METHOD OF MODELING A MASK BY TAKING INTO ACCOUNT OF MASK PATTERN EDGE INTERACTION

• US 20180150578A1
• Filed: 05/10/2017
• Published: 05/31/2018
• Est. Priority Date: 11/29/2016
• Status: Active Grant

• Current Assignee: Taiwan Semiconductor Manufacturing Company Limited
• Original Assignee: Taiwan Semiconductor Manufacturing Company Limited
• Inventors: Lai, Chien-Jen, Zhou, Xin, Peng, Danping
• Granted Patent: US 10,671,786 B2
• Time in Patent Office: Days
• 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 7/70283 Mask effects on the imaging process
• 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 30/367 Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods

Abstract

A mask layout is received. An interaction-free mask model is applied to the mask layout. An edge interaction model is applied to the mask layout. The edge interaction model describes an influence due to a plurality of combinations of two or more edges interacting with one another. A thin mask model is applied to the mask layout. A near field is determined based on the applying of the interaction-free mask model, the applying of the edge interaction model, and the applying of the thin mask model.

Claims

1. A method, comprising:

receiving a mask layout;

applying an interaction-free mask model to the mask layout;

applying an edge interaction model to the mask layout, the edge interaction model describing an influence due to a plurality of combinations of two or more edges interacting with one another;

applying a thin mask model to the mask layout; and

determining a near field based on the applying of the interaction-free mask model, the applying of the edge interaction model, and the applying of the thin mask model.

2. The method of claim 1, further comprising:

applying the edge interaction model by computing the influence exerted to a plurality of pixels of the mask layout by the plurality of combinations of two or more edges.

3. The method of claim 2, wherein the applying the edge interaction model comprises:

dividing the mask layout into a plurality of tiles;

computing the edge interaction correction for each of the tiles; and

determining the total edge interaction correction for the entire mask layout by combining the edge interaction correction from each of the tiles.

4. The method of claim 3, wherein:

the computing the edge interaction correction comprises computing a plurality of kernels; and

the kernels each describe a correction field at an influenced pixel due to an interaction between two or more edge pixels each located on an edge of a pattern of the mask layout.

5. The method of claim 4, wherein the computing the plurality of kernels comprises computing each of the kernels as a function of:

a location of the influenced pixel, a respective location of each of the two or more edge pixels, and a respective orientation angle associated with each of the two or more edge pixels.

6. The method of claim 3, wherein the dividing comprises:

adding a margin of interaction length to each of the divided tiles.

7. The method of claim 1, wherein the mask layout contains one or more non-Manhattan patterns.

8. The method of claim 1, wherein the two or more edges interacting with one another are located within a predefined proximity to one another.

9. The method of claim 1, further comprising:

processing the mask layout, wherein the applying of the interaction-free mask model, the applying of the edge interaction model, and the applying of the thin mask model are performed to the processed mask layout.

10. The method of claim 9, wherein the processing the mask layout comprises performing rasterization or anti-aliasing filtering to the mask layout.

11. The method of claim 1, wherein the thin mask model contains binary modeling of patterns on the mask layout.

12. The method of claim 1, further comprising:

applying an optical model to the near field to obtain an aerial image on a wafer; and

applying a resist model to the aerial image to obtain a final resist image on the wafer.

13. A method, comprising:

receiving a mask layout, the mask layout including one or more non-Manhattan patterns;

applying an interaction-free mask model to the received mask layout;

generating an edge interaction model with a set of pre-selected mask layout samples, wherein the generating comprises determining an influence exerted to a plurality of pixels of the mask layout samples by a plurality of combinations of two or more edge segments of the one or more non-Manhattan patterns;

applying the edge interaction model to the received mask layout;

applying a thin mask model to the received mask layout; and

determining a near field of the received mask layout based on the applying of the interaction-free mask model, the applying of the edge interaction model, and the applying of the thin mask model.

14. The method of claim 13, wherein the generating the edge interaction model comprises:

dividing the mask layout samples into a plurality of tiles;

computing the edge interaction field for each of the tiles by computing a plurality of kernels, wherein the kernels each describe a correction field at an influenced pixel due to an interaction between two or more edge pixels each located on an edge of the one or more non-Manhattan patterns; and

determining the edge interaction kernels for a tile.

15. The method of claim 14, wherein the computing the plurality of kernels comprises computing each of the kernels as a function of:

a location of the influenced pixel, a respective location of each of the two or more edge pixels, and a respective orientation angle associated with each of the two or more edge pixels.

16. The method of claim 14, wherein the dividing comprises:

adding a margin of interaction length to each of the divided tiles.

17. The method of claim 13, further comprising:

processing the received mask layout at least in part by performing rasterization or anti-aliasing filtering to the received mask layout, wherein the applying of the interaction-free mask model, the applying of the edge interaction model, and the applying of the thin mask model are performed to the processed received mask layout;

applying an optical model to the near field to obtain an aerial image of the received mask layout on a wafer; and

applying a resist model to the aerial image to obtain a final resist image on the wafer.

18. A method, comprising:

receiving a mask layout, the mask layout including one or more non-Manhattan patterns;

processing the received mask layout;

applying an interaction-free mask model to the processed received mask layout;

generating an edge interaction model with a set of pre-selected mask layout samples, wherein the generating comprises computing a plurality of kernels that each describe a correction field at an influenced pixel due to an interaction between two or more edge pixels each located on an edge of the one or more non-Manhattan patterns;

applying the edge interaction model to the processed received mask layout;

applying a thin mask model to the processed received mask layout;

determining a near field of the received mask layout based on the applying of the interaction-free mask model, the applying of the edge interaction model, and the applying of the thin mask model;

applying an optical model to the near field to obtain an aerial image of the received mask layout on a wafer; and

applying a resist model to the aerial image to obtain a final resist image on the wafer.

19. The method of claim 18, wherein the generating the edge interaction model comprises:

dividing the mask layout samples into a plurality of tiles, the tiles each being surrounded by a margin of interaction length;

determining the edge interaction model for a tile.

20. The method of claim 18, wherein the computing the plurality of kernels comprises computing each of the kernels as a function of:

a location of the influenced pixel, a respective location of each of the two or more edge pixels, and a respective orientation angle associated with each of the two or more edge pixels.