Title: Circuit pattern inspection method and apparatus
Abstract: The present invention provides techniques, including a method and system, for inspecting for defects in a circuit pattern on a semi-conductor material. One specific embodiment provides a trial inspection threshold setup method, where the initial threshold is modified after a defect analysis of trial inspection stored data. The modified threshold is then used as the threshold in actual inspection.
Patent Number: 6,975,754 Issued on 12/13/2005 to Hiroi, et al.
| Inventors:
|
Hiroi; Takashi (Yokohama, JP);
Watanabe; Masahiro (Yokohama, JP);
Shishido; Chie (Yokohama, JP);
Kuni; Asahiro (Tokyo, JP);
Tanaka; Maki (Yokohama, JP);
Miyai; Hiroshi (Yokohama, JP);
Nara; Yasuhiko (Hitachinaka, JP);
Nozoe; Mari (Hino, JP)
|
| Assignee:
|
Hitachi, Ltd. (Tokyo, JP)
|
| Appl. No.:
|
802693 |
| Filed:
|
March 8, 2001 |
| Current U.S. Class: |
382/149 |
| Intern'l Class: |
G06K 009/00 |
| Field of Search: |
382/144,145,148,149,150,152
348/87,126
356/237.4,237.5
|
References Cited [Referenced By]
U.S. Patent Documents
| 4870357 | Sep., 1989 | Young et al.
| |
| 5043663 | Aug., 1991 | Lam.
| |
| 5502306 | Mar., 1996 | Meisburger et al.
| |
| 6087673 | Jul., 2000 | Shishido et al.
| |
| 6411377 | Jun., 2002 | Noguchi et al.
| |
| 6504948 | Jan., 2003 | Schemmel et al.
| |
| 2001/0000460 | Apr., 2001 | Ishihara et al.
| |
| Foreign Patent Documents |
| 57-196377 | Feb., 1982 | JP.
| |
Primary Examiner: Werner; Brian
Attorney, Agent or Firm: Townsend and Townsend and Crew LLP
Parent Case Text
CROSS-REFERENCES TO RELATED APPLICATIONS
This application is a divisional application of and claims priority to the following
prior non-provisional application:
U.S. patent application Ser. No. 09/791,911, titled "A Circuit Pattern Inspection
Method And Apparatus", by Takashi Hiroi et al., filed Feb. 22, 2001, now U.S. Pat.
No. 6,898,305.
The following commonly assigned, co-pending application is incorporated by reference
in their entirety:
U.S. patent application Ser. No. 09/450,856, "Inspection Method, Apparatus and
System for Circuit Pattern," by Nara Yasuhiko, et. al, filed Nov. 29, 1999.
Claims
1. A method of resetting a threshold using a display coupled with a computer,
said method comprising:
displaying a first standard on said display, said first standard used to select
defect candidate images among defect candidate images stored in a memory beforehand;
graphically displaying on said display a distribution of defects in a wafer map
format in which said defects are selected from said defect candidate images stored
in said memory by applying said first standard;
changing said first standard to a second standard on said display; and
changing said graphical display of wafer format in response to said change to
said second standard by applying said second standard to said defect candidate
images stored in said memory.
2. The method of claim 1 further comprising:
selecting a selected indication of said defect candidate image indications; and
viewing an inspection image associated with said selected indication.
3. The method of claim 1 wherein said first standard is calculated using an electron
beam noise value for a SEM system.
4. The method of claim 1 wherein the graphical display which is changed in response
to said change to said second standard is used to judge an effect of said change
to said second standard.
5. The method of claim 4 wherein the graphical display which is changed in response
to said change to said second standard is used to judge whether said change to
said second standard is proper.
6. The method of claim 1 further comprising graphically displaying a relation
between defect density and threshold in which said first standard is indicated.
7. A method in a computer system for displaying a defect candidate, said defect
candidate stored in a memory with an expanded view of said defect candidate, said
method comprising:
displaying a two-dimensional defect candidate distribution in a wafer map format
on a first screen in which defect candidates displayed on said first screen are
selected from said defect candidate images stored in a memory by applying a standard; and
displaying on a second screen an expanded view of defect candidate stored in
said memory, responsive to a selection of a defect candidate among defects in said
wafer map format displayed on said first screen,
wherein said two-dimensional defect candidate distribution displayed on said
first screen changes by changing said standard.
8. The method of claim 7 further comprising changing said standard to change
said two-dimensional defect candidate distribution displayed on said first screen.
9. The method of claim 8 wherein said two-dimensional defect candidate distribution
displayed on said first screen which is changed in response to said change of said
standard is used to judge an effect of said change of said standard.
10. The method of claim 9 wherein said two-dimensional defect candidate distribution
displayed on said first screen which is changed in response to said change of said
standard is used to judge whether said change of said standard is proper.
11. The method of claim 7 wherein said expanded view comprises an image associated
with said defect candidate and selected from a group consisting of a clipped inspection
image, a clipped reference image, or a defect candidate image.
12. The method of claim 7 wherein said expanded view comprises a re-scanned image
of said defect candidate.
13. The method of claim 7 further comprising a threshold screen for changing
said threshold.
14. The method of claim 7 further comprising a screen displaying a graph of defect
density versus threshold.
15. The method of claim 7 wherein said two-dimensional defect candidate distribution
displays defect candidates responsive to a user selected area.
16. The method of claim 7 wherein said two-dimensional defect candidate distribution
displays defect candidates by type of defect.
17. The method of claim 16 wherein each type of defect has a different symbol,
said defect being displayed using a symbol.
