System and method for simulating an aerial image

• US 20060048089A1
• Filed: 08/27/2004
• Published: 03/02/2006
• Est. Priority Date: 08/27/2004
• Status: Active Grant

• Current Assignee: Applied Materials Israel Limited (Applied Materials Incorporated)
• Original Assignee: Applied Materials Israel Limited (Applied Materials Incorporated)
• Inventors: Schwarzband, Ishai
• Granted Patent: US 7,310,796 B2
• Time in Patent Office: Days
• US Class Current: 382/144
• 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 1/68 Preparation processes not covered by groups G03F1/20 - G03F1/50
• 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: G03F 7/70666 Aerial image, i.e. measuring the image of the patterned exposure light at the image plane of the projection system
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30

Abstract

A method for generating a simulated aerial image includes forming a reference aerial image of a first mask using an optical system, and capturing and processing the reference aerial image so as to generate a set of expansion functions representative of the optical system. The simulated aerial image of a second mask is then computed by applying the expansion functions to a design of the second mask.

Claims

1. A method for generating a simulated aerial image, comprising:

forming a reference aerial image of a first mask using an optical system;

capturing and processing the reference aerial image so as to generate a set of expansion functions representative of the optical system; and

computing the simulated aerial image of a second mask by applying the expansion functions to a design of the second mask.

2. The method according to claim 1, wherein the first mask comprises a pseudo-noise pattern.

3. The method according to claim 2, wherein the pseudo-noise pattern is characterized by a transmission function g that approximates a condition:

∫

∫

g({right arrow over (x)}−

{right arrow over (z)}₁)·

g({right arrow over (x)}−

{right arrow over (z)}₂) ·

g({right arrow over (x)}−

{right arrow over (z)}₃)·

g({right arrow over (x)}−

{right arrow over (z)}₄)d{right arrow over (x)}=Gδ

({right arrow over (z)}₁−

{right arrow over (z)}₃,{right arrow over (z)}₂−

{right arrow over (z)}₄),

wherein G is a constant.

4. The method according to claim 1, wherein processing the reference aerial image comprises finding the set of expansion functions so as to minimize a difference between the reference aerial image formed using the system and a simulation of the reference aerial image computed using the expansion functions.

5. The method according to claim 4, wherein finding the expansion functions comprises determining a kernel function based on properties of the optical system, and estimating a series of eigenfunctions of the kernel function.

6. The method according to claim 5, wherein finding the expansion functions comprises expressing each of the expansion functions as a linear combination of the estimated eigenfunctions multiplied by respective expansion coefficients, and determining the expansion coefficients so as to minimize the difference between the reference aerial image formed using the system and the simulation of the reference aerial image.

7. The method according to claim 6, wherein determining the expansion coefficients comprises computing a gradient of the difference with respect to the expansion coefficients, and optimizing the expansion coefficients using the gradient.

8. The method according to claim 6, wherein determining the expansion coefficients comprises defining a hermitian matrix based on the expansion coefficients, and applying a linear regression analysis to the difference between the reference aerial image formed using the system and the simulation of the reference aerial image in order to compute elements of the hermitian matrix.

9. The method according to claim 1, wherein capturing the reference aerial image comprises capturing the image using a camera having optical properties, and wherein processing the reference aerial image comprises determining the expansion functions so as to take into account the optical properties of the camera.

10. The method according to claim 1, and comprising forming and capturing an actual aerial image of the second mask using the optical system, and comparing the actual aerial image to the simulated aerial image so as to evaluate the second mask.

11. The method according to claim 10, wherein comparing the actual aerial image to the simulated aerial image comprises detecting a difference between the actual aerial image to the simulated aerial image, and identifying a defect in the second mask if the difference is greater than a predetermined threshold.

12. Apparatus for modeling an optical system, comprising:

a camera, which is adapted to capture a reference aerial image of a first mask, which image is formed using the optical system; and

an image processor, which is adapted to process the reference aerial image so as to generate a set of expansion functions representative of the optical system, and to compute a simulated aerial image of a second mask by applying the expansion functions to a design of the second mask.

13. The apparatus according to claim 12, wherein the first mask comprises a pseudo-noise pattern.

14. The apparatus according to claim 13, wherein the pseudo-noise pattern is characterized by a transmission function g that approximates a condition:

∫

∫

g({right arrow over (x)}−

{right arrow over (z)}₁)·

g({right arrow over (x)}−

{right arrow over (z)}₂)·

g({right arrow over (x)}−

{right arrow over (z)}₃)·

g({right arrow over (x)}−

{right arrow over (z)}₄)d{right arrow over (x)}=Gδ

({right arrow over (z)}₁−

{right arrow over (z)}₃,{right arrow over (z)}₂−

{right arrow over (z)}₄),

wherein G is a constant.

15. The apparatus according to claim 12, wherein the image processor is adapted to find the expansion functions so as to minimize a difference between the reference aerial image formed using the system and a simulation of the reference aerial image computed using the expansion functions.

16. The apparatus according to claim 15, wherein the image processor is adapted to find the expansion functions by determining a kernel function based on properties of the optical system, and estimating a series of eigenfunctions of the kernel function.

17. The apparatus according to claim 16, wherein the image processor is adapted to express each of the expansion functions as a linear combination of the estimated eigenfunctions multiplied by respective expansion coefficients, and to determine the expansion coefficients so as to minimize the difference between the reference aerial image formed using the system and the simulation of the reference aerial image.

18. The apparatus according to claim 17, wherein the image processor is adapted to determine the expansion coefficients by computing a gradient of the difference with respect to the expansion coefficients, and optimizing the expansion coefficients using the gradient.

19. The apparatus according to claim 17, wherein the image processor is adapted to determine the expansion coefficients by defining a hermitian matrix based on the expansion coefficients, and applying a linear regression analysis to the difference between the reference aerial image formed using the system and the simulation of the reference aerial image in order to compute elements of the hermitian matrix.

20. The apparatus according to claim 12, wherein the camera is characterized by optical properties, and wherein the expansion functions take into account the optical properties of the camera.

21. The apparatus according to claim 12, wherein the camera is adapted to capture an actual aerial image of the second mask formed using the optical system, and wherein the image processor is adapted to compare the actual aerial image to the simulated aerial image so as to evaluate the second mask.

22. The apparatus according to claim 21, wherein the image processor is adapted to detect a difference between the actual aerial image to the simulated aerial image, and to identify a defect in the second mask if the difference is greater than a predetermined threshold.

23. Apparatus for mask inspection, comprising:

an optical system, which is adapted to form aerial images of masks, the aerial images comprising a reference aerial image of a first mask and an actual aerial image of a second mask;

a camera, which is adapted to capture the aerial images formed by the optical system; and

an image processor, which is adapted to process the reference aerial image so as to generate a set of expansion functions representative of the optical system, and to compute a simulated aerial image of a second mask by applying the expansion functions to a design of the second mask, and to compare the actual aerial image to the simulated aerial image so as to evaluate the second mask.

24. The apparatus according to claim 23, wherein the image processor is adapted to detect a difference between the actual aerial image to the simulated aerial image, and to identify a defect in the second mask if the difference is greater than a predetermined threshold.

25. A computer software product for modeling an optical system, the product comprising a computer-readable medium, in which program instructions are stored, which instructions, when read by a computer, cause the computer to process a reference aerial image of a first mask, which image is formed using the optical system, so as to generate a set of expansion functions representative of the optical system, and to compute a simulated aerial image of a second mask by applying the expansion functions to a design of the second mask.