Inspection with previous step subtraction

Patent metadata

Field	Value
Publication number	US-11921052-B2
Application number	US-202318128203-A
Country	US
Kind code	B2
Filing date	Mar 29, 2023
Priority date	Mar 31, 2022
Publication date	Mar 5, 2024
Grant date	Mar 5, 2024

Abstract

An inspection system may generate first-step images of multiple sample regions after a first process step and generate second-step images of the sample regions after a second process step, where the second process step modifies the sample in at least one of the sample regions. The system may further identify one of the sample regions as a test region and at least some of the remaining sample regions as comparison regions, where the second-step image of the test region is a test image and the second-step images of the comparison regions are comparison images. The system may further generate a multi-step difference image by subtracting a combination of at least one of the second-step comparison images and at least two of the first-step images from the test image. The system may further identify defects in the test region associated with the second process step based on the multi-step difference image.

First claim

What is claimed:

1. An inspection system comprising:
a controller including one or more processors configured to execute program instructions causing the one or more processors to implement an inspection recipe by:
receiving first-step images of a plurality of sample regions after a first process step;
receiving second-step images of the plurality of sample regions after a second process step, wherein the second process step modifies the sample in at least one of the plurality of sample regions;
identifying one of the plurality of sample regions as a test region and at least some of the remaining sample regions as comparison regions, wherein the second-step image of the test region is a test image and the second-step images of the comparison regions are second-step comparison images;
generating a multi-step difference image by a weighted subtraction of a combination of at least one of the second-step comparison images and at least two of the first-step images from the test image; and
identifying defects in the test region associated with the second process step based on the multi-step difference image.

2. The inspection system of claim 1 , wherein a number of the first-step images used to generate the multi-step difference image is one greater than a number of the second-step comparison images to provide that the multi-step difference image is generated based on a same number of the first-step and the second-step images.

3. The inspection system of claim 2 , wherein the number of first-step images used to generate the multi-step difference image is at least three.

4. The inspection system of claim 1 , further comprising:
classifying at least some of the defects as at least one of a nuisance or a defect of interest.

5. The inspection system of claim 1 , wherein weights associated with the weighted subtraction are determined by a fitting technique.

6. The inspection system of claim 1 , wherein weights associated with the weighted subtraction are determined by a regression technique.

7. The inspection system of claim 1 , wherein weights associated with the weighted subtraction are determined by a multi-color adaptive threshold (MCAT) technique.

8. The inspection system of claim 1 , wherein weights associated with the weighted subtraction are determined by a machine learning technique.

9. The inspection system of claim 1 , wherein generating the multi-step difference image by the weighted subtraction of the combination of at least one of the second-step comparison images and at least two of the first-step images from the test image comprises:
generating the multi-step difference image (I MSDIFF,i ) based on the equation I MSDIFF,i =I SecondStep,i −Σ j≠i α j I SecondStep,j −Σ j β j I FirstStep,j , wherein I SecondStep,i corresponds to the test image, Σ j≠i α j I SecondStep,j corresponds to the at least one of the second-step comparison images, and Σ j β j I FirstStep,j corresponds to the at least two of the first-step images, wherein subscripts i and j correspond to the sample regions, wherein α and β correspond to weights.

10. The inspection system of claim 1 , wherein generating the multi-step difference image by the weighted subtraction of the combination of at least one of the second-step comparison images and at least two of the first-step images from the test image comprises:
generating the multi-step difference image (I MSDIFF,i ) based on the equation I MSDIFF,i =I SecondStep,i −Σ j≠i α j I SecondStep,j −[I FirstStep,i −Σ j≠i β j I FirstStep,j ], where I SecondStep,i corresponds to the test image, Σ j≠i α j I SecondStep,j corresponds to the at least one of the second-step comparison images, and [I FirstStep,i −Σ j≠i β j I FirstStep,j ] corresponds to the at least one of the first-step images, wherein subscripts i and j correspond to the sample regions, wherein α, β, and γ correspond to weights.

11. The inspection system of claim 1 , wherein generating the multi-step difference image by the weighted subtraction of the combination of at least one of the second-step comparison images and at least two of the first-step images from the test image comprises:
generating the multi-step difference image (I MSDIFF,i ) based on the equation I MSDIFF,i =I SecondStep,i −Σ j≠i α j I SecondStep,j −γ[I FirstStep,i −Σ j≠i β j I FirstStep,j ], where I SecondStep,i corresponds to the test image, Σ j≠i α j I SecondStep,j corresponds to the at least one of the second-step comparison images, and [I FirstStep,i −Σ j≠i β j I FirstStep,j ] corresponds to the at least one of the first-step images, wherein subscripts i and j correspond to the sample regions, wherein α, β, and γ correspond to weights.

12. The inspection system of claim 1 , wherein detecting defects on the sample based on the multi-step difference image comprises:
providing the inspection reference image to a machine learning algorithm; and
detecting defects on the sample based on an output of the machine learning algorithm.

13. The inspection system of claim 1 , wherein detecting defects on the sample based on the multi-step difference image comprises:
detecting defects on the sample based on the multi-step difference image using a multi-die adaptive threshold (MDAT) technique.

14. The inspection system of claim 1 , wherein the defects comprise:
a deviation of at least one of a shape, size, or orientation of a feature fabricated by the second process step.

15. The inspection system of claim 1 , wherein the defects comprise:
an absence of a feature intended to be fabricated by the second process step.

16. The inspection system of claim 1 , wherein the defects comprise:
at least one of a scratch, a pit, or residual material in the inspection region after the second process step.

17. The inspection system of claim 1 , wherein the defects comprise:
at least one of an unintended bridge between two features fabricated by the second process step or an unintended break in a feature fabricated by the second process step.

18. An inspection method comprising:
generating first-step images of a plurality of sample regions after a first process step;
generating second-step images of the plurality of sample regions after a second process step, wherein the second process step modifies the sample in at least one of the plurality of sample regions;
identifying one of the plurality of sample regions as a test region and at least some of the remaining sample regions as comparison regions, wherein the second-step image of the test region is a test image and the second-step images of the comparison regions are comparison images;
generating a multi-step difference image by a weighted subtraction of a combination of at least one of the second-step comparison images and at least two of the first-step images from the test image; and
identifying defects in the test region associated with the second process step based on the multi-step difference image.

19. The inspection method of claim 18 , wherein a number of the first-step images used to generate the multi-step difference image is one greater than a number of the second-step comparison images to provide that the multi-step difference image is generated based on a same number of the first-step and the second-step images.

20. The inspection method of claim 19 , wherein the number of first-step images used to generate the multi-step difference image is at least three.

21. The inspection method of claim 18 , further comprising:
classifying at least some of the defects as at least one of a nuisan

Assignees

• Kla Corp

Inventors

• Danen Robert M
• Park Sangbong
• Starodub Dmitri
• Sezginer Abdurrahman

Classifications

• H10P74/203

  Structural properties, e.g. testing or measuring thicknesses, line widths, warpage, bond strengths or physical defects · CPC title

• H10P74/277

  Circuits for electrically characterising or monitoring manufacturing processes, e.g. circuits in tested chips or circuits in testing wafers · CPC title

• G06T2207/30148

  Semiconductor; IC; Wafer · CPC title

• G06T2207/20224

  Image subtraction · CPC title

• G06T2207/20081

  Training; Learning · CPC title