SYSTEM AND METHOD FOR MASK VERIFICATION USING AN INDIVIDUAL MASK ERROR MODEL

• US 20070061772A1
• Filed: 09/08/2006
• Published: 03/15/2007
• Est. Priority Date: 09/09/2005
• Status: Active Grant

• Current Assignee: ASML Netherlands BV (ASML Holding NV)
• Original Assignee: Brion Technologies Incorporated (ASML Holding NV)
• Inventors: Ye, Jun, Hunsche, Stefan
• Granted Patent: US 7,587,704 B2
• Time in Patent Office: Days
• US Class Current: 716/52
• 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 1/84 Inspecting
• 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 30/398 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

Methods and systems are disclosed to inspect a manufactured lithographic mask, to extract physical mask data from mask inspection data, to determine systematic mask error data based on differences between the physical mask data and mask layout data, to generate systematic mask error parameters based on the systematic mask error data, to create an individual mask error model with systematic mask error parameters, to predict patterning performance of the lithographic process using a particular mask and/or a particular projection system, and to predict process corrections that optimize patterning performance and thus the final device yield.

Claims

1. An individual mask error model comprising:

a model of a lithographic mask having systematic mask error parameters determined from an inspection of the lithographic mask.

2. The individual mask error model of claim 1, wherein the systematic mask error parameters are selected from the group consisting of linewidth bias, phase bias, corner rounding radii, line-edge roughness, etch depth, and sidewall angle.

3. A method comprising:

obtaining mask inspection data from a mask manufactured using mask layout data;

determining differences between the mask inspection data and the mask layout data;

generating systematic mask error data based on the differences between the mask inspection data and the mask layout data; and

generating systematic mask error parameters for an individual mask error model based on the systematic mask error data.

4. The method of claim 3, further comprising:

simulating a lithography process using the individual mask error model having the systematic mask error parameters to produce simulated results; and

analyzing the simulated results to evaluate a process window of the lithography process.

5. The method of claim 3, further comprising:

simulating a lithography process using the individual mask error model having the systematic mask error parameters to produce simulated results; and

analyzing the simulated results to verify the mask.

6. The method of claim 3, further comprising:

simulating a lithography process using the individual mask error model having the systematic mask error parameters to produce simulated results; and

comparing the simulated results to a design target.

7. The method of claim 3, further comprising:

simulating a lithography process using the individual mask error model having the systematic mask error parameters to produce simulated results;

simulating the lithography process using a nominal mask error model to produce nominal simulated results; and

comparing the simulated results and the nominal simulated results to verify the mask.

8. The method of claim 3, further comprising:

simulating a lithography process using the individual mask error model having the systematic mask error parameters to produce simulated results;

simulating the lithography process using a post-OPC mask layout to produce ideal simulated results; and

comparing the simulated results and the ideal simulated results to verify the mask.

9. The method of claim 3, wherein the systematic mask error parameters are selected from the group consisting of linewidth bias, phase bias, corner rounding radii, line-edge roughness, etch depth, and sidewall angle.

10. The method of claim 3, wherein the mask inspection data is obtained by directly measuring pattern metrics of the mask using a metrology tool selected from the group consisting of a CD-SEM tool, an imaging SEM tool, an AFM tool, an OCD tool, an optical microscope, a phase-shifting interferometer, and an ellipsometer.

11. The method of claim 3, wherein the mask inspection data is obtained by determining pattern metrics of an aerial image produced by an exposure tool and the mask using an image sensor array disposed in the exposure tool.

12. The method of claim 3, wherein the mask inspection data is obtained by determining pattern metrics of the mask using an inspection tool selected from the group consisting of an optical mask inspection tool, an electron beam mask inspection tool, and an aerial image measurement system.

13. The method of claim 3, wherein the mask layout data is circuit design data.

14. The method of claim 3, wherein the mask layout data is test pattern data.

15. A method comprising:

obtaining mask layout data;

creating a mask inspection plan based on the mask layout data;

inspecting a mask manufactured using the mask layout data according to the mask inspection plan to produce mask inspection data;

extracting physical mask data from the mask inspection data;

determining differences between the extracted physical mask data and the mask layout data;

determining systematic mask error data based on the differences between the extracted physical mask data and the mask layout data;

generating systematic mask error parameters by fitting the systematic mask error data to input variables of a mask error model; and

applying the systematic mask error parameters to the mask error model to create an individual mask error model.

