Fast freeform source and mask co-optimization method

• US 9,111,062 B2
• Filed: 11/08/2013
• Issued: 08/18/2015
• Est. Priority Date: 11/21/2008
• Status: Active Grant

• Current Assignee: ASML Netherlands BV (ASML Holding NV)
• Original Assignee: ASML Netherlands BV (ASML Holding NV)
• Inventors: Chen, Luoqi, Ye, Jun, Cao, Yu
• Primary Examiner(s): Whitmore, Stacy
• Application Number:
  US14/075,917
• Publication Number:
  US 20140068530A1
• Time in Patent Office: 648 Days
• Field of Search: 716 50- 56
• US Class Current: 1/1
• CPC Class Codes: G03F 1/36 Masks having proximity correction features; Preparation thereof, e.g. optical proximity correction [OPC] design processes
• CPC Class Codes: G03F 7/70083 Non-homogeneous intensity distribution in the mask plane
• CPC Class Codes: G03F 7/70125 Use of illumination settings tailored to particular mask patterns details of setting means G03F7/70091
• CPC Class Codes: G03F 7/70441 Optical proximity correction [OPC]
• 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: G06F 30/39 Circuit design at the physical level physical level design for reconfigurable circuits G06F30/347
• 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 disclosure relates to lithographic apparatuses and processes, and more particularly to tools for optimizing illumination sources and masks for use in lithographic apparatuses and processes. According to certain aspects, the present disclosure significantly speeds up the convergence of the optimization by allowing direct computation of gradient of the cost function. According to other aspects, the present disclosure allows for simultaneous optimization of both source and mask, thereby significantly speeding the overall convergence. According to still further aspects, the present disclosure allows for free-form optimization, without the constraints required by conventional optimization techniques.

Claims

1. A computer-implemented method for optimizing a lithographic process in a simulation domain, the simulated lithographic process having data representing an illumination source and a mask, the method comprising:

performing, by the computer, a free-form optimization process;

placing, by the computer, sub-resolution assist feature (SRAF) seeds in a description of the mask based on a result of the free-form optimization process; and

performing, by the computer, a constrained optimization process, including growing the SRAF seeds while taking into account manufacturability constraints for both the illumination source and the mask.

2. The method of claim 1, wherein the free-form optimization process includes designing an optimal illumination source that comprises a fully flexible set of illumination source points.

3. The method of claim 2, wherein taking into account the manufacturability constraints for the illumination source includes matching the optimal illumination source to a diffractive optical element.

4. The method of claim 1, wherein taking into account the manufacturability constraints for the mask includes constraining a mask transmission to a predetermined value.

5. The method of claim 1, wherein the constrained optimization process includes iteratively converging a cost function, wherein the cost function comprises a function of both the illumination source and the mask.

6. The method of claim 5, wherein the cost function is formulated in terms of one of the following:

worst case edge placement error (EPE) over a given process window, EPE least square function, EPE least p-norm function, inverse NILS p-norm function, contour integral image slop, edge image value least square, edge image p-norm, and ILS p-norm.

7. The method of claim 5, where the convergence of the cost function is accelerated by directly computing a gradient of the cost function in each iteration with respect to the current data representing the mask and the source.

8. The method of claim 7, wherein the current data representing the source and the mask are reconfigured for each iteration, until the gradient is at a desired minimum value.

9. A computer program product comprising a non-transitory computer readable medium having instructions recorded thereon, the instructions, when executed by a computer, implements a method for optimizing a lithographic process in a simulation domain, the simulated lithographic process having data representing an illumination source and a mask, the method comprising:

performing a free-form optimization process;

placing sub-resolution assist feature (SRAF) seeds in a description of the mask based on a result of the free-form optimization process; and

performing a constrained optimization process, including growing the SRAF seeds while taking into account manufacturability constraints for both the illumination source and the mask.

10. The computer program product of claim 9, wherein the free-form optimization process includes designing an optimal illumination source that comprises a fully flexible set of illumination source points.

11. The computer program product of claim 10, wherein taking into account the manufacturability constraints for the illumination source includes matching the optimal illumination source to a diffractive optical element.

12. The computer program product of claim 9, wherein taking into account the manufacturability constraints for the mask includes constraining a mask transmission to a predetermined value.

13. The computer program product of claim 9, wherein the constrained optimization process includes iteratively converging a cost function, wherein the cost function comprises a function of both the illumination source and the mask.

14. The computer program product of claim 13, wherein the cost function is formulated in terms of one of the following:

worst case edge placement error (EPE) over a given process window, EPE least square function, EPE least p-norm function, inverse NILS p-norm function, contour integral image slop, edge image value least square, edge image p-norm, and ILS p-norm.

15. The computer program product of claim 13, where the convergence of the cost function is accelerated by directly computing a gradient of the cost function in each iteration with respect the current data representing the mask and the source.

16. The computer program product of claim 13, wherein the current data representing the source and the mask are reconfigured before each iteration until the gradient is at a desired minimum value.