18. The method of claim 16 wherein each type of defect has an associated threshold value.
19. The method of claim 7 wherein said two-dimensional defect candidate distribution
displays defect candidates as symbols.
20. The method of claim 19 wherein a symbol of said symbols comprise a grayscale value.
21. The method of claim 20 wherein said grayscale value is related to a margin.
22. The method of claim 20 wherein said grayscale value is related to an enhanced result.
23. The method of claim 19 wherein a symbol of said symbols comprise a color value.
24. The method of claim 19 wherein a symbol of said symbols comprise a black
or a white value.
25. A method of resetting a threshold using a display coupled with a computer
and displaying a defect candidate, said defect candidate stored in a memory with
an expanded view of said defect candidate, said method comprising:
displaying a first standard on said display, said first standard used to select
defect candidate image indications to store in a memory and to be shown on a defect
candidate distribution screen of said display;
graphically displaying a relation between defect density and threshold in which
said first standard is indicated;
displaying a two-dimensional defect candidate distribution for said first standard
on a first screen, said two-dimensional defect candidate distribution comprising
an indication of said defect candidate;
displaying on a second screen said expanded view of said defect candidate stored
in said memory, responsive to a selection of said indication on said first screen;
changing said first standard to a second standard on said display; and
changing said graphical display of said relation in response to said change to
said second standard by applying said second standard to said defect candidate
image indications selected by said first standard and stored in said memory.
26. The method of claim 25 wherein said two-dimensional defect candidate distribution
displayed on said first screen changes by changing said first standard to said
second standard.
27. The method of claim 25 further comprising:
selecting a selected indication of said defect candidate image indications; and
viewing an inspection image associated with said selected indication.
28. The method of claim 25 wherein said first standard is calculated using an
electron beam noise value for a SEM system.
29. The method of claim 25 wherein said expanded view comprises an image associated
with said defect candidate and selected from a group consisting of a clipped inspection
image, a clipped reference image, or a defect candidate image.
30. The method of claim 25 wherein said expanded view comprises a re-scanned
image of said defect candidate.
31. The method of claim 25 further comprising a threshold screen for changing
said threshold.
32. The method of claim 25 further comprising a screen displaying a graph of
defect density versus threshold.
33. The method of claim 25 wherein said two-dimensional defect candidate distribution
displays defect candidates responsive to a user selected area.
34. The method of claim 25 wherein said two-dimensional defect candidate distribution
displays defect candidates by type of defect.
35. The method of claim 34 wherein each type of defect has a different symbol,
said defect being displayed using a symbol.
36. The method of claim 34 wherein each type of defect has an associated threshold value.
37. The method of claim 25 wherein said two-dimensional defect candidate distribution
displays defect candidates as symbols.
38. The method of claim 37 wherein a symbol of said symbols comprise a grayscale value.
39. The method of claim 38 wherein said grayscale value is related to an enhanced result.
40. The method of claim 38 wherein said grayscale value is related to a margin.
41. The method of claim 37 wherein a symbol of said symbols comprise a color value.
42. The method of claim 37 wherein a symbol of said symbols comprise a black
or a white value.
Description
BACKGROUND OF THE INVENTION
The present invention generally relates to inspection in a semiconductor manufacturing
process and in particular to using an inspection system to inspect for defects
in a circuit pattern on a semiconductor material. The circuit pattern may include,
a Liquid Crystal Diode (LCD) display, a Thin Film Transistor (TFT) display, a memory
matte, an integrated circuit, a photomask, a magnetic head, and the like. The inspection
system may include a Semiconductor Electron Microscope (SEM) detection system,
an optical detection system, a X-ray detection system, a Focus Beam Ion detection
system, a Transparent Electron Microscope (TEM) detector system, a particle detection
system, and the like.
FIG. 1 shows a simplified layout of a semiconductor wafer
100 which is
a target of an inspection system. There are many die, for example,
110,
112, and
114 on the wafer
100. Normally each die has the same
pattern on the wafer
100 for use in the same product.
FIGS. 2 and 3 each show a conventional inspection system (an example is given
in U.S. Pat. No. 5,502,306, "Electron Beam Inspection System and Method," by Meisburger,
et. al., issued Mar. 26, 1996. Another example is given in U.S. Pat. No. 6,087,673,
"Method for Inspecting Pattern and Apparatus Thereof," by Shishido, et. al., issued
Jul. 11, 2000) In the conventional system, a SEM Detecting Apparatus
208
is connected to an Image Processing System
228. The SEM Detecting Apparatus
includes, an electron beam
210 from an electron source
212 sending
electrons to a wafer
100 through an objective lens
214. The secondary
electron emissions
216 from the wafer
100 are detected by a sensor
218. A beam deflector
220 causes the electron beam
210 to
scan horizontally, while stage
222 movement causes a vertical scan. Thus
a two dimensional (x-y) image is obtained. The resulting analog sensor signals
are converted to digital data and this two-dimensional digital image is sent to
Image Processing System
228 for defect detection.
In the Image Processing System
228 the first digital image is stored as
a reference image. Another scan of a different potion of the wafer, produces a
second digital image. This second image is the inspection image which may or may
not be stored, and is compared with the reference image. As the two images are
presumed to have the same pattern, a difference image is formed. The difference
image is thresholded with an initial threshold, and when a defect image exists,
a defect is determined to exist. Defect information such as defect position (x-y
coordinates), size (area), x-projection size, and y-projection size is also generated.
