Multi-step image alignment method for large offset die-die inspection

Patent metadata

Field	Value
Publication number	US-10522376-B2
Application number	US-201816160515-A
Country	US
Kind code	B2
Filing date	Oct 15, 2018
Priority date	Oct 20, 2017
Publication date	Dec 31, 2019
Grant date	Dec 31, 2019

Abstract

A die-die inspection image can be aligned using a method or system configured to receive a reference image and a test image, determine a global offset and rotation angle from local sections on the reference image and test image, and perform a rough alignment de-skew of the test image prior to performing a fine alignment.

First claim

What is claimed is:

1. A method for obtaining an aligned die-die inspection image, comprising:
receiving a reference image at a processor, the reference image comprising rows and columns of pixels;
selecting a first local section from the reference image using the processor;
receiving a test image at the processor, the test image comprising rows and columns of pixels;
selecting a second local section from the test image using the processor;
determining, using the processor, an estimated rotation offset and an estimated translation offset from the first local section and the second local section;
performing a rough alignment comprising a test image de-skew using the processor, thereby making a partially-aligned test image; and
performing a fine alignment comprising partitioned translation on the partially-aligned test image to obtain an aligned die-die inspection image.

2. The method of claim 1 , wherein the test image de-skew comprises:
determining a skew angle of the test image using the processor; and
de-skewing the test image using the processor.

3. The method of claim 2 , wherein the skew angle of the test image is determined by performing a skew comparison of the first local section from the reference image and the second local section from the test image.

4. The method of claim 3 , wherein the skew comparison comprises:
performing a fast Fourier transform on the first local section from the reference image using the processor to obtain a reference scene function;
performing a fast Fourier transform on the second local section from the test image using the processor to obtain a test scene function; and
comparing the test scene function to the reference scene function using the processor to determine the skew angle.

5. The method of claim 3 , wherein the skew comparison comprises performing a pattern recognition of one or more prominent features in the test image to determine the skew angle.

6. The method of claim 3 , wherein the skew comparison is performed using a machine learning module to determine the skew angle.

7. The method of claim 2 , wherein de-skewing the test image comprises:
determining, based on the skew angle, for each of the pixels in the test image, a column shift vector and a row shift vector using the processor, wherein
the column shift vector comprises a quantity of pixels to shift collinear to the column containing the pixel and a direction; and
the row shift vector comprises a quantity of pixels to shift collinear to the row containing the pixel and a direction; and

shifting each of the pixels according to its column shift vector and row shift vector using the processor.

8. The method of claim 1 , wherein the partitioned translation comprises:
partitioning, using the processor, the reference image into at least one reference image sub-section;
partitioning, using the processor, the test image into at least one test image sub-section; and
translating, using the processor, the test image sub-section to align with the reference image sub-section corresponding to the test image sub-section.

9. A non-transitory computer-readable storage medium, comprising one or more programs for executing the following steps on one or more computing devices:
receive a reference image, the reference image comprising rows and columns of pixels;
select a first local section from the reference image;
receive a test image, the test image comprising rows and columns of pixels;
select a second local section from the test image;
determine an estimated rotation offset and an estimated translation offset from the first local section and the second local section;
perform a rough alignment on the test image comprising a test image de-skew, thereby making a partially-aligned test image; and
perform a fine alignment comprising partitioned translation on the partially-aligned test image to obtain an aligned die-die inspection image.

10. The non-transitory computer-readable storage medium of claim 9 , wherein the test image de-skew comprises:
determining a skew angle of the test image; and
de-skewing the test image, comprising:
determining, based on the skew angle, for each of the pixels in the test image, a column shift vector and a row shift vector, wherein
the column shift vector comprises a quantity of pixels to shift collinear to the column containing the pixel and a direction; and
the row shift vector comprises a quantity of pixels to shift collinear to the row containing the pixel and a direction; and

shifting each of the pixels according to its column shift vector and row shift vector.

11. The non-transitory computer-readable storage medium of claim 10 , wherein the skew angle of the test image is determined by performing a skew comparison of the first local section from the reference image and the second local section from the test image.

12. The non-transitory computer-readable storage medium of claim 11 , wherein the skew comparison comprises:
performing a fast Fourier transform on the first local section from the reference image to obtain a reference scene function;
performing a fast Fourier transform on the second local section from the test image to obtain a test scene function; and
comparing the test scene function to the reference scene function to determine the skew angle.

13. The non-transitory computer-readable storage medium of claim 9 , wherein the partitioned translation comprises:
partitioning the reference image into at least one reference image sub-section;
partitioning the test image into at least one test image sub-section; and
translating the test image sub-section to align with the reference image sub-section corresponding to the test image sub-section.

14. A semiconductor die-die inspection system comprising a sensor to capture images of features of a die and a computing system comprising:
a beam source, wherein the beam source is a light source or an electron beam source;
a stage configured to hold a wafer in a path of a beam produced by the beam source, wherein the beam is a light beam from the light source or an electron beam from the electron beam source;
a detector configured to receive a portion of the beam reflected from the wafer; and
a processor in electronic communication with the detector configured to perform:
a rough alignment of a test image comprising a test image de-skew, thereby making a partially-aligned test image, and
a fine alignment comprising partitioned translation of the partially-aligned test image.

15. The semiconductor die-die inspection system of claim 14 , wherein the processor is further configured to:
receive a reference image, the reference image comprising rows and columns of pixels;
select a first local section from the reference image;
receive a test image, the test image comprising rows and columns of pixels;
select a second local section from the test image; and
determine the estimated rotation offset and the estimated translation offset from the first local section and the second local section.

16. The semiconductor die-die inspection system of claim 14 , wherein the test image de-skew comprises:
determining a skew angle of the test image; and
de-skewing the test image.

17. The semiconductor die-die inspection system of claim 16 , wherein the skew angle of the test image is determined by performing a skew comparison of the first local section from the reference image and the second local section from the test image.

18. The semiconductor die-die inspection system of claim 17 , wherein the skew comparison comprise

Assignees

• Kla Tencor Corp

Inventors

• Lauber Jan
• Vajaria Himanshu
• Zhang Yong

Classifications

• H10P72/0616 Primary

  Monitoring of warpages, curvatures, damages, defects or the like · CPC title

• G06T2207/20016

  Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform · CPC title

• G06T7/001

  using an image reference approach · CPC title

• G06T2207/20021

  Dividing image into blocks, subimages or windows · CPC title

• G06T7/74

  involving reference images or patches · CPC title