Method for defect review measurement on a substrate, apparatus for imaging a substrate, and method of operating thereof

Patent metadata

Field	Value
Publication number	US-2026036536-A1
Application number	US-202218998756-A
Country	US
Kind code	A1
Filing date	Aug 18, 2022
Priority date	Aug 18, 2022
Publication date	Feb 5, 2026
Grant date	—

Abstract

A method for defect classification is described. The method includes storing a plurality of defect classes in terms of a plurality of classification rules in a multi-dimensional feature space, wherein the plurality of classification rules, for each defect class of the plurality of defect classes, defines in the multi-dimensional feature space a boundary of a region associated with the defect class; receiving one or more electron beam image data associated with a plurality of defects detected in one or more display devices on a large area substrate under inspection; applying, by a processor, an automatic classifier to the electron beam image data, the automatic classifier based on the plurality of classification rules; and identifying the plurality of defects each classified with at least a first level of confidence based on at least one confidence threshold.

First claim

1 . A method for defect classification, comprising:
storing a plurality of defect classes in terms of a plurality of classification rules in a multi-dimensional feature space, wherein the plurality of classification rules, for each defect class of the plurality of defect classes, defines in the multi-dimensional feature space a boundary of a region associated with the defect class; receiving one or more electron beam image data associated with a plurality of defects detected in one or more display devices on a large area substrate under inspection; applying, by a processor, an automatic classifier to the electron beam image data, the automatic classifier based on the plurality of classification rules; and identifying the plurality of defects each classified with at least a first level of confidence based on at least one confidence threshold.

2 . The method of claim 1 , wherein the one or more electron beam image data is received in a first data format and is converted, by an interface algorithm, to a second data format for applying the automatic classifier.

3 . The method of claim 2 , wherein the first data format comprises a reference image and a defect image.

4 . The method of claim 3 , wherein the first data format further includes a class image.

5 . The method of claim 2 , further comprising:
adding, by the interface algorithm, a defect classification for each defect of the plurality of defects identified with at least the first level of confidence to the first data format.

6 . The method of claim 1 , further comprising:
generating a defect image of a portion of the large area substrate, the portion including a defect; generating a reference image corresponding to the defect image; determining a mask pattern based on the reference image; comparing the defect image and the reference image in regions outside the mask pattern to detect the defect; and redetecting the defect without the mask pattern to generate the electron beam image data.

7 . The method of claim 1 , further comprising:
loading a large area substrate having one or more structures of the one or more display devices from a first production chamber into a vacuum chamber having an electron beam microscope coupled to the vacuum chamber configured to measure electron beam images for the one or more electron beam image data; and loading the large area substrate from the vacuum chamber directly or indirectly into a second production chamber.

8 . The method of claim 7 , further comprising:
loading the large area substrate to a repair station before loading the large area substrate into the second production chamber.

9 . The method of claim 1 , further comprising:
measuring the one or more display devices with at least one of an automated optical inspection tool or an electron beam test tool to obtain a plurality of positions associated with the plurality of defects.

10 . The method of claim 9 , further comprising:
compensating a defect location offset for each of the plurality of positions.

11 . The method of claim 1 , further comprising:
providing electron beam image data to an operator for defects classified below the first level of confidence, wherein the first level of confidence being indicative of the defect being located in an overlap area between respective boundaries of at least two of the plurality of defect classes and/or outside a boundary of a single-class classifier.

12 . The method of claim 1 , wherein applying the automatic classifier comprises:
applying a multi-class classifier to the electron beam image data to classify the plurality of defects; and applying a single-class classifier to identify the plurality of defects with the at least one confidence threshold.

13 . The method of claim 12 , further comprising:
setting a purity level and/or a confidence threshold to adapt a rejection rate.

14 . A method of generating a plurality of classification rules, comprising:
receiving a plurality of electron beam image data associated with a plurality of defects detected in one or more display devices on a large area substrate; receiving defect classes, each defect class associated with one or more of the plurality of electron beam image data; and generating the plurality of classification rules in a multi-dimensional feature space, wherein the plurality of classification rules, for each defect class of a plurality of defect classes, defines in the multi-dimensional feature space a boundary of a region associated with the defect class.

15 . The method of claim 14 , further comprising:
converting, by an interface algorithm, the plurality of electron beam image data from a first data format to a second data format before generating the plurality of classification rules.

16 . The method of claim 14 , wherein the first data format comprises a reference image, a defect image, and a class image.

17 . An automated defect classification system, comprising:
a memory comprising instructions and a processor, wherein the instructions, when executed by the processor, cause the automated defect classification system to execute a method according to claim 1 .

18 . An automated defect classification system, comprising:
a memory comprising instructions and a processor, wherein the instructions, when executed by the processor, cause the automated defect classification system to execute a method according to claim 14 .

Assignees

• Applied Materials Inc

Inventors

• Mueller Bernhard G
• Gao Yong
• Nunan Peter
• Knaub Nikolai
• Li Lingjia

Classifications

• G06V2201/06

  Recognition of objects for industrial automation · CPC title

• G06V10/759

  Region-based matching · CPC title

• G06T2207/30148

  Semiconductor; IC; Wafer · CPC title

• G06T2207/10061

  from scanning electron microscope · CPC title

• G01N2223/6116

  semiconductor wafer · CPC title