MASK3D model accuracy enhancement for small feature coupling effect

• US 9,348,964 B2
• Filed: 04/21/2014
• Issued: 05/24/2016
• Est. Priority Date: 04/21/2014
• Status: Active Grant

• Current Assignee: Synopsys Incorporated
• Original Assignee: Synopsys Incorporated
• Inventors: Zhang, Hongbo, Yan, Qiliang
• Primary Examiner(s): Chiang, Jack
• Assistant Examiner(s): Bowers, Brandon
• Application Number:
  US14/257,798
• Publication Number:
  US 20150302132A1
• Time in Patent Office: 764 Days
• Field of Search: 716/53
• 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: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• 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
• CPC Class Codes: H01L 21/027 Making masks on semiconductor bodies for further photolithographic processing not provided for in group H01L21/18 or H01L21/34 photographic masks or originals per se G03F1/00; registration or positioning of photographic masks or originals G03F9/00; photographic cameras G03B; control of position G05D3/00

Abstract

A method and apparatus of a novel full chip edge-based mask three-dimensional (3D) model for performing photolithography simulation with consideration for edge coupling effect is described. The method receives a mask design layout in order to perform mask topography effect modeling. The method generates scaling parameters for edge coupling effects. Each scaling parameter has an associated combination of feature width and space. The sum of feature width and space associated with at least one scaling parameter is less than a minimum pitch. The method applies a thick mask model that includes several edge-based kernels to the mask design layout to create a mask 3D residual. To apply the thick mask model to the mask design layout, the method updates the edge-based kernels with the scaling parameters.

Claims

1. A method of performing mask topography effect modeling on a mask design layout for integrated circuit design, the method comprising, upon receiving the mask design layout:

generating scaling parameters for edge coupling effects, each scaling parameter having an associated combination of feature width and space, wherein a sum of feature width and space associated with at least one scaling parameter is less than a minimum pitch; and

using a computing device, applying a thick mask model comprising a plurality of edge-based kernels to the mask design layout to create a mask 3D residual, comprising updating the plurality of edge-based kernels with the scaling parameters.

2. The method of claim 1, wherein the applying of the thick mask model further comprises:

applying a rasterization filter to the plurality of edge-based kernels to generate a plurality of filtered kernels; and

applying the plurality of filtered kernels to edges in the mask design layout to create the 3D mask residual.

3. The method of claim 2, wherein generating the scaling parameters comprises:

generating a first plurality of periodic structures, for simulation;

collecting near-field data for the first plurality of periodic structures through rigorous simulation, each of the first plurality of periodic structures associated with a combination of feature width and space, wherein a sum of the feature width and the space is greater than or equal to the minimum pitch;

for each of the first plurality of periodic structures, calculating a set of first scaling parameters by performing linear regression based on the plurality of filtered kernels and the near-field data related to the periodic structure; and

storing the calculated first scaling parameters in a lookup table, each first scaling parameter indexed by a combination of feature width and space of a corresponding periodic structure.

4. The method of claim 3, wherein generating the scaling parameters further comprises:

collecting reference near-field data for a second plurality of periodic structures through rigorous simulation, each of the second plurality of periodic structures comprises a first set of components whose sum of feature width and space is less than the minimum pitch and a second set of components whose sum of feature width and space is greater than or equal to the minimum pitch;

extracting near-field data for the first set of components by subtracting near-field data for the second set of components from the reference near-field data;

for each of the second plurality of periodic structures, calculating a set of second scaling parameters by performing linear regression based on the plurality of filtered kernels and the near-field data for the first set of components of the periodic structure; and

storing the calculated second scaling parameters in the lookup table, each second scaling parameter indexed by a combination of feature width and space of the first set of components of a corresponding periodic structure.

5. The method of claim 4, wherein the set of scaling parameters includes scaling factors for TE polarization and TM polarization.

6. The method of claim 1, wherein the updating of the plurality of edge-based kernels comprises:

determining edge segments and adjacent feature width and space for each edge segment;

for each edge segment, determining a set of scaling parameters corresponding to the feature width and space combination for the edge segment;

for each edge segment, updating the plurality of edge-based kernels by applying the set of scaling parameters determined for the edge segment; and

for each edge segment, applying the plurality of updated kernels to reflect edge coupling effect.

