Determining the gradient and hessian of the image log slope for design rule optimization for accelerating source mask optimization (SMO)

• US 8,356,261 B1
• Filed: 07/02/2010
• Issued: 01/15/2013
• Est. Priority Date: 07/02/2009
• Status: Active Grant

• Current Assignee: ASML Netherlands BV (ASML Holding NV)
• Original Assignee: ASML Netherlands BV (ASML Holding NV)
• Inventors: Socha, Robert John
• Primary Examiner(s): Siek, Vuthe
• Assistant Examiner(s): Alam, Mohammed
• Application Number:
  US12/830,258
• Time in Patent Office: 928 Days
• Field of Search: 716/53, 716/54, 716/55
• US Class Current: 716/54
• CPC Class Codes: G03F 1/70 Adapting basic layout or design of masks to lithographic process requirements, e.g., second iteration correction of mask patterns for imaging
• CPC Class Codes: G03F 7/705 Modelling or simulating from physical phenomena up to complete wafer processes or whole workflow in wafer productions

Abstract

The Hessian (second derivative) of the image log slope (ILS) can be quickly and accurately calculated without the need to use approximate methods from the gradient of the ILS with respect to mask transmission and source intensity. The Hessian has been traditionally calculated using a finite-difference approach. Calculating the Hessian through a finite-difference approach is slow and is an approximate method. The gradient of the ILS improves the speed of calculation of the Hessian, and thus accelerated SMO operation is realized. The results of ILS evaluation can be used in design for manufacturing (DFM) to suggest changes in the design rules to improve imaging. For a fixed illumination, this information can help remove forbidden pitches and help select design rules for 1-D and 2-D patterns on a mask design layout.

Claims

1. A computer-implemented method for optimizing a mask pattern and an illumination source, the method comprising:

identifying a plurality of source points of an illumination source and a predetermined mask pattern;

selecting evaluation points in an image plane of an image formed by illumination provided to the predetermined mask pattern by the illumination source;

determining an image log slope (ILS) of illumination at each evaluation point;

determining the Hessian of the image log slope, wherein the Hessian comprises a second order partial derivative with respect to two different variables associated with the mask pattern and the illumination source; and

using the Hessian for determining at least one of an improved mask pattern and an improved illumination source which maximizes the image log slope at the selected evaluation points,

wherein at least some of the above functions are performed by a computer.

2. The method of claim 1, wherein the Hessian of the ILS is determined with respect to mask transmission and source intensity.

3. The method of claim 1, wherein the Hessian of ILS is determined without using approximate methods based on the gradient of the ILS.

4. The method of claim 1, wherein the Hessian of the ILS is determined with respect to an illumination pupil and a mask pixel.

5. The method of claim 1, wherein a sub-resolution assist feature is added to the mask pattern to improve the ILS.

16. The method of claim 1, wherein the Hessian indicates whether to optimize the mask pattern, the illumination source, or both, to maximize the ILS.

17. The method of claim 1, wherein the optimizing of the illumination source occurs simultaneously with optimizing the mask pattern.

6. A non-transitory computer readable medium containing instructions for a computer to cause optimizing a mask pattern and an illumination source, the process of optimizing comprising:

identifying a plurality of source points of an illumination source and a predetermined mask pattern;

selecting evaluation points in an image plane of an image formed by illumination provided to the predetermined mask pattern by the illumination source;

determining an image log slope (ILS) of illumination at each evaluation point;

determining the Hessian of the image log slope, wherein the Hessian comprises a second order partial derivative with respect to two different variables associated with the mask pattern and the illumination source; and

using the Hessian for determining at least one of an improved mask pattern and an improved illumination source which maximizes the image log slope at the selected evaluation points.

7. The computer readable medium of claim 6, wherein the Hessian of the ILS is determined with respect to mask transmission and source intensity.

8. The computer readable medium of claim 6, wherein the Hessian of ILS is determined without using approximate methods based on the gradient of the ILS.

9. The computer readable medium of claim 6, wherein the Hessian of the ILS is determined with respect to an illumination pupil and a mask pixel.

10. The computer readable medium of claim 6, wherein a sub-resolution assist feature is added to the mask pattern to improve the ILS.

18. The computer readable medium of claim 6, wherein the Hessian indicates whether to optimize the mask pattern, the illumination source, or both, to maximize the ILS.

19. The computer readable medium of claim 6, wherein the optimizing of the illumination source occurs simultaneously with optimizing the mask pattern.

11. A computer-implemented method of generating a design rule defining relative positioning of polygons in an integrated circuit design, the design rule being usable for producing a design layout for a mask used in a lithographic apparatus, the method comprising the steps of:

identifying one or more source points of an illumination source;

identifying a target pattern in a design layout of the mask;

identifying one or more evaluation points of an image of the target pattern formed in an image plane when illuminated by the illumination source;

calculating an image log slope (ILS) function of an intensity profile at each evaluation point;

calculating a second order partial derivative of the ILS function; and

using the second order partial derivative of the ILS function with respect to two different variables associated with one or both of the illumination source and the target pattern to update the design-rule for the target pattern,

wherein at least some of the above steps are performed by a computer.

12. The method of claim 11, wherein calculating the second order partial derivative of the ILS function includes calculating one or more of the following:

calculating variation of the ILS function with respect to two source points;

calculating variation of the ILS with respect to electric field transmission at two pixel points on the design layout of the mask; and

calculating variation of the ILS function with respect to one source point and electric field transmission at one pixel point on the design layout of the mask.

13. The method of claim 11, where the evaluation points are selected based on a fragmentation algorithm run on the target pattern that identifies fragmentation points.

14. The method of claim 11, wherein updating the design rule comprises repositioning edges of target pattern features to maximize ILS or minimize ILS variation.

15. The method of claim 11, wherein the method further comprises:

optimizing the illumination source based on the updated design rule.

20. The method of claim 15, wherein the optimizing of the illumination source occurs simultaneously with optimizing the design layout of the mask using the generated design rule.