The defect information forms an entry in a defect list
232. The above process
is repeated with the inspection image, i.e., second digital image, being stored
as the reference image and overwriting the stored first digital image. A newly
scanned image, i.e., third digital image, is the new inspection image and replaces
the second digital image. The result of this repetitive process is a defect list
232. This defect list
232 is sent by the Image Processing System
228 to a Graphic User Interface (GUI) Console
230 for verification
by the user. If the user desires to view a defect in the defect list
232,
the positional information is used to re-scan the defect area and show the defect
on the console
230.
The conventional Image Processing System
228 normally operates in one
and only one of two detection modes at a time. One is a die to die comparison mode
234 (FIG. 2) and the other is an array comparison mode
236 (FIG.
3). The die to die comparison
234 compares one die image with the next die
image, where each die belongs to the same product. Array comparison
236
compares a repeated pattern in, for example, a memory matte, on a die. Thus the
conventional image processing system has a problem in that a mixture of die to
die comparison and array comparison cannot be done in one scan of the wafer.
In the conventional system determining the threshold to be used in the difference
images during actual inspection of the wafer
100 is very important. Since
the defect image is determined from thresholding the difference image, too low
a threshold may cause many false defects. Too high a threshold may miss many actual
defects. Thus setting up the threshold is an important part of the inspection process.
FIG. 4 shows a conventional threshold setup method. At step
310 the user
sets a value for the initial threshold based on the user's best guess as to the
maximum noise level in the images. The initial threshold is typically set low and
is raised to a higher value when applying this setup method. The user also selects
a small region of the wafer for trial inspection. The conventional system, using
the initial threshold, determines a defect list
232, including defect information
(step
320), which is sent to the Graphical User Interface (GUI) console
230 for user evaluation. At step
330, the defect information is used
to re-scan the defect locations on the wafer
100 and display the defects
for verification. The user then verifies whether the defects are true or false
defects. At step
340, if there are too few true defects or too many false
defects, the user sets a new higher threshold, and the system goes back to step
310. Typically this loop must be repeated one to three times, before a final
threshold is determined. In FIG. 4 user operation is indicated by a bold box
360.
Thus step
310 and
330 involve user operation
360. This threshold
setup method has several problems. First it is slow and manually intensive. Since
defect images are not stored, rescanning is necessary to view the defect list
232
for verification. As scanning requires wafer stage
222 movement, this process
takes time. If the user determines that the threshold is too low, the user must
guess at a new level. The results of the threshold modification are available only
after the small region is re-scanned during a second trial inspection. The above
process is repeated several times and is slow. Another problem is that the repetitive
re-scanning of the wafer
100 could alter the wafer surface and hence the
inspection results. Lastly, no image data is retained for use in actual inspection
or follow-up analysis, thus it is difficult to improve the process.
Therefore there is a need for a defect inspection method and system that
is faster and more efficient. There is also a need for maintaining defect image
data for use in, for example, trial inspection, defect analysis, actual inspection,
and/or after inspection analysis.
SUMMARY OF THE INVENTION
The present invention provides techniques, including a method and system, for
inspecting for defects in a circuit pattern on a semi-conductor material. One specific
embodiment provides a trial inspection threshold setup method, where the initial
threshold is modified after a defect analysis of trial inspection stored data.
The modified threshold is then used as the threshold in actual inspection.
One embodiment of the present invention provides a method for inspecting a specimen,
for example, a circuit pattern on a semiconductor wafer. The method includes: setting
a threshold value. Next, a detected image of a specimen is detected and compared
to a reference image; A defect candidate is then extracted using the threshold
value and an information of the defect candidate is stored to a memory. A new threshold
value is set and a defect is extracted from the defect candidate using the stored
information and the new threshold value. The information of the defect may include
at least one of the following: a defect candidate position, a defect candidate
area, a defect candidate x and y projection size, a maximum difference between
the detected image and the reference image, a defect texture, or a reference texture
or an image of the defect candidate.
In an alternative embodiment of the present invention a method for inspecting
a circuit pattern on a semiconductor material is provided. First an initial threshold
is set. Next, an inspection image is detected. A defect candidate information,
for example, a margin, is determined by thresholding a comparison between the inspection
image and a reference image, where the thresholding uses the initial threshold.
A new threshold is determined using the defect candidate information and a defect
in the inspection image is evaluated using said new threshold.
Another embodiment of the present invention stores raw images received from
a detector system, for example, a Semiconductor Electron Microscope (SEM) detecting
apparatus, which scans dies on a semi-conductor wafer. An initial threshold is
set from an average of the electron beam noise. From the stored raw images, inspection
images and corresponding reference images are extracted using die to die comparison
and/or array comparison. A difference between an inspection image and its corresponding
reference image is thresholded, using the initial threshold, to determine if a
potential or candidate defect exists in the inspection image. The defect is, at
this stage a potential or candidate defect, as it later will be verified by the
user to be either a true or a false defect. Clipped images of the inspection and
reference images, along with defect candidate information, are stored in a computer
readable medium. Next using the clipped images of the inspection and reference
images, a defect distribution is obtained. Using this defect distribution a new
threshold is determined. A GUI display is provided showing a two dimensional defect
distribution with symbols representing the defect candidates with thresholds equal
to or above the new threshold. By selecting a symbol the user can verify the defect,
as a true or false defect, in an expanded view, showing, for example, the clipped
inspection image associated with the symbol. After verifying a plurality of defect
candidates, the user may set another threshold, and again view the results responsive
to this other threshold. As the images are stored re-scanning is not necessary.
The end result is a threshold that may be used for actual inspection. This threshold
is obtained faster and more efficiently than the conventional method. In addition,
the stored images may be used for actual inspection and after inspection analysis.
One embodiment of the present invention provides a method, using a computer,
for performing defect analysis on a plurality of images from an inspection system.