7. The method of claim 6, wherein the determining of the adjacent feature width and space for each edge segment comprises performing a vertical scan and a horizontal scan using a scanline algorithm.

8. The method of claim 1 further comprising:

applying a thin mask model to the mask design layout to create a thin mask transmission; and

combining the thin mask transmission and the mask 3D residual to create a mask 3D transmission.

9. The method of claim 1 further comprising generating the thick mask model.

10. The method of claim 9, wherein the generating of the thick mask model comprises:

receiving a mask topography structure;

performing a rigorous simulation to simulate a near-field;

generating a reference mask 3D residual by subtracting a thin mask electric field from the near-field; and

deriving the plurality of edge-based kernels by decomposing the reference mask 3D residual.

11. The method of claim 10 further comprising obtaining the thin mask electric field through a rasterization with foreground and background transmission.

12. A data processing system for performing mask topography effect modeling on a mask design layout, the data processing system comprising:

a calibrator that is to generate scaling parameters for edge coupling effects, each scaling parameter having an associated combination of feature width and space, wherein a sum of feature width and space associated with at least one scaling parameter is smaller than a minimum pitch;

a coupling effect processor that is to update a plurality of edge-based kernels with the scaling parameters; and

a rasterization processor that is to apply a thick mask model comprising the updated edge-based kernels to the mask design layout to create a mask 3D residual.

13. The data processing system of claim 12, wherein the plurality of edge-based kernels is generated by the calibrator.

14. The data processing system of claim 12, wherein the calibrator comprises:

a large component scaling factor generator that is to generate a first set of scaling parameters for large edge-based components, wherein a sum of feature width and space for each large edge-based component is larger than or equal to the minimum pitch; and

a small component scaling factor generator that is to generate a second set of scaling parameters for small edge-based components, wherein a sum of feature width and space for each small edge-based component is smaller than the minimum pitch.

15. The data processing system of claim 14, wherein the small component scaling factor generator generates the second set of scaling parameters by using the first set of scaling parameters.

16. The data processing system of claim 12, wherein the scaling parameters includes scaling factors for transverse electric (TE) polarization and transverse magnetic (TM) polarization.

17. The data processing system of claim 12 further comprising a filter applicator that is to apply a rasterization filter to the plurality of edge-based kernels.

18. 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 mask topography effect modeling on a mask design layout, the computer program product comprising a computer readable program code comprising instructions for:

generating scaling parameters for edge coupling effects, each scaling parameter having an associated combination of feature width and space, wherein a sum of feature width and space associated with at least one scaling parameter is less than a minimum pitch; and

using a computing device, applying a thick mask model comprising a plurality of edge-based kernels to the mask design layout to create a mask 3D residual, comprising updating the plurality of edge-based kernels with the scaling parameters.

19. The computer program product of claim 18, wherein the instructions for generating the scaling parameters comprise instructions for:

collecting near-field data for a first plurality of periodic structures through rigorous simulation, each of the first plurality of periodic structures associated with a combination of feature width and space, wherein a sum of the feature width and the space is greater than or equal to the minimum pitch;

for each of the first plurality of periodic structures, calculating a set of first scaling parameters by performing linear regression based on the plurality of filtered kernels and the near-field data related to the periodic structure; and

storing the calculated first scaling parameters into a lookup table, each first scaling parameter indexed by a combination of feature width and space of a corresponding periodic structure.

20. The computer program product of claim 19, wherein the instructions for generating the scaling parameters further comprise instructions for:

collecting reference near-field data for a second plurality of periodic structures through rigorous simulation, each of the second plurality of periodic structures comprises a first set of components whose sum of feature width and space is less than the minimum pitch and a second set of components whose sum of feature width and space is greater than or equal to the minimum pitch;

extracting near-field data for the first set of components by subtracting near-field data for the second set of components from the reference near-field data;

for each of the second plurality of periodic structures, calculating a set of second scaling parameters by performing linear regression based on the plurality of filtered kernels and the near-field data for the first set of components of the periodic structure; and

storing the calculated second scaling parameters into the lookup table, each second scaling parameter indexed by a combination of feature width and space of the first set of components of a corresponding periodic structure.