16. The method of claim 15, wherein creating the mask inspection plan includes selecting sampling locations in the mask layout data that cover a parameter space.

17. The method of claim 16, wherein the parameter space includes parameters selected from the group consisting of pattern density, pattern orientation, pattern pitch, pattern size, pattern polarity, corners having different orientation and polarity, and line-ends with varying spacing.

18. The method of claim 15, further comprising:

simulating a lithography process using the model of the lithography process and the individual mask error model to produce simulation results; and

analyzing the simulation results to evaluate a process window of the lithography process.

19. The method of claim 15, further comprising:

simulating a lithography process using the model of the lithography process and the individual mask error model to produce simulation results; and

analyzing the simulation results to verify the mask.

20. The method of claim 15, further comprising:

simulating a lithography process using the model of the lithography process and the individual mask error model to produce simulation results; and

comparing the simulation results to a design target.

21. The method of claim 15, wherein the systematic mask error parameters are selected from the group consisting of linewidth bias, phase bias, corner rounding radii, line-edge roughness, etch depth, and sidewall angle.

22. The method of claim 15, wherein the input variables of the mask error model are pattern environment and pattern location on the mask.

23. The method of claim 15, further comprising:

creating a plurality of individual mask error models for a plurality of masks made by a mask manufacturing process;

simulating a lithography process using the plurality of individual mask error models to produce a plurality of simulated results; and

analyzing the plurality of simulated results to characterize the mask manufacturing process.

24. The method of claim 15, further comprising:

creating a plurality of individual mask error models for a plurality of masks made by a mask manufacturing process;

simulating a lithography process using the plurality of individual mask error models to produce a plurality of simulated results; and

analyzing the plurality of simulated results to identify process corrections for the mask manufacturing process.

25. A system comprising:

a mask inspection tool configured to produce mask inspection data;

a model-based mask data analysis system coupled to the mask inspection tool, the model-based mask data analysis system configured to extract physical mask data from the mask inspection data, to determine systematic mask error data based on differences between the extracted physical mask data and mask layout data, and configured to generate systematic mask error parameters for an individual mask error model based on the systematic mask error data.

26. The system of claim 25, wherein the systematic mask error parameters are selected from the group consisting of linewidth bias, phase bias, corner rounding radii, line-edge roughness, etch depth, and sidewall angle.

27. The system of claim 25, wherein the mask inspection tool is selected from the group consisting of a CD-SEM tool, an imaging SEM tool, an AFM tool, an OCD tool, an optical microscope, a phase-shifting interferometer, and an ellipsometer.

28. The system of claim 25, wherein the mask inspection tool is an image sensor array disposed in an exposure tool.

29. The system of claim 25, wherein the mask inspection tool is selected from the group consisting of an optical mask inspection tool, an electron beam mask inspection tool, and an aerial image measurement system.

30. A method comprising:

obtaining mask layout data;

selecting a mask model having mask model parameters;

obtaining mask inspection data from a mask manufactured using the mask layout data;

extracting physical mask data from the mask inspection data;

determining differences between the physical mask data and the mask layout data;

generating systematic mask error data based on the differences between the physical mask data and the mask layout data; and

modifying values of the mask model parameters based on the systematic mask error data to produce systematic mask error parameters.

31. The method of claim 30, wherein the systematic mask error parameters are selected from the group consisting of linewidth bias, phase bias, corner rounding radii, line-edge roughness, etch depth, and sidewall angle.

32. The method of claim 30, wherein the mask inspection data is obtained by directly measuring pattern metrics of the mask using a metrology tool selected from the group consisting of a CD-SEM tool, an imaging SEM tool, an AFM tool, an OCD tool, an optical microscope, a phase-shifting interferometer, and an ellipsometer.

33. The method of claim 30, wherein the mask inspection data is obtained by determining pattern metrics of an aerial image produced by an exposure tool and the mask using an image sensor array disposed in the exposure tool.

34. The method of claim 30, wherein the mask inspection data is obtained by determining pattern metrics of the mask using an inspection tool selected from the group consisting of an optical mask inspection tool, an electron beam mask inspection tool, and an aerial image measurement system.

35. The method of claim 30, wherein the mask inspection data is obtained in accordance with a mask inspection plan.