

examining a real-time response of the reticle/mask and the heating apparatus;

estimating reticle/mask stresses using the real-time response;

creating a thermal model for a gap between the reticle/mask and the heating apparatus, wherein a thermal response for the gap is predicted based on the estimated reticle/mask stresses; and

incorporating the thermal model for the gap into the dynamic thermal model of the system.

10. The method as claimed in claim 1, further comprising:

estimating reticle/mask curvature;

creating a thermal model for a gap between the reticle/mask and the heating apparatus, wherein a thermal response for the gap is predicted based on the estimated reticle/mask curvature; and

incorporating the thermal model for the gap into the dynamic thermal model of the system.

11. The method as claimed in claim 1, further comprising:

modeling a thermal interaction between the segments of the heating apparatus; and

incorporating the model for the thermal interaction into the dynamic thermal model of the system.

12. The method as claimed in claim 1, further comprising:

creating a virtual sensor for estimating a temperature for the reticle/mask; and

incorporating the virtual sensor into the dynamic thermal model of the system.

13. The method as claimed in claim 1, further comprising:

modeling a thermal interaction between the heating apparatus and an ambient environment; and

incorporating the model for the thermal interaction into the dynamic thermal model of the system.

14. The method as claimed in claim 1, further comprising:

creating a diffusion-amplification model; and

incorporating the diffusion-amplification model into the dynamic thermal model of the system.

15. The method as claimed in claim 1, further comprising:

creating a variation vector d, wherein the variation vector comprises differences between measurement data and a desired value;

parameterizing at least one nominal setpoint into a vector r comprising at least one intelligent setpoint;

creating a sensitivity matrix using the dynamic thermal model; and

determining the at least one intelligent setpoint by solving an optimization problem comprising

$\underset{r}{\min }<br><br><br><br>d-\mathrm{\alpha <br><br>}·<br><br>\mathrm{Mr}<br><br>,$

wherein r_min<

r, r<

r_max, r is a vector comprising the at least one intelligent setpoint, M is the sensitivity matrix, □

is a proportionality constant relating the measurement data to the sensitivity matrix M, and d is the variation vector.

16. The method as claimed in claim 15, further comprising:

updating a recipe with the at least one determined intelligent setpoint;

running the updated recipe;

obtaining updated measurement data; and

iterating until a desired uniformity is achieved.

17. The method as claimed in claim 16, wherein the desired uniformity comprises a 3-sigma variation of less than approximately one percent.

18. The method as claimed in claim 17, wherein the desired uniformity comprises a 3-sigma variation of less than approximately one-half percent.

19. The method as claimed in claim 15, further comprising:

receiving feed forward data;

obtaining the measurement data from the feed forward data, wherein the measurement data comprises at least one of critical dimension measurements, profile measurements, and uniformity measurements; and

determining the desired value, wherein the desired value comprises at least one of desired critical dimension, a desired profile, and a desired uniformity.

20. The method as claimed in claim 15, further comprising:

executing a process using a recipe having at least one nominal setpoint for each segment of the heating apparatus;

obtaining the measurement data from the executed process, wherein the measurement data comprises at least one of critical dimension measurements, profile measurements, and uniformity measurements; and

determining the desired value, wherein the desired value comprises at least one of desired critical dimension, a desired profile, and a desired uniformity.

21. The method as claimed in claim 15, further comprising:

making temperature perturbations for each segment of the heating apparatus; and

establishing the sensitivity matrix M using results of the temperature perturbations.

22. The method as claimed in claim 15, further comprising using an instrumented reticle/mask to establish the sensitivity matrix M.

23. The method as claimed in claim 15, further comprising:

determining a vector D of a thermal dose (reaction supply) at each radial element location, wherein

$D=\left[\begin{array}{c}{D}_1\\ \mathrm{⋮<br><br>}\\ D_n\end{array}\right];<br><br>\mathrm{and}$

characterizing perturbations in the thermal dose (reaction supply) as

$\left[\begin{array}{c}{d}_1\\ \mathrm{⋮<br><br>}\\ d_n\end{array}\right]=M<br><br>\left[\begin{array}{c}{r}_1\\ \mathrm{⋮<br><br>}\\ r_m\end{array}\right];<br><br>\mathrm{and}$

determining values of the vector r, such that the vector d removes across reticle/mask variations in the vector D.

24. The method as claimed in claim 16, further comprising:

storing the updated recipe, when the desired CD uniformity is achieved.

25. The method as claimed in claim 1, wherein:

the positioning of the reticle/mask on a heating apparatus comprises positioning the mask on the heating apparatus to be processed by the system.

26. The method as claimed in claim 1, wherein:

the positioning of the reticle/mask on a heating apparatus comprises positioning the mask on the heating apparatus to process a substrate therewith by the system.

29. The method as claimed in claim 1, wherein the heating apparatus comprises a square shape and the plurality of segments comprise a number of uniformly spaced square shaped segments.

30. The method as claimed in claim 29, wherein each segment comprises a heating element for heating or cooling to establish the corresponding actual temperature.

31. The method as claimed in claim 30, wherein the heating element comprises a resistive heater.

32. The method as claimed in claim 30, wherein the heating apparatus further comprises a cooling element.

33. The method as claimed in claim 1, wherein the heating apparatus comprises a rectangular shape, and the plurality of segments comprise a plurality of uniformly spaced rectangular segments.

34. The method as claimed in claim 1, wherein at least one segment comprises a sensor element for measuring a temperature of the heating apparatus.

27. A thermal processing system comprising:

means for positioning a reticle/mask being processed by the system on a heating apparatus comprising a plurality of segments;

means for creating a dynamic thermal model of the system;

means for establishing a plurality of intelligent setpoints using the dynamic thermal model of the system, wherein at least one intelligent setpoint is created for each segment of the heating apparatus; and

means for controlling an actual temperature of each segment using the at least one intelligent setpoint and thereby establish a predetermined temperature profile across the reticle/mask.

28. A processing system comprising:

a temperature controlled apparatus comprising a plurality of segments;

structure to support a reticle/mask on the temperature controlled apparatus to be processed thereon by the system or to process a wafer therewith by the system;

a controller programmed to provide a dynamic thermal model of the system and to establish therewith a plurality of intelligent setpoints for the segments of the temperature controlled apparatus; and

the actual temperature of each segment of the temperature controlled apparatus being responsive to at least one intelligent setpoint.

35. The processing system of claim 28 wherein:

the temperature controlled apparatus further comprises at least one physical sensor operative to input to the controller at least one variable parameter of the system;

the controller programmed to maintain the dynamic thermal model that includes calculated temperature data of the system and the reticle/mask to calculate the intelligent setpoints to control the segments of the temperature controlled apparatus to maintain the reticle/mask at a predetermined temperature distribution. L