The method includes, storing the plurality of images in a computer readable medium;
retrieving an inspection image from a first image of the stored plurality of images;
retrieving a corresponding reference image from a second image of the stored plurality
of images; and analyzing the inspection image and corresponding reference image
to determine if a true defect exists. In addition, in some cases the first and
second image may be the same image.
A second embodiment of the present invention provides a method, using a computer,
for inspecting for defects in a circuit pattern, including determining if there
is a defect candidate image, by thresholding a difference image, where the difference
image comprises the difference between an inspection image and a corresponding
reference image. And if there is a defect candidate image, storing a clipped inspection
image and a corresponding clipped reference image. In addition defect candidate
information, including, defect candidate positional coordinates, is stored. A margin
is also determined using the clipped inspection image and the clipped reference
image. Optionally, calculations for determining a classification, a threshold for
a type of defect, or an enhanced result may be done.
A third embodiment of the present invention provides an inspection system for
examining
a plurality of images having potential defects in a circuit pattern on a semiconductor
material. The system includes, a defect image memory for storing clipped images
of the plurality of images; an image analyzer, comprising a plurality of processors,
coupled with the defect image memory, for analyzing the clipped images retrieved
from the defect image memory; and a non-volatile storage coupled with the image
analyzer for storing the clipped images and results of the analyzing.
A forth embodiment of the present invention provides a method for detecting defects
in a circuit pattern on a semiconductor material using an inspection system. First,
a plurality of scanned images from a detecting apparatus are stored. Next an inspection
image and a reference image are determined from the plurality of scanned images
based on a selection of either die to die comparison or array comparison. And using
the inspection image and the reference image, a defect candidate image is determined.
A fifth embodiment of the present invention provides an image processing system
for detecting defects in a circuit pattern on a semiconductor material using images
from a detecting apparatus. The image processing system includes, an image memory
for storing the images; and a defect detection image processing module for detecting
defect candidate information from stored images, where the stored images include
an inspection image and/or a reference image.
A sixth embodiment of the present invention provides a method for determining
an
updated threshold for use in actual inspection of a semiconductor wafer. The method
includes: setting an initial threshold; determining a plurality of difference metrics
using the initial threshold; determining a difference distribution based on the
plurality of difference metrics; and determining the updated threshold based on
an evaluation of the difference distribution.
A seventh embodiment of the present invention provides a method of resetting a
threshold using a display coupled with a computer. The method includes, displaying
a first threshold value, were the first threshold value is used to select the defect
candidate image indications to be shown on a defect candidate distribution screen
of the display; changing the first threshold value to a second threshold value,
wherein the defect candidate image indications on the defect distribution screen
change responsive to the second threshold.
A eighth embodiment of the present invention provides a method in a computer
system
for determining a threshold for use in actual inspection of a semiconductor material,
comprising a circuit pattern. A first threshold and a second threshold are displayed.
In addition a graphic representation of a defect candidate image with a margin
greater than or equal to the second threshold minus the first threshold is displayed;
Next, when the graphic representation of the defect candidate image is selected
for expanded viewing, a clipped image associated with the graphic representation
is shown; and when the defect candidate image is a false defect, and a predetermined
number of allowable false defects is exceeded, a new second threshold is received
from the user. The selected clipped image is selected from a group consisting of
a clipped inspection image, a clipped reference image, or a clipped defect candidate image.
A ninth embodiment of the present invention provides a method in a computer system
for displaying a defect candidate, where the defect candidate is stored in a memory.
The method includes: displaying a two-dimensional defect candidate distribution
for a threshold on a first screen, the two-dimensional defect candidate distribution
including an indication of the defect candidate; and displaying on a second screen
an expanded view of the defect candidate, responsive to a selection of the indication
on the first screen.
A tenth embodiment of the present invention provides a distributed system for
inspecting
semiconductor circuit pattern defects. The system includes: an inspection apparatus
for acquiring a plurality of images associated with the semiconductor circuit pattern
defects and for performing defect analysis on a plurality of stored images; a server
connected to the inspection apparatus via a communications network for storing
the plurality of images, and for providing access to the plurality of stored images;
and a client computer connected to the server and the inspection apparatus via
the communications network for displaying a plurality of symbols associated with
selected images of the plurality of stored images in response to selection of the
selected images by the defect analysis. In an alternative embodiment the defect
analysis is performed by the server instead of the inspection apparatus.
A eleventh embodiment of the present invention provides a method for determining
an inspection threshold used in actual defect inspection of a semiconductor. First,
a first threshold using a defect difference distribution is calculated; next a
second threshold based on said first threshold is stored in a computer readable
medium and then used in actual inspection.
In another embodiment of the present invention a method for determining a selected
threshold of a plurality of thresholds, where the plurality is used in actual defect
inspection of a semiconductor, is provided. The method includes: determining the
plurality of thresholds from a defect difference distribution; displaying to a
user an indication, such as a user selectable button, for each of the plurality
of thresholds; and responsive to a user selection of a selected threshold, displaying
symbols of defects with differences greater than or equal to the selected threshold.
