Defect detection of a semiconductor specimen

Patent metadata

Field	Value
Publication number	US-12223641-B2
Application number	US-202217748996-A
Country	US
Kind code	B2
Filing date	May 19, 2022
Priority date	May 19, 2022
Publication date	Feb 11, 2025
Grant date	Feb 11, 2025

Abstract

There is provided a system and method of defect detection of a semiconductor specimen. The method includes obtaining a first image of the specimen acquired at a first bit depth, converting by a first processor the first image to a second image with a second bit depth lower than the first bit depth, transmitting the second image to a second processor configured to perform first defect detection on the second image using a first defect detection algorithm to obtain a first set of defect candidates, and sending locations of the first set of defect candidates to the first processor, extracting, from the first image, a set of image patches corresponding to the first set of defect candidates based on the locations, and performing second defect detection on the set of image patches using a second defect detection algorithm to obtain a second set of defect candidates.

First claim

The invention claimed is:

1. A computerized system of defect detection of a semiconductor specimen, the system comprising a first processing and memory circuitry (PMC) configured to:
obtain a first image representative of at least a part of the semiconductor specimen, the first image acquired by an inspection tool at a first bit depth;
convert the first image to a second image with a second bit depth, wherein the second bit depth is a lower bit depth than the first bit depth;
transmit the second image to a second PMC operatively connected to the first PMC, wherein the second PMC is configured to perform first defect detection on the second image using a first defect detection algorithm, giving rise to a first set of defect candidates, and send locations of the first set of defect candidates to the first PMC;
upon receiving the locations from the second PMC, extract, from the first image, a set of image patches corresponding to the first set of defect candidates based on the locations; and
perform second defect detection on the set of image patches using a second defect detection algorithm, giving rise to a second set of defect candidates.

2. The computerized system according to claim 1 , wherein the first bit depth is one of: 32 bits per pixel, 24 bits per pixel, and 16 bits per pixel; and the second bit depth is one of: 16 bits per pixel and 8 bits per pixel.

3. The computerized system according to claim 1 , wherein the first PMC comprises one or more graphics processing units (GPUs) optimized to process images with the first bit depth.

4. The computerized system according to claim 1 , wherein the second PMC comprises one or more digital signal processors (DSPs) optimized to process images with the second bit depth.

5. The computerized system according to claim 1 , wherein the first PMC is configured to convert the first image by selecting a transformation function based on pixel value distribution of the first image, the transformation function usable for converting a range of pixel values represented by the first bit depth to a range of pixel values represented by the second bit depth, and the pixel values of the first image are converted using the transformation function to obtain the second image.

6. The computerized system according to claim 5 , wherein the transformation function is specifically selected and configured to improve accuracy of the second image with the second bit depth.

7. The computerized system according to claim 1 , wherein the first defect detection algorithm is a die-to-reference detection algorithm using one or more reference images of the second image.

8. The computerized system according to claim 7 , wherein the first defect detection algorithm is specifically configured for defect detection on one or more second images with the second bit depth.

9. The computerized system according to claim 1 , wherein the second defect detection algorithm comprises a machine learning (ML) model trained using one or more training images with the first bit depth and respective labels associated therewith indicative of presence of defects of interest (DOIs) or nuisances thereof.

10. The computerized system according to claim 9 , wherein the second set of defect candidates are reviewed by a review tool, giving rise to a set of DOIs, the ML model is re-trained using image patches corresponding to the set of DOIs and associated with a label of DOI, and image patches corresponding to non-DOI defect candidates from the second set of defect candidates and associated with a label of nuisance, and the re-trained ML model is used to re-perform the second defect detection.

11. The computerized system according to claim 1 , wherein the first PMC is configured to obtain a plurality of first images sequentially acquired by the inspection tool during runtime inspection and representative of different parts of the semiconductor specimen, and perform the converting, transmitting, extracting and performing for each given first image of the plurality of first images, and wherein each given first image is converted adaptively by selecting a specific transformation function based on pixel value distribution of the given first image.

12. The computerized system according to claim 1 , further comprising the second PMC, wherein the first PMC is optimized for first bit depth processing and is thereby capable of improving detection sensitivity, the second PMC is optimized for second bit depth processing and is thereby capable of improving computation efficiency and lowering computation cost, and wherein the first PMC and the second PMC are leveraged in a specific way for performing respective defect detections on images with different bit depths, thereby enabling improved throughput without sacrificing detection sensitivity.

13. A computerized method of defect detection of a semiconductor specimen, the method performed by a first processing and memory circuitry (PMC) and comprising:
obtaining a first image representative of at least a part of the semiconductor specimen, the first image acquired by an inspection tool at a first bit depth;
converting the first image to a second image with a second bit depth, wherein the second bit depth is a lower bit depth than the first bit depth;
transmitting the second image to a second PMC operatively connected to the first PMC, wherein the second PMC is configured to perform first defect detection on the second image using a first defect detection algorithm, giving rise to a first set of defect candidates, and send locations of the first set of defect candidates to the first PMC;
upon receiving the locations from the second PMC, extracting, from the first image, a set of image patches corresponding to the first set of defect candidates based on the locations; and
performing second defect detection on the set of image patches using a second defect detection algorithm, giving rise to a second set of defect candidates.

14. The computerized method according to claim 13 , wherein the first PMC comprises one or more graphic processing units (GPUs) optimized to process images with the first bit depth.

15. The computerized method according to claim 13 , wherein the second PMC comprises one or more digital signal processors (DSPs) optimized to process images with the second bit depth.

16. The computerized method according to claim 13 , wherein the converting the first image comprises selecting a transformation function based on pixel value distribution of the first image, the transformation function usable for converting a range of pixel values represented by the first bit depth to a range of pixel values represented by the second bit depth, and the pixel values of the first image are converted using the transformation function to obtain the second image.

17. The computerized method according to claim 13 , wherein the second defect detection algorithm comprises a machine learning (ML) model trained using one or more training images with the first bit depth and respective labels associated therewith indicative of presence of defects of interest (DOIs) or nuisances thereof.

18. The computerized method according to claim 17 , wherein the second set of defect candidates are reviewed by a review tool, giving rise to a set of DOIs, the ML model is re-trained using image patches corresponding to the set of DOIs and associated with a label of DOI, and image patches corresponding to non-DOI defect candidates from the second set of defect candidates and associated with a label of nuisance, and the re-trained ML model is used to re-perform the second defect d

Assignees

• Applied Materials Israel Ltd

Inventors

• Dudovich Boaz
• Ariel Assaf
• Bar Amir
• Yehieli Lior
• Itzikowitz Chen
• Ben Israel Shiran
• Katz Lior
• Joni Eli Oren
• Rot Eyal

Classifications

• H10P74/203

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

• G06T3/10

  Selection of transformation methods according to the characteristics of the input images · CPC title

• H04N7/01

  Conversion of standards {, e.g. involving analogue television standards or digital television standards processed at pixel level} · CPC title

• G06T2207/20081

  Training; Learning · CPC title

• G06T2207/30148

  Semiconductor; IC; Wafer · CPC title