WO2001040145A2 - Power assisted automatic supervised classifier creation tool for semiconductor defects - Google Patents

Power assisted automatic supervised classifier creation tool for semiconductor defects Download PDF

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Publication number
WO2001040145A2
WO2001040145A2 PCT/US2000/032635 US0032635W WO0140145A2 WO 2001040145 A2 WO2001040145 A2 WO 2001040145A2 US 0032635 W US0032635 W US 0032635W WO 0140145 A2 WO0140145 A2 WO 0140145A2
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WO
WIPO (PCT)
Prior art keywords
images
features
training set
user
portion configured
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2000/032635
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English (en)
French (fr)
Other versions
WO2001040145A3 (en
Inventor
David Bakker
Saibal Banerjee
Ian Smith
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KLA Corp
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KLA Tencor Corp
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Filing date
Publication date
Application filed by KLA Tencor Corp filed Critical KLA Tencor Corp
Priority to JP2001541837A priority Critical patent/JP2003515942A/ja
Priority to EP00982744A priority patent/EP1238367B1/en
Priority to AU20541/01A priority patent/AU2054101A/en
Publication of WO2001040145A2 publication Critical patent/WO2001040145A2/en
Anticipated expiration legal-status Critical
Publication of WO2001040145A3 publication Critical patent/WO2001040145A3/en
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10056—Microscopic image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20092—Interactive image processing based on input by user
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30148—Semiconductor; IC; Wafer

Definitions

  • Supervised Automatic Dynamic Classifier software 204 uses the extracted features of the images in the training set to classify the images in the working set (W) that were not selected by the user as part of the training set (T) (i.e., to classify the set of images W-T).
  • the classifier 204 uses a nearest neighbor method in which the features are extracted from the image set W-T and each image in W-T is classified as belonging to a class.
  • an image in W-T belongs to the class whose members have feature vectors most closely resembling the feature vector of the image. It should be understood that other automatic classification methods could be used.
  • the features in the feature vectors could be weighted (either by the user or using a predefined weighting).
  • the Smart Gallery window allows the user to view thumbnail images of the defects at adjustable sizes and resolutions, presenting a group of defects in an organized fashion. It also assists the user in the classification scheme creation by providing defects in groups based on appearance.
  • the window of Fig. 3 includes a tool bar 302, which contains commands that allow the user to open and save classes and defect images, to sort images in the working set, and to create and manipulate the training set.
  • a confidence setting area 304 allows the user to adjust the Confidence level, which is an adjustable setting of how close an unknown defect can be to a training set. Values preferably range from 0 (loosest setting) to 1 (tightest setting). Changes can be dynamically viewed in confusion matrix 306.
  • Confusion matrix 306 is used to display the results of manual vs. automatic defect classification.
  • a confusion matrix can be generated for both a current set of images or an explicitly selected set.
  • An area 308 displays the working set of images. These images may be displayed in unsorted order or may by sorted or arranged by natural grouping, at the choice of the user.
  • the user preferably drags and drops images from the working set gallery 308 into the training set gallery 310.
  • images in the training set are displayed arranged in user- specified classes.
  • Training set area 312 displays the classes (also called "bins") that contain the composition of the training set, defines grouping, and allows the user to create new classes/bins. When this area 312 is active, the user can create new classes/bins using the toolbar 302.
  • Natural grouping matrix 314 allows the user to view how the images in the working group are distributed in the natural groupings. The number of images in a group is represented by a number in an element of the matrix 314. The user can click on an element in the matrix 314 to view all defect images in a particular natural grouping.
  • Fig. 4(a) shows an example of confusion matrix 306 where the manual and automatic classification are in agreement (see diagonal elements 402).
  • one image is agreed to be in class "3”
  • three images are agreed to be in class "2”
  • one image is agreed to be in class "1”.
  • Clicking on a "Correct” button next to the matrix will cause results in agreement to be highlighted.
  • Clicking on a "known errors” button will cause results not in agreement to be highlighted.
  • Clicking on the "image” button allows the user to view the images that were used to generate a particular element in the matrix.
  • Figs. 5(a) and 5(b) shows respective examples of an interface 502, 504 that allows the user to display in sorted order the images in the working set and in the training set.
  • the images are preferably sorted by such factors as lot number, manual bin, suggested bin, and size.
  • Fig. 6(a) is a flow chart showing a method for natural grouping of images in the working set 308.
  • images are captured for defect images.
  • Features are extracted from the images in element 604.
  • the extracted features are input to a natural grouping method 606, which can be any appropriate method.
  • the feature vectors of the images are grouped using a known Kohonen mapping technique.
  • the Kohonen map is seeded with non-random numbers to improve stability of the grouping and to make the grouping repeatable.
  • the images are displayed in their natural groups (also called clusters), as shown in Fig. 6(b).
  • the images are arranged to reflect the actual Kohonnen maps layout.
  • Figs. 10(a) and 10(b) are block diagrams of systems in accordance with the present invention distributed over a network, such as the internet or an intranet.
  • a network such as the internet or an intranet.
  • an optical, ebeam, or other types of inspection systems 1002 a classifier 1004/104, and an analysis system 1006 (see Fig. 1) are distributed over the network.
  • Fig. 10(a) an optical, ebeam, or other types of inspection systems 1002
  • a classifier 1004/104, and an analysis system 1006 are distributed over the network.
  • Fig. 1 an optical, ebeam, or other types of inspection systems 1002
  • a classifier 1004/104 are distributed over the network.
  • an analysis system 1006 see Fig. 1
  • Tool history includes, for example, the maintenance history of the tools or machine performing the inspection process and/or the manufacturing process. If the tool has been maintained according to its suggested maintenance schedule, its data may be weighted more than data from an unmaintained tool.
  • Tool History 1055 may also include a threshold of inspection value, indicating that maintenance must be found in order for the classifier to give credence to the data from that tool. This threshold may vary for individual tools or may be the same for all the tools of a particular type or function. Tool history may also indicate, for example, whether two runs of semiconductors where taken from the same tool (or which tool they were taken from). Thus, tool history 1055 may include, for example, equipment Ids.
  • iADC integrated ADC

Landscapes

  • Engineering & Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
  • Testing Or Measuring Of Semiconductors Or The Like (AREA)
PCT/US2000/032635 1999-11-29 2000-11-29 Power assisted automatic supervised classifier creation tool for semiconductor defects Ceased WO2001040145A2 (en)

Priority Applications (3)

Application Number Priority Date Filing Date Title
JP2001541837A JP2003515942A (ja) 1999-11-29 2000-11-29 半導体欠陥用のパワー・アシスト自動監視分類器作製ツール
EP00982744A EP1238367B1 (en) 1999-11-29 2000-11-29 Power assisted automatic supervised classifier creation tool for semiconductor defects
AU20541/01A AU2054101A (en) 1999-11-29 2000-11-29 Power assisted automatic supervised classifier creation tool for semiconductor defects

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US16795599P 1999-11-29 1999-11-29
US60/167,955 1999-11-29
US09/724,633 US6999614B1 (en) 1999-11-29 2000-11-28 Power assisted automatic supervised classifier creation tool for semiconductor defects
US09/724,633 2000-11-28

Publications (2)

Publication Number Publication Date
WO2001040145A2 true WO2001040145A2 (en) 2001-06-07
WO2001040145A3 WO2001040145A3 (en) 2002-07-11

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US (1) US6999614B1 (enExample)
EP (1) EP1238367B1 (enExample)
JP (1) JP2003515942A (enExample)
AU (1) AU2054101A (enExample)
WO (1) WO2001040145A2 (enExample)

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