Learnable defect detection for semiconductor applications

Patent metadata

Field	Value
Publication number	US-2023118839-A1
Application number	US-202218078989-A
Country	US
Kind code	A1
Filing date	Dec 11, 2022
Priority date	Apr 9, 2019
Publication date	Apr 20, 2023
Grant date	—

Abstract

Methods and systems for learnable defect detection for semiconductor applications are provided. One system includes a deep metric learning defect detection model configured for projecting a test image for a specimen and a corresponding reference image into latent space, determining a distance in the latent space between one or more different portions of the test image and corresponding portion(s) of the corresponding reference image, and detecting defects in the one or more different portions of the test image based on the determined distances. Another system includes a learnable low-rank reference image generator configured for removing noise from one or more test images for a specimen thereby generating one or more reference images corresponding to the one or more test images.

First claim

1 - 36 . (canceled)

37 . A system configured to generate a reference image for a specimen, comprising:
one or more computer systems; and one or more components executed by the one or more computer systems, wherein the one or more components comprise a learnable low-rank reference image generator, wherein the one or more computer systems are configured for inputting one or more test images for a specimen into the learnable low-rank reference image generator, wherein the one or more test images are generated for different locations on the specimen corresponding to the same location in a design for the specimen, and wherein the learnable low-rank reference image generator is configured for removing noise from the one or more test images thereby generating one or more reference images corresponding to the one or more test images; and wherein a defect detection component detects defects on the specimen based on the one or more test images and their corresponding one or more reference images.

38 . The system of claim 37 , wherein the defect detection component comprises a deep learning defect detection component.

39 . The system of claim 37 , wherein the defect detection component comprises a deep metric learning defect detection model.

40 . (canceled)

41 . The system of claim 37 , wherein the specimen is a wafer on which a layer of patterned features has been formed using multiple lithography exposure steps.

42 . The system of claim 37 , wherein the specimen is a wafer on which a layer of patterned features has been formed using extreme ultraviolet lithography.

43 . The system of claim 37 , wherein the learnable low-rank reference image generator comprises a learnable principle component analysis model.

44 . The system of claim 37 , wherein the learnable low-rank reference image generator comprises a learnable independent component analysis model or a learnable canonical correlation analysis model.

45 . The system of claim 37 , wherein the learnable low-rank reference image generator comprises a linear or non-linear regression model, a spatial low-rank neural network model, or a spatial low-rank probabilistic model.

46 - 48 . (canceled)

49 . The system of claim 37 , wherein the defect detection component is included in the one or more components executed by the one or more computer systems, and wherein the one or more computer systems are further configured for jointly training the learnable low-rank reference image generator and the defect detection component with one or more training images and pixel-level ground truth information for the one or more training images.

50 . The system of claim 49 , wherein the one or more training images comprise images for one or more defect classes selected by a user, images for one or more hot spots on the specimen selected by the user, or images for one or more weak patterns in a design for the specimen selected by the user.

51 - 55 . (canceled)

56 . The system of claim 49 , wherein the pixel-level ground truth information comprises information for the one or more training images generated from results of physics simulation performed with the one or more training images.

57 . The system of claim 49 , wherein the pixel-level ground truth information comprises information converted into a first format from known defect locations in a second format different than the first format.

58 . The system of claim 37 , wherein the learnable low-rank reference image generator and the defect detection component are further configured for inline defect detection.

59 . (canceled)

60 . The system of claim 37 , wherein the one or more components further comprise a defect classification component configured for separating the detected defects into two or more types, and wherein the defect classification component is a deep learning defect classification component.

61 . The system of claim 37 , wherein the different locations comprise locations in different dies on the specimen.

62 . The system of claim 37 , wherein the different locations comprise multiple locations in only one die on the specimen.

63 . The system of claim 37 , wherein the one or more test images correspond to a job frame generated for the specimen by an imaging system, and wherein the one or more computer systems are further configured for repeating the inputting for one or more other test images corresponding to a different job frame generated for the specimen by the imaging system such that the learnable low-rank reference image generator separately generates the one or more reference images for the job frame and the different job frame.

64 . (canceled)

65 . The system of claim 37 , wherein the one or more test images are generated for the specimen by an imaging system using only a single mode of the imaging system, and wherein the one or more computer systems are further configured for repeating the inputting for one or more other test images generated for the specimen by the imaging system using a different mode of the imaging system such that the learnable low-rank reference image generator generates the one or more reference images for the one or more other test images.

66 . A computer-implemented method for generating a reference image for a specimen, comprising:
inputting one or more test images for a specimen into a learnable low-rank reference image generator, wherein the learnable low-rank reference image generator is included in one or more components executed by one or more computer systems, wherein the one or more test images are generated for different locations on the specimen corresponding to the same location in a design for the specimen, and wherein the learnable low-rank reference image generator is configured for removing noise from the one or more test images thereby generating one or more reference images corresponding to the one or more test images; and detecting defects on the specimen based on the one or more test images and their corresponding one or more reference images.

67 . A non-transitory computer-readable medium storing program instructions executable on one or more computer systems for performing a computer-implemented method for generating a reference image for a specimen, wherein the computer-implemented method comprises:
inputting one or more test images for a specimen into a learnable low-rank reference image generator, wherein the learnable low-rank reference image generator is included in one or more components executed by the one or more computer systems, wherein the one or more test images are generated for different locations on the specimen corresponding to the same location in a design for the specimen, and wherein the learnable low-rank reference image generator is configured for removing noise from the one or more test images thereby generating one or more reference images corresponding to the one or more test images; and detecting defects on the specimen based on the one or more test images and their corresponding one or more reference images.

Assignees

• Kla Corp

Inventors

• Zhang Jing
• Yuan Zhuoning
• Dong Yujie
• Bhaskar Kris

Classifications

• H10P74/203 Primary

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

• H10P74/235

  comprising optical enhancement of defects or not-directly-visible states · CPC title

• H10P74/23

  characterised by multiple measurements, corrections, marking or sorting processes · CPC title

• G01N2021/8809

  Adjustment for highlighting flaws · CPC title

• G01N2021/8854

  Grading and classifying of flaws · CPC title