These and other embodiments of the present invention are described in more
detail in conjunction with the text below and attached figures.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 shows a simplified layout of a semiconductor wafer which is a target
of an inspection system;
FIGS. 2 and 3 each show a conventional inspection system;
FIG. 4 shows a conventional threshold setup method;
FIG. 5 shows a simplified block diagram of an embodiment of an inspection system
of the present invention;
FIG. 6 shows an inspection system for another embodiment of the present invention;
FIG. 7 shows an inspection system for yet another embodiment of the present invention;
FIG. 8 shows a flowchart for a threshold set up method of an embodiment of the
present invention;
FIG. 9 show an expanded flowchart of the initial setup and trial inspection
steps of FIG. 8 of an embodiment of the present invention;
FIG. 10 gives an example of the initial setup and trial inspection;
FIG. 11 shows an expanded view of the threshold calculation step of FIG. 8 of
an embodiment of the present invention;
FIG. 12 show was an expanded view of the threshold modification and the judgment
step of FIG. 8 of an embodiment of the present invention;
FIG. 13 shows an alternate embodiment of the threshold modification and judgment
step of FIG. 8;
FIG. 14 gives an example of the threshold setup method of an embodiment of the
present invention;
FIG. 15 is a schematic view of a GUI display used in the Threshold Modification
and Re-judgement step of FIG. 8 of an embodiment of the present invention;
FIG. 16 shows a GUI of another embodiment of the present invention;
FIG. 17 shows a GUI of yet another embodiment of the present invention;
FIG. 18 shows a display of an embodiment of the present invention having defect
candidate images classified according to type;
FIG. 19 shows a distributed system for an embodiment of the present invention;
FIG. 20 shows a flowchart for using a stored recipe in actual inspection of
an embodiment of the present invention;
FIG. 21 shows thresholds that may be used in actual inspection 2130 of
an embodiment of the present invention; and
FIG. 22 shows an example of defect difference distributions of an embodiment
of the present invention.
DESCRIPTION OF THE SPECIFIC EMBODIMENTS
FIG. 5 shows a simplified block diagram of an embodiment of an inspection system
of the present invention. The embodiment of the inspection system includes a Defect
Detection Processing Unit
410 and a Defect Image Processing Unit
430.
A SEM detection apparatus
208 is coupled with a Defect Detection Processing
Unit
410. The SEM Detection Apparatus
208 is the same as in FIGS.
2 and 3. The Defect Detection Processing Unit
410 includes an image memory
(not shown) for storing the two dimensional (x-y) images from the SEM Detection
Apparatus
208, a die to die comparison module
412 and an array comparison
module
414. The images stored in image memory are raw images of the scan
of the wafer
100 by the SEM Detection Apparatus
208. The image memory
may include images of an entire wafer
100 or a section of the wafer. If
only a section of the wafer is included, the image memory acts as a buffer or queue,
inputting new raw images from the SEM Detection Apparatus
208 and outputting
raw images to be processed by die to die comparison module
412 or the array
comparison module
414. Since raw images are stored, either die to die comparison
or array comparison may be done with one scan. A user via a control console inputs
into the Defect Detection Processing Unit
410, the wafer and die layout.
The input includes, a die pitch which is used in die to die comparison and a cell
pitch which is used in array comparison. The die to die comparison module
412
has function similar to the die to die comparison
234 performed in the Image
Processing System
228 of FIG. 2, but in the Defect Detection Processing
Unit
410, a defect candidate
416 is produced rather than an entry
in a defect list
232. In addition the Defect Detection Processing Unit
410
has an array comparison module
414 for performing an array comparison like
236 in Image Processing System
228 of FIG. 3, and it also produces
a defect candidate
418. A defect candidate is a potential defect, which
may be a true, i.e., actual, or a false defect. On one or more of the defect candidates,
the user will later verify, whether the defect candidate represents a true or false
defect. When a defect candidate, for example
416 or
418, is determined
to exists, clipped images, including a clipped inspection image having the defect
candidate, and a corresponding clipped reference image (and optionally a clipped
defect candidate image), and defect candidate information is outputted by the Defect
Detection Processing Unit
410 to the Defect Image Processing Unit
430.
The Defect Detection Processing Unit
410 has program code for clipping the
images and for determining the defect candidate information, for example, defect
candidate position, area, x & y projection sizes, and optionally a margin.
The Defect Image Processing Unit
430 includes, a Defect Image Memory
432,
Multiple Processor Elements
434, a Monitor
436, and System Software
438. The Defect Image Memory
432 receives and stores the clipped
images
420 and the defect candidate information from the Defect Detection
Processing Unit
410. The Multiple Processor Elements
434, include
one or more processors, and perform the defect analysis on the data stored in Defect
Image Memory
432 using System Software
438. The defect analysis results
are displayed to the user on Monitor
436.
FIG. 6 shows an inspection system for another embodiment of the present invention.
The SEM Detecting Apparatus
510 has a Semiconductor Electron Microscope
(SEM) and is coupled with an Image Processing System
512, which analyzes
the images from the SEM. The Image Processing System
512 includes an Image
memory
520, a Defect Detection Image Processing Circuit
530, a Defect
Image Memory
550, and an Image Analyzer
560. The Image Processing
System
512 is coupled with a Monitor
575 and a non-volatile Storage
Medium
570. The Image Memory
520 and the Defect Detection Image Processing
Circuit
530 perform functions the same as or similar to the Defect Detection
Processing Unit
410 of FIG. 5. The Image Analyzer
560 has functions
similar to the Multiple Processor Elements
434 and System Software
438
of FIG. 5.
The SEM Detecting Apparatus
510 scans a wafer and records the images in
Image Memory
520. In order to maintain a reasonable memory size, Image Memory
520 may be a fixed size queue or buffer. A defect candidate is located as
the result of a comparison check performed by a Defect Detection Image Processing
Circuit
530 using a reference image and an inspection image stored in the
Image Memory
520. In one embodiment the reference image is subtracted from
the inspection image and thresholded using an initial threshold th
0. If
there exists a binary defect image then a defect candidate exists. The defect candidate
positional information
540 is calculated by Defect Detection Image Processing
Circuit
530, and the Image Processing System
512 clips an image of
the defect candidate out of the inspection image and clips a corresponding image
out of the reference image and stores the clipped images into a Defect Image Memory
550. An Image Analyzer
560 performs defect analysis on the clipped
images stored in the Defect Image Memory
550. In one embodiment defect analysis
includes determining a new threshold th
1 based on a difference distribution.
