Integrated mask-aware lithography modeling to support off-axis illumination and multi-tone masks

• US 9,354,511 B2
• Filed: 12/06/2013
• Issued: 05/31/2016
• Est. Priority Date: 12/06/2013
• Status: Active Grant

• Current Assignee: Synopsys Incorporated
• Original Assignee: Synopsys Incorporated
• Inventors: Zhang, Hongbo, Yan, Qiliang
• Primary Examiner(s): DOAN, NGHIA M
• Application Number:
  US14/099,885
• Publication Number:
  US 20150161302A1
• Time in Patent Office: 907 Days
• Field of Search: 716 50- 55, 430/5, 430/30
• 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 7/00 Photomechanical, e.g. photolithographic, production of textured or patterned surfaces, e.g. printing surfaces; Materials therefor, e.g. comprising photoresists; Apparatus specially adapted therefor using photoresist structures for special production processes, see the relevant places, e.g. B44C, H01L, e.g. H01L21/00, H05K
• 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 7/70283 Mask effects on the imaging process
• 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 17/11 for solving equations , e.g. nonlinear equations, general mathematical optimization problems optimization specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30

Abstract

A method and apparatus of a novel modeling scheme for performing optical lithography simulation for a multi-tone mask with a plurality of mask tones is described. The method generates a transmission function matrix based on a setting of the multi-tone mask. The method applies the transmission function matrix to transform a formula for calculating light intensity from Abbe'"'"'s form to Hopkins'"'"' form while maintaining the accuracy of Abbe'"'"'s form. The method then computes the light intensity using the transformed formula.

Claims

1. A method of performing optical lithography simulation implemented in a computer upon receipt of a multi-tone mask having a plurality of mask tones, the method comprising:

generating, using a computing device, a function with polarization having a portion representing a tone mask matrix and a portion representing a characteristic transmission (CT) function matrix based on the multi-tone mask, wherein the CT function matrix is a function of light source point information and not a function of mask geometry information; and

generating one or more convolution kernel for the multi-tone mask from the CT function for use in a Hopkins'"'"' form for calculating light intensity for the optical lithography simulation.

2. The method of claim 1, wherein the generating of the CT function matrix comprises transforming mask transmission under each light source point to a linear combination of a plurality of Zernike functions.

3. The method of claim 2, wherein the CT function matrix is given for any light source point information, without need for interpolation.

4. The method of claim 1, wherein the

generating the one or more convolution kernels for the multi-tone mask from the CT function matrix further comprises:

reducing the one or more generated convolution kernels; and

generating model data from the one or more reduced convolution kernels.

5. The method of claim 4, wherein reducing the one or more generated convolution kernels comprises:

generating a set of Eigen-kernels for each mask tone of the multi-tone mask based on the one or more convolution kernels.

6. The method of claim 4, wherein reducing the one or more generated convolution kernels comprises:

reducing a first set and a second set of Eigen-kernels so that the first set of Eigen-kernels is larger than the second set of Eigen-kernels.

7. The method of claim 6, wherein the first set of Eigen-kernels is used for major calculation and the second set of Eigen-kernels is used for accuracy enhancement.

8. The method of claim 6 further comprising saving the reduced first set and second set of Eigen-kernels into the model data.

9. A data processing system for performing optical lithography simulation upon receipt of a multi-tone mask having a plurality of mask tones, the data processing system comprising:

a function with polarization generator to generate a tone mask matrix and a characteristic transmission (CT) function matrix based on a the multi-tone mask, wherein the CT function matrix is a function of light source point information and not a function of mask geometry information; and

a kernel generator to generate a set of one or more convolution kernels based on the CT function matrix, wherein the one or more convolution kernels include information for the light source and is used in a Hopkins form to calculate light intensity for the optical lithography simulation.

10. The data processing system of claim 9, wherein the function with polarization generator generates the CT function matrix by transforming mask transmission under each light source point to a linear combination of a plurality of Zernike functions.

11. The data processing system of claim 9, further comprising:

an Eigen-kernel generator to generate a set of Eigen-kernels for each mask tone based on the set of one or more convolution kernels.

12. The data processing system of claim 11, wherein the Eigen-kernel generator is configurable to apply singular value decomposition (SVD) to one or more convolution kernels to generate a plurality of retained convolution kernels, wherein each convolution kernel corresponds to a tone mask and has an ordered set of kernels.

13. The data processing system of claim 11, further comprising:

an kernel reducer for reducing the set of Eigen-kernels.

14. The data processing system of claim 13, wherein the kernel reducer reduces a second set of Eigen-kernels so that the set of Eigen-kernels is larger than the second set of Eigen-kernels.

15. The data processing system of claim 14, wherein the set of Eigen-kernels is used for major calculation and the second set of Eigen-kernels is used for accuracy enhancement.

16. The data processing system of claim 15, wherein the Eigen-kernel reducer further records the reduced set and second set of Eigen-kernels into a model data.

17. A computer program product stored as program code on a non-transitory computer-readable medium, the program code executable by at least one processor for performing optical lithography simulation for a multi-tone mask with a plurality of mask tones, the computer program product comprising a computer readable program code comprising instructions for a method of performing optical lithography simulation implemented in a computer upon receipt of a multi-tone mask having a plurality of mask tones, the method comprising:

generating, using a computing device, a function with polarization having a portion representing a tone mask matrix and a portion representing a characteristic transmission (CT) function matrix based the multi-tone mask, wherein the CT function matrix is a function of light source point information and not a function of mask geometry information; and

generating one or more convolution kernels for the multi-tone mask from the CT function for use in a Hopkins'"'"' form for calculating light intensity for the optical lithography simulation.

18. The computer program product of claim 17, wherein generating the CT function matrix comprises transforming mask transmission under each light source point to a linear combination of a plurality of Zernike functions.

19. The computer program product of claim 18, wherein the CT function matrix is given for any light source point information, without need for interpolation.

20. The computer program product of claim 17, wherein

generating the one or more convolution kernels for the multi-tone mask comprises:

reducing the one or more generated convolution kernels; and

generating model data from the one or more reduced convolution kernels.

21. The computer program product of claim 20, wherein reducing the one or more generated convolution kernels comprises:

generating a set of Eigen-kernels for each mask tone based on the set-ef one or more convolution kernels.