SYSTEM, A METHOD AND A COMPUTER PROGRAM PRODUCT FOR FITTING BASED DEFECT DETECTION

• US 20150332451A1
• Filed: 05/15/2014
• Published: 11/19/2015
• Est. Priority Date: 05/15/2014
• Status: Active Application

• Current Assignee: Applied Materials Israel Limited (Applied Materials Incorporated)
• Original Assignee: Applied Materials Israel Limited (Applied Materials Incorporated)
• Inventors: Amzaleg, Moshe, Dodzin, Nir Ben-David
• Granted Patent: US 10,290,092 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01N 2021/95615 with stored comparision signal
• CPC Class Codes: G06T 2207/20076 Probabilistic image processing
• CPC Class Codes: G06T 2207/30148 Semiconductor; IC; Wafer
• CPC Class Codes: G06T 7/0002 Inspection of images, e.g. flaw detection
• CPC Class Codes: G06T 7/0004 Industrial image inspection
• CPC Class Codes: G06T 7/0006 using a design-rule based approach
• CPC Class Codes: G06T 7/0008 checking presence/absence
• CPC Class Codes: G06T 7/001 using an image reference approach

Abstract

A system configured to detect defects in an inspection image generated by collecting signals arriving from an article, the system comprising a tangible processor which includes: (i) a distribution acquisition module, configured to acquire a distribution of comparison values, each of the comparison values being indicative of a relationship between a value associated with a pixel of the inspection image and a corresponding reference value; (ii) a fitting module, configured to fit to the distribution an approximation function out of a predefined group of functions; and (iii) a defect detection module, configured to: (a) set a defect detection criterion based on a result of the fitting; and to (b) determine a presence of a defect in the inspection image, based on the defect detection criterion.

Claims

1. A method for computerized defect detection in an inspection image generated by collecting signals arriving from an article, the method comprising:

acquiring a distribution of comparison values, each of the comparison values being indicative of a relationship between a value associated with a pixel of the inspection image and a corresponding reference value;

fitting to the distribution an approximation function out of a predefined group of functions;

setting a defect detection criterion based on a result of the fitting; and

determining a presence of a defect in the inspection image, based on the defect detection criterion.

2. The method according to claim 1, wherein the approximation function is a Parabola and the fitting includes selecting one or more Parabola parameters.

3. The method according to claim 1, wherein the fitting includes selecting the approximation function by estimating errors between the distribution of comparison values and the approximation function.

4. The method according to claim 1, wherein each of the comparison values is indicative of a difference between a color value of a pixel of the inspection image and a color value of a corresponding pixel of a reference image.

5. The method according to claim 1, wherein the fitting gives smaller weight to common comparison values.

6. The method according to claim 1, wherein the acquiring comprises applying an operator to values of the distribution, wherein for at least half of values of the distribution, a difference between an output value of the operator and MAX(log(x), const) is less than 10% of a maximum value of the distribution.

7. The method according to claim 1, wherein the fitting gives smaller weight to comparison values whose distance from an average comparison value is larger than a standard deviation of the distribution.

8. The method according to claim 1, wherein the fitting gives smaller weight to:

(a) common comparison values, and to (b) comparison values whose distance from an average comparison value is larger than a standard deviation of the distribution.

9. A system configured to detect defects in an inspection image generated by collecting signals arriving from an article, the system comprising a tangible processor which comprises:

a distribution acquisition module, configured to acquire a distribution of comparison values, each of the comparison values being indicative of a relationship between a value associated with a pixel of the inspection image and a corresponding reference value;

a fitting module, configured to fit to the distribution an approximation function out of a predefined group of functions; and

a defect detection module, configured to (a) set a defect detection criterion based on a result of the fitting; and

to (b) determine a presence of a defect in the inspection image, based on the defect detection criterion.

10. The system according to claim 9, wherein the approximation function is a Parabola and the fitting module is configured to select one or more Parabola parameters.

11. The system according to claim 9, wherein the fitting module is configured to select the approximation function by estimating errors between the distribution of comparison values and the approximation function.

12. The system according to claim 9, wherein each of the comparison values is indicative of a difference between a color value of a pixel of the inspection image and a color value of a corresponding pixel of a reference image.

13. The system according to claim 9, wherein the fitting module is configured to give smaller weight to common comparison values when fitting the approximation function to the distribution.

14. The system according to claim 9, wherein the distribution acquisition module is configured to apply an operator to values of the distribution, wherein for at least half of values of the distribution, a difference between an output value of the operator and MAX(log(x), const) is less than 10% of a maximum value of the distribution.

15. The system according to claim 9, wherein the fitting module is configured to give smaller weight to comparison values whose distance from an average comparison value is larger than a standard deviation of the distribution when fitting the approximation function to the distribution.

16. The system according to claim 9, wherein the fitting module is configured to give smaller weight to:

(a) common comparison values, and to (b) comparison values whose distance from an average comparison value is larger than a standard deviation of the distribution, when fitting the approximation function to the distribution.

17. A non-volatile program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform a method for defect detection in an inspection image generated by collecting signals arriving from an article comprising:

acquiring a distribution of comparison values, each of the comparison values being indicative of a relationship between a value associated with a pixel of the inspection image and a corresponding reference value;

fitting to the distribution an approximation function out of a predefined group of functions;

setting a defect detection criterion based on a result of the fitting; and

determining a presence of a defect in the inspection image, based on the defect detection criterion.

18. The program storage device according to claim 17, wherein the approximation function is a Parabola and the fitting includes selecting one or more Parabola parameters.

19. The program storage device according to claim 17, wherein the fitting includes selecting the approximation function by estimating errors between the distribution of comparison values and the approximation function.

20. The program storage device according to claim 17, wherein the fitting gives smaller weight to common comparison values.

21. The program storage device according to claim 17, wherein the acquiring comprises applying an operator to values of the distribution, wherein for at least half of values of the distribution, a difference between an output value of the operator and MAX(log(x), const) is less than 10% of a maximum value of the distribution.

22. The program storage device according to claim 17, wherein the fitting gives smaller weight to comparison values whose distance from an average comparison value is larger than a standard deviation of the distribution.

23. The program storage device according to claim 17, wherein the fitting gives smaller weight to:

(a) common comparison values, and to (b) comparison values whose distance from an average comparison value is larger than a standard deviation of the distribution.