The Image Analyzer
560 includes a multiprocessor system, having a plurality
of processors, where each processor may concurrently analyze a set of clipped images,
for example, a clipped inspection image, and a clipped reference image (optionally,
a clipped defect candidate image may also be included).
In one embodiment the Image Analyzer
560 calculates a defect detection
margin, herein also called "margin," from the clipped inspection image and the
corresponding clipped reference image stored in the Defect Image Memory
550.
The defect detection margin is a threshold range from the initial threshold level
(for example, th
0) to the maximum up to which the defect can be detected.
By calculating the defect detection margin per defect candidate, inspection results
can be viewed as the threshold setting changes and inspection need not be conducted
again. The clipped images, defect positional information, and defect detection
margins are written onto the Storage Medium
570. Media such as DVD, CD,
and HD may be used as the storage medium
570. The storage medium may be
accessed via a communications network.
FIG. 7 shows an inspection system for yet another embodiment of the present
invention. The Optical Detecting Apparatus
610 has an optical device used
to inspect semiconductors and is coupled with an Image Processing System
612,
which analyzes the images from the an Optical Inspection Apparatus
610.
The Image Processing System
612 includes an Image Memory
620, a Defect
Detection Image Processing Circuit
630, a Defect Image Memory
650,
and an Image Analysis System
660. The Image Processing System
612
is coupled with a Monitor
675 and a non-volatile Storage Medium
670.
In effect the Image Processing System
612 is similar to the Image Processing
System
512 for the SEM Detecting Apparatus
510. Thus embodiments
of the Image Processing System of the present invention may be used with other
detecting systems, for example, a Focus Ion Beam System, or a Transparent Electron
Microscope (TEM) system, and are not limited to the embodiments for the SEM or
Optical Detecting Systems described herein.
FIG. 8 shows a flowchart for a threshold set up method of an embodiment of the
present invention. At step
710, the initial threshold is set either manually
by the user or automatically by the inspection system. The inspection system measures
the electron beam
210 noise and uses the average electron beam noise as
the initial threshold, th
0. Next trial inspection (step
720) is performed
by determining defect candidates, determining defect candidate information and
images, and clipping the images. At step
730, another threshold (th
1)
is determined from defect analysis. The user views the results of the defect analysis,
including a thresholded defect candidate distribution, on a GUI display on a monitor.
Using this display the user verifies one or more of the defect candidates and may
modify the threshold th
1 (step
740) to, for example, a value th
2.
The Image Analyzer
560 calculates a new a thresholded defect candidate distribution
for th
2 and displays it on the monitor. As the Defect Image Memory
520
has the necessary defect candidate images and information, the user can reset thresholds,
verify defects in images, and view the results without the need to rescan the wafer.
Although optional re-scan of certain areas may be provided, it is not necessary.
Thus as shown by the bolded User Operation box
760 in FIG. 8, the user is
only needed at step
740. Once the threshold is determined, the actual inspection
(step
750) is performed. At least one step, Threshold Modification and Re-judgement
(step
740) may optionally be used in the actual inspection (step
750)
to modify the threshold. In another embodiment step
740 is not used and
th
1 is used as the threshold for actual inspection.
In addition as the defect candidate images and information are stored on a non-volatile
storage medium
570, this data may be used for after-inspection analysis.
For example, the steps
710 to
740 can be repeated and analyzed to
evaluate if a better threshold could have been determined. If so, then corrective
measures on the process or on operator training may be instituted.
FIG. 9 shows an expanded flowchart of the initial setup (step
710) and
trial inspection (step
720) steps of FIG. 8. At process
810 the initial
threshold (th
0) is set either manually by the user or automatically using
a standard metric for the system, for example, using electron beam noise. An inspection
image
812, having a potential or candidate defect, and a reference image
814, corresponding to the inspection image
812, are subtracted
816
from each other to give a difference image
818. The initial threshold
810
is then used to threshold the difference image
818 (process
820)
and to generate a binary defect candidate image
824 and defect candidate
information
822, for example defect candidate position, defect candidate
area, defect candidate x and y projection sizes, maximum difference between inspection
and reference images, defect texture, reference texture, average difference inside
a standard circle or average difference inside several selected standard circles
in a pattern of repeated standard circles. In an alternative embodiment the margin
may be calculated. The defect candidate information
822 is used at process
830 to clip the inspection image
812, the reference image
814,
and the defect candidate image
824, resulting in a clipped reference image
832, a clipped inspection image
834, and a clipped defect candidate
image
836. The clipped defect candidate image
836 is optional and
is provided to assist the user in viewing the potential defect. Clipped reference
image
832 and clipped inspection image
834 are used to calculate
the margin in process
840. Note the margin could also have been calculated
in an alternative embodiment at process
820. At process
840 optionally
other calculations may be gone, for example, classification of the defect candidates,
thresholding of the defect candidates by a type of defect, or an enhanced result.
For an enhanced result a predetermined normal threshold thN is set. The normal
threshold is greater than or equal to th
0. The enhanced result for a defect
candidate is the normal threshold thN subtracted from the defect candidate's maximum
threshold (i.e., the largest threshold at which a defect candidate can be detected).
The enhanced result is similar to a normalized value. The results of process
840,
the defect information
822, the clipped reference image
832, the
clipped inspection image
834, and the clipped defect candidate image
836,
are stored in a non volatile storage media (process
842). In another embodiment
only the margin is calculated in process
840 and stored in the storage medium
(process
842). The other optional calculations, for example, classification
of the defect candidates, thresholding of the defect candidates by a type of defect,
or an enhanced result are done, using, for example, the stored data in storage
medium
570, when the defect candidates are displayed, for example, in FIG.
18. In another embodiment, the only information calculated in process
840
is for example the first, second and third local maximums in the difference distribution
graph. These are stored in the storage medium (process
842).
FIG. 10 gives an example of the initial setup and trial inspection. In
910
an inspection image
912 having a cross-section
914 and clipped inspection
image
916 are shown. The clipped inspection image
916 may have dimensions
of 128×128 pixels. In
910 there is a graph
920 showing signal
amplitude
924 versus pixel location
922 for cross-section
914.
A defect candidate
926 is shown on graph
920. In
930, a reference
image
932, a cross-section
934 of the reference image
932,
and a clipped reference image
936 is shown. A graph
938 shows the
background noise in reference image
932 along cross-section
934.
In
950, a difference image
952 having a cross-section
954
is shown. The graph
956 has as its y-axis
958 the difference in signal
amplitude from graphs
920 and
938. The defect candidate signal
960
associated with the defect candidate
926 has defect signal maximum difference
962. The noise
964 results from the difference in noise from inspection
image
912 and reference image
932. The initial threshold th
0
966 in one embodiment is manually or automatically set to the noise
964.
The margin
968 is the difference between the defect signal maximum difference
962 and the threshold th
0 966. In an
970 a defect candidate
image
972, having cross-section
974, and a clipped defect candidate
image
978 are shown. The defect candidate or potential defect
980
can be readily seen. In
970 a graph
982 with a binary amplitude
984
is shown for defect candidate image cross-section
974.
FIG. 11 shows an expanded view of the threshold calculation step
730
of FIG. 8. The clipped reference image
1010 and clipped inspection image
1012 are received at process
1014. These images are retrieved from
the storage medium
570 or are used directly from FIG. 9 (images
832
and
834). At process
1014, first the clipped reference image
1010
is subtracted from the clipped inspection image
1012 to obtain a difference
image. Next a difference metric is obtained from the difference image, for example,
the signal amplitude above threshold th
0 for a cross-section of the difference
image is calculated. In an alternative embodiment the difference metric for the
difference image is the margin (process
1016). The difference metric for
each defect candidate is used to determine a difference distribution over all the
defect candidates (process
1018). At process
1020 a new threshold
th
1 is determined, for example, using the first local minimum in the difference
distribution. In another embodiment defect density, i.e., frequency per unit area,
versus difference is first plotted. Next the area from a threshold thX to infinity
is calculated for each difference, i.e., threshold, value. Where there is a plateau,
i.e., the area does not substantially change, the defect density is determined
to be stabilized and the threshold th
1 is set at one of the plateau values.
In another embodiment a fixed defect count or a fixed defect density may be set
as th
1. In yet another embodiment, a 3 DB point above the minimum at infinity
may be set as threshold th
1 in a defect density diagram such as 1452 in
FIG. 15.
FIG. 12 shows an expanded view of the threshold modification and Re-judgment
step
740 of FIG. 8. At process
1110 the initial threshold th
0
and the threshold th
1 calculated from step
730 are displayed. Next
a two dimensional defect candidate difference distribution is displayed using symbols
or indications representing defect candidate images with margins greater than or
equal to (th
1-th
0). At process
1114 a symbol representing
a defect candidate is selected for expanded view. One to three images, for example
the clipped reference image, the clipped inspection image, and/or the clipped defect
candidate image, may be displayed in another screen. Optionally the defect area
may be SEM re-scanned and/or optical rescanned, and the corresponding image(s)
displayed. The expanded image(s) of the defect candidate is verified by the user
to be a true or false defect (process
1116). If there are more defect candidates
to check (decision
1118) then process
1114 is returned to. If there
are no more defect candidates to check then at decision
1120 it is determined
if there are an allowable number of false defects. If there are allowable number
of false defects, then the threshold setup process of FIG. 8 is complete (process
1122) and actual inspection is performed (step
750). If there are
too many false defects, then at decision
1120 a new threshold level is set
for th
1 by the user and process
1110 is repeated.
FIG. 13 shows an alternate embodiment of the Threshold Modification and Re-judgment
step
740 of FIG. 8. At process
1210 thresholds th
0 and th
1
are displayed. At process
1212 defect candidate images with difference metric's
greater than or equal to th
1 are displayed. At process
1214 when
an indication of a defect candidate image is selected for expanded view, the associated
clipped inspection image is displayed. At process
1216 the user views the
clipped inspection image and verifies if the defect candidate is a true or a false
defect. At decision
1218 a test is made to see if there are more defect
candidates to check. If yes then the process returns to process
1214. If
no then the allowable number of false defects is checked (decision
1220).
If there are an allowable number of false defects then the threshold setup process
is finished (process
1222). If there are too many false defects, then the
threshold level th
1 is set to a new value at process
1224. At process
1226 new defect candidate images are generated and the process returns to
process
1210.
FIG. 14 gives an example of the threshold setup method of an embodiment of the
present invention. The graph
1308 shows frequency
1310 versus the
difference metric
1312. Graph
1308 includes a sub-graph
1322
showing a Gaussian noise distribution and sub-graph
1324 showing a defect
distribution. The area
1314 under the Gaussian noise curve
1322 is
a normal frequency distribution without any defects. The area
1318 under
the defect distribution curve
1324 gives the frequency of defects at a specified
threshold. Graph
1308 represents all the differences prior to any thresholding.
Graph
1325 shows the results of the initial setup and trial inspection steps
710 and
720 of FIG. 8. The initial threshold th
0 1326
is set. All differences below the threshold th
0 had been removed. The normal
noise above threshold
1326 includes areas
1328 and
1330. Graph
1334 shows the results of the threshold calculation step
730 of FIG.
8. The new threshold th
1 1340 is set as the first minimum or valley
between curve Gaussian noise curve
1322 and defect distribution curve
1324.
Graph
1342 shows the result of the Threshold Modification and Re-judgment
step
740 of FIG. 8. This step
740 may be optional, but, if it is
included, it uses user selection from a GUI to modify the threshold from threshold
th
1 1340 to threshold th
2 1346. For this example the
optimal threshold, thresholds out areas
1328 and
1330 as they are
noise and retains the defect area
1318. In this example the optimal threshold
is th
2.
FIG. 15 is a schematic view of a GUI display used in the Threshold Modification
and Re-judgement step
740 of FIG. 8 of an embodiment of the present invention.
The GUI is used for checking inspection results after trial inspection and threshold
calculation of th
1. On a map display area
1410 on the display, the
small solid square marks, for example,
1412,
1414,
1416, and
1420, indicate the locations of detected defect candidates. When one of
these marks (i.e., symbols) is selected, for example,
1420, and dragged
to the expanded image display area
1430, the clipped inspection image of
the defect candidate stored in the defect image memory
550 is displayed
in the expanded image display area
1430. A defect category input box, not
shown on figure, is also displayed on the GUI. Defect category examples are hole
missing, high impedance, foreign particle, and short circuit. In another embodiment,
the clipped inspection image, the clipped reference image, the clipped defect candidate
image or any combination thereof, may be shown in the expanded image display area
1430. In an alternate embodiment a re-scanned SEM and/or a re-scanned optical
image(s) of the defect area may be displayed. In a further embodiment, if these
images are in the Image Memory
520 a re-scan may be skipped and the images
recalled from memory.
Buttons
1432 and
1434 allows a choice of automatic
1432
or manual
1434 threshold re-setting. In this example, it is assumed that
the Auto button
1432 is chosen. On horizontal bar
1440 there is an
initial threshold of th
0 that has been preset before trial inspection and
on horizontal bar
1442 there is a recommended threshold of th
1 that
has been automatically calculated, for example at step
730 of FIG. 8. When
the Execute button
1444 is selected, the defect candidates which have defect
detection margins greater than or equal to (th
1-th
0) are shown on
map display area
1410. The values of defect count
1446 and defect
density
1448 are also updated accordingly. In an alternative embodiment,
the th
1 threshold is applied to the difference of the clipped inspection
and corresponding clipped reference images stored in defect image memory
550
for each defect candidate image. The plurality of processing elements in Image
Analyzer
560 allow many of these calculations to occur in parallel. On the
map display area
1410, the defect candidate marks relating to the th
1
threshold are shown, and the values of defect count
1446 and defect density
1448 are also updated accordingly. In another embodiment, the defect distribution
is shown for a range of margins; for example, thL<defect detection margin<thH,
where thL, thH are low and high thresholds, respectively.
When the Inform button
1450 is chosen, a graph
1452 showing the
relation between the threshold (e.g., th
0 and th
1) and the defect
density is displayed and this graph
1452 provides information that can be
used for judging whether the new threshold of th
1 is proper.
If the Manual select button
1434 is chosen, the threshold th
1 may
be changed by sliding the Display TH bar
1442. When the Execute button
1444
is pressed after selecting another threshold, the defect candidate marks and the
values displayed in the area
1410 and the values of defect count
1446
and defect density
1448 are updated to those in accordance with the result
of inspection to which the threshold set by the slide position
1442 is applied.
Buttons
1454 and
1456 allows the choice of two-value
1454
or multilevel (grayscale)
1456 for the defect candidate marks on the map
display area
1410. If multilevel
1456, is chosen, a gray scale display
in which the greater the defect detection margin the darker the defect candidate
mark, is presented. The multilevel display is used for reference, when the th
1
threshold is manually set and shows how dark defect candidates and light defect
candidates are distributed on the wafer. In another embodiment a color code, mark
size, mark shape code may be used instead of the grayscale. In yet another embodiment
the greater the difference above the threshold th
0 (i.e., the greater the
defect detection margin) the lighter the defect candidate mark. For example, if
the difference represented electrical resistance then the lighter the defect candidate
mark, the lower the resistance. A very light mark may indicate a short circuit,
while a very dark mark an open circuit.
Create Recipe button
1470 allows the use of a recipe or program script
that sets the inspection mode in either die to die or array for various sections
of the wafer
100. The Inspect button
1472 allows use of this GUI
in actual inspection. And the button Check Defect allows use of this GUI in after
inspection analysis.
According to this example, the user can easily view trial inspection results
after threshold setting change without conducting the trial inspection again as
in the conventional system, and therefore can greatly save time as compared with
conducting the inspection again. In addition, this threshold setting process may
be used during actual inspection to make adjustments. Thus this method is more flexible.
As the clipped images are stored in storage medium
570, it is also possible
to do after inspection analysis of the defects. Thus the defect inspection process
can be examined for improvements. Data is also available to assist in determining
future initial threshold values. Thus efficiency may be improved.
FIG. 16 shows a GUI of another embodiment of the present invention. In this
embodiment the user can select which sections of the defect distribution screen
