Integration of lithography apparatus and mask optimization process with multiple patterning process

• US 8,819,601 B2
• Filed: 04/04/2012
• Issued: 08/26/2014
• Est. Priority Date: 04/04/2011
• Status: Active Grant

• Current Assignee: ASML Netherlands BV (ASML Holding NV)
• Original Assignee: ASML Netherlands BV (ASML Holding NV)
• Inventors: Chen, Luoqi, Ye, Jun, Chen, Hong
• Primary Examiner(s): Parihar, Suchin
• Application Number:
  US13/439,692
• Publication Number:
  US 20120254813A1
• Time in Patent Office: 874 Days
• Field of Search: 716/100, 716/54
• 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 7/70433 Layout for increasing efficiency or for compensating imaging errors, e.g. layout of exposure fields for reducing focus errors; Use of mask features for increasing efficiency or for compensating imaging errors
• CPC Class Codes: G03F 7/70466 Multiple exposures, e.g. combination of fine and coarse exposures, double patterning or multiple exposures for printing a single feature stitching G03F7/70475
• CPC Class Codes: G03F 7/705 Modelling or simulating from physical phenomena up to complete wafer processes or whole workflow in wafer productions
• CPC Class Codes: G03F 7/70508 Data handling in all parts of the microlithographic apparatus, e.g. handling pattern data for addressable masks or data transfer to or from different components within the exposure apparatus
• CPC Class Codes: G03F 7/707 Chucks, e.g. chucking or un-chucking operations or structural details
• CPC Class Codes: G06F 30/398 Design verification or optimisation, e.g. using design rule check [DRC], layout versus schematics [LVS] or finite element methods [FEM] optical proximity correction [OPC] design processes G03F1/36

Abstract

The present invention relates to lithographic apparatuses and processes, and more particularly to multiple patterning lithography for printing target patterns beyond the limits of resolution of the lithographic apparatus. A method of splitting a pattern to be imaged onto a substrate via a lithographic process into a plurality of sub-patterns is disclosed, wherein the method comprises a splitting step being configured to be aware of requirements of a co-optimization between at least one of the sub-patterns and an optical setting of the lithography apparatus used for the lithographic process. Device characteristic optimization techniques, including intelligent pattern selection based on diffraction signature analysis, may be integrated into the multiple patterning process flow.

Claims

1. A method of splitting a pattern to be imaged onto a substrate via a lithographic process into sub-patterns, wherein the method is implemented by a computer, the method comprising:

selecting representative clips for two or more sub-patterns into which the pattern can be split, the selection being settings of the lithographic apparatus and estimated imaging quality of each of the sub-patterns;

optimizing two or more optical settings for illumination from a lithographic apparatus used for the lithographic process, each optical setting being dynamically optimized for respectively imaging the two or more sub-patterns based on analysis of the representative clips; and

splitting, by the computer, the pattern into the two or more sub-patterns, the sub-patterns each comprising a subset of features from the pattern, the splitting step including co-optimizing the estimated imaging quality of each of the sub-patterns for the respective optimized optical settings.

2. The method of claim 1, wherein the splitting step comprises rule-based splitting, algorithm based splitting, or a combination of rule-based and algorithm-based splitting.

3. The method of claim 1, wherein the splitting and co-optimization are performed sequentially, in parallel, alternatively, or iteratively.

4. The method of claim 1, wherein the selecting step uses diffraction signature analysis to select the representative clips for the two or more sub-patterns, such that the co-optimized estimated imaging quality is obtained.

5. The method of claim 1, wherein the selecting step uses two-dimensional pattern clustering in spatial domain to select the representative clips for the two or more sub-patterns, such that the co-optimized estimated imaging quality is obtained.

6. The method of claim 1, wherein the selecting step uses one or both of frequency information and local spatial information of the pattern to select the representative clips for the two or more sub-patterns, such that the co-optimized estimated imaging quality is obtained.

7. The method of claim 1, wherein the sub-patterns are sequentially imaged on the substrate while being combined on the single layer of the substrate.

8. The method of claim 4, wherein the co-optimized estimated imaging quality is associated with a predefined process window for the selected two or more sub-patterns.

9. The method of claim 1, wherein optimizing the two or more optical settings includes optimizing one or more of:

setting and characteristics of an illumination source;

setting and characteristics of a projection optics system; and

, combined setting and characteristics of an illumination source and a projection optics system.

10. The method of claim 1, wherein selecting includes selecting the representative clips from a relatively larger set, wherein the representative clips adequately cover characteristic pattern features of the relatively larger set, such that the process of optimizing the optical settings is expedited.

11. The method of claim 1, wherein the splitting step includes generating rules for splitting the pattern based on the optimized optical settings, the generated rules that each of the two or more sub-patterns contain features that are configured to be within a resolution limit of the lithography apparatus.

12. The method of claim 1, wherein optical proximity correction (OPC) is performed to further optimize the sub-patterns after optical settings are fixed as a result of the co-optimization.

13. The method of claim 12, wherein the OPC is performed for full-chip optimization.

14. The method of claim 1, wherein hot spots and warm spots are identified during a verification process that follows at least one iterative cycle of co-optimization.

15. The method of claim 14, wherein the identified hot spots and warm spots are fed back into the selecting step.

16. The method of claim 14, wherein the identified hot spots and warm spots are fed back into the selecting step and wherein the representative clips are selected from a relatively larger set, wherein the representative clips adequately cover characteristic pattern features of the relatively larger set, such that the process of optimizing the optical settings is expedited.

17. A computer program product comprising one or more non-transitory computer-readable storage media having computer-executable instructions for causing a computer to select test patterns for calibrating a computational lithography model, the instructions causing the computer to perform the method of claim 1.

18. A method implemented by a computer, comprising:

identifying a design layout, the design layout comprising a full set of features for a layer of an integrated circuit;

selecting a representative set of clips from the design layout, the selection being based on requirements for co-optimizin both of optical settings of the lithographic apparatus and estimated imaging quality of each of the sub-patterns;

splitting the representative set of clips into two or more sub-layouts;

optimizing, using the computer, respective source configurations, each source configuration being dynamically optimized for respectively imaging the sub-layouts comprising the representative set of clips; and

co-optimizing the design layout by splitting the design layout into two or more sub-patterns for being imaged using the optimized respective source configurations, such that an estimated imaging quality for imaging the sub-patterns is optimized.

19. The method of claim 18, wherein selecting comprises using diffraction signature analysis to identify diffraction-signature groups.

20. The method of claim 18, wherein selecting comprises using two-dimensional pattern clustering in spatial domain to identify topologically similar groups of features.