Method and System for Iterative Defect Classification

Patent metadata

Field	Value
Publication number	US-2016358041-A1
Application number	US-201615010887-A
Country	US
Kind code	A1
Filing date	Jan 29, 2016
Priority date	Jun 5, 2015
Publication date	Dec 8, 2016
Grant date	—

Abstract

Defect classification includes acquiring one or more images of a specimen including multiple defects, grouping the defects into groups of defect types based on the attributes of the defects, receiving a signal from a user interface device indicative of a first manual classification of a selected number of defects from the groups, generating a classifier based on the first manual classification and the attributes of the defects, classifying, with the classifier, one or more defects not manually classified by the manual classification, identifying the defects classified by the classifier having the lowest confidence level, receiving a signal from the user interface device indicative of an additional manual classification of the defects having the lowest confidence level, determining whether the additional manual classification identifies one or more additional defect types not identified in the first manual classification, and iterating the procedure until no new defect types are found.

First claim

What is claimed:

1 . A method for defect classification comprising:
acquiring one or more images of a specimen, the one or more images including a plurality of defects; grouping each of at least a portion of the plurality of defects into one of two or more groups of defect types based on one or more attributes of the defects; receiving a signal from a user interface device indicative of a first manual classification of a selected number of defects from each of the two or more groups of defect types; generating a classifier based on the received first manual classification and the attributes of the defects; classifying, with the classifier, one or more defects not manually classified by the manual classification; identifying a selected number of defects classified by the classifier having the lowest confidence level; receiving a signal from the user interface device indicative of an additional manual classification of the selected number of the defects having the lowest confidence level; and determining whether the additional manual classification identifies one or more additional defect types not identified in the first manual classification.

2 . The method of claim 1 , further comprising:
responsive to the identification by the additional manual classification of one or more defect types not identified by the first manual classification, generating an additional classifier based on the first manual classification and the additional manual classification; classifying, with the additional classifier, one or more defects not classified by the first manual classification or the additional manual classification; identifying a selected number of defects classified by the additional classifier having the lowest confidence level; receiving a signal from the user interface device indicative of a second additional manual classification of the selected number of the defects having the lowest confidence level; and determining whether the second additional manual classification identifies one or more additional defect types not identified in the first manual classification or the additional.

3 . The method of claim 1 , further comprising:
responsive to the determination that the additional manual classification does not identify a defect type not included in the first manual classification, reporting at least the defect types classified by the first manual classification and the defect types classified by the classifier.

4 . The method of claim 1 , wherein the grouping each of at least a portion of the plurality of defects into one of two or more groups of defect types based on one or more attributes of the defects comprises:
grouping each of at least a portion of the plurality of defects into one of two or more groups of defect types with a real-time automatic defect classification (RT-ADC) scheme applied to the one or more attributes.

5 . The method of claim 1 , wherein at least of the first classifier or the additional classifier comprise:
an ensemble learning classifier.

6 . The method of claim 5 , wherein the ensemble learning classifier comprises:
a random forest classifier.

7 . The method of claim 5 , wherein the ensemble learning classifier comprises:
a support vector machine (SVM).

8 . The method of claim 1 , wherein at least of the first classifier or the additional classifier comprise:
at least one of a decision tree classifier or a multiple decision tree classifier.

9 . The method of claim 1 , wherein a confidence level is calculated for the one or more defects classified with the classifier.

10 . The method of claim 9 , wherein the confidence level is calculated for the one or more defects classified with the classifier with a voting scheme.

11 . An apparatus for defect classification comprising:
an inspection tool, the inspection tool including one or more detectors configured to acquire one or more images of at least a portion of a specimen; a user interface device; and a controller, the controller including one or more processors communicatively coupled to the one or more detectors of the inspection tool, wherein the one or more processors are configured to execute a set of program instructions stored in memory, the set of program instructions configured to cause the one or more processors to:
receive the one or more images from the one or more detectors of the inspection tool;
group each of at least a portion of the plurality of defects into one of two or more groups of defect types based on one or more attributes of the defects;
receive a signal from a user interface device indicative of a first manual classification of a selected number of defects from each of the two or more groups of defect types;
generate a classifier based on the received first manual classification and the attributes of the defects;
classify, with the classifier, one or more defects not manually classified by the manual classification;
identify a selected number of defects classified by the classifier having the lowest confidence level;
receive a signal from the user interface device indicative of an additional manual classification of the selected number of the defects having the lowest confidence level; and
determine whether the additional manual classification identifies one or more additional defect types not identified in the first manual classification.

12 . The apparatus of claim 11 , wherein controller is further configured to:
responsive to the identification by the additional manual classification of one or more defect types not identified by the first manual classification, generate an additional classifier based on the first manual classification and the additional manual classification; classify, with the additional classifier, one or more defects not classified by the first manual classification or the additional manual classification; identify a selected number of defects classified by the additional classifier having the lowest confidence level; receive a signal from the user interface device indicative of a second additional manual classification of the selected number of the defects having the lowest confidence level; and determine whether the second additional manual classification identifies one or more additional defect types not identified in the first manual classification or the additional.

13 . The apparatus of claim 11 , wherein controller is further configured to:
responsive to the determination that the additional manual classification does not identify a defect type not included in the first manual classification, report at least the defect types classified by the first manual classification and the defect types classified by the classifier.

14 . The apparatus of claim 11 , wherein the controller is further configured to:
group each of at least a portion of the plurality of defects into one of two or more groups of defect types with a real-time automatic defect classification (RT-ADC) scheme applied to the one or more attributes.

15 . The apparatus of claim 11 , wherein at least of the first classifier or the additional classifier comprise:
an ensemble learning classifier.

16 . The apparatus of claim 15 , wherein the ensemble learning classifier comprises:
a random forest classifier.

17 . The apparatus of claim 15 , wherein the ensemble learning classifier comprises:
a support vector machine (SVM).

18 . The apparatus of claim 11 , wherein at least of the first classifier or the additional classifier comprise:
at least one of a decision tree classif

Assignees

• Kla Tencor Corp

Inventors

• Venkataraman Sankar
• He Li
• Jordan Iii John R
• Baris Oksen
• Sinha Harsh

Classifications

• H10P74/203

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

• H10P74/23

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

• H10P74/00

  Testing or measuring during manufacture or treatment of wafers, substrates or devices · CPC title

• G06V10/7788

  the supervisor being a human, e.g. interactive learning with a human teacher · CPC title

• G06V10/772

  Determining representative reference patterns, e.g. averaging or distorting patterns; Generating dictionaries · CPC title