U.S. patent application number 11/168318 was filed with the patent office on 2006-01-05 for pattern recognition with the use of multiple images.
Invention is credited to Douglas D. Do.
Application Number | 20060002606 11/168318 |
Document ID | / |
Family ID | 34885887 |
Filed Date | 2006-01-05 |
United States Patent
Application |
20060002606 |
Kind Code |
A1 |
Do; Douglas D. |
January 5, 2006 |
Pattern recognition with the use of multiple images
Abstract
A pattern inspection apparatus and method that uses multiple
images in a pattern recognition process used to detect defects in
an object being inspected is disclosed. A user is provided with
multiple image selection windows allowing the user to select
multiple desired images from the object to form a pattern to be
recognized within the object. The multiple desired images will be
substantially free from undesired features of the object. Once the
multiple desired images are selected, the spatial relationship
between them is determined and used to learn the pattern to be
recognized. The spatial relationship between the desired images
further filters out undesired features. The pattern to be
recognized is used in a subsequent pattern recognition analysis.
Since the pattern to be recognized includes only desired images and
their relationship, undesired features that could corrupt the
pattern recognition analysis are not present during the
analysis.
Inventors: |
Do; Douglas D.; (Boise,
ID) |
Correspondence
Address: |
DICKSTEIN SHAPIRO MORIN & OSHINSKY LLP
2101 L Street, NW
Washington
DC
20037
US
|
Family ID: |
34885887 |
Appl. No.: |
11/168318 |
Filed: |
June 29, 2005 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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09493663 |
Jan 28, 2000 |
6941007 |
|
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11168318 |
Jun 29, 2005 |
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Current U.S.
Class: |
382/145 |
Current CPC
Class: |
G06T 7/001 20130101;
G06K 9/6253 20130101; G06K 9/6255 20130101 |
Class at
Publication: |
382/145 |
International
Class: |
G06K 9/00 20060101
G06K009/00 |
Claims
1-39. (canceled)
40. An inspection for inspecting a manufacturing device used in a
manufacturing process, said apparatus comprising: means for
scanning the device; means for displaying a plurality of images
corresponding to respective scanned areas of the device; means for
inputting at least two desired scanned images to be inspected,
wherein the desired scanned images are selected from the images
corresponding to the scanned areas of the device; means for
deriving a spatial relationship between the input desired scanned
images; and means for forming a pattern to be recognized on the
device from the input desired scanned images and the derived
spatial relationship.
41. The apparatus of claim 40 further comprising: means for storing
information associated with the input desired scanned images and
the derived spatial relationship on a computer readable medium.
42. The apparatus of claim 41 further comprising: means for using
said stored input desired scanned images and the stored derived
spatial relationship in a pattern recognition analysis to detect
defects in the device.
43. The apparatus of claim 41 further comprising: means for using
said stored input desired scanned images and the stored derived
spatial relationship in a pattern recognition analysis to detect
desired patterns in the device.
44. The apparatus of claim 40, wherein said inputting means
comprises: means for displaying at least two image selection
windows with the displayed images; and means for inputting data
corresponding to the input desired scanned images in response to a
user placing the at least two image selection windows over
respective displayed images.
45. The apparatus of claim 44, wherein said inputting means
comprises: means for displaying an image selection window with the
displayed images; means for inputting data corresponding to one of
the input desired scanned images in response to a user placing the
image selection window over a displayed image; and means for
determining if another image selection window is required.
46. The apparatus of claim 40, wherein the device is a
semiconductor wafer.
47. The apparatus of claim 46, wherein the areas are contacts
formed in the wafer.
48. The apparatus of claim 47, wherein the contact areas have
desired and undesired features and said inputting means comprises:
means for displaying at least two image selection windows with the
displayed images; and means for inputting data corresponding to the
input desired scanned images in response to a user placing the at
least two image selection windows over images associated with the
desired features of the contact areas.
49. The apparatus of claim 47, wherein the contact areas have
desired and undesired features and said inputting means comprises:
means for displaying at least two image selection windows with the
displayed images; and means for inputting data corresponding to the
input desired scanned images in response to a user placing the at
least two image windows over images associated with the undesired
features of the contact areas.
50. The apparatus of claim 47, wherein the contact areas have
desired and undesired features and said inputting means comprises:
means for displaying an image selection window with the displayed
images; means for inputting data corresponding to one of the input
desired scanned images in response to a user placing the image
selection window over an image associated with the desired feature
of a contact area; and means for determining if another image
selection window is required.
51. The apparatus of claim 40, wherein the derived spatial
relationship comprises respective spatial relationships between
pairs of the input desired scanned images.
52. An inspection apparatus for use in inspecting a manufacturing
device used in a manufacturing process, said apparatus comprising:
a scanning device configured to obtain images of the manufacturing
device; a display; an input device configured to select desired
features of an image to be inspected; and a processor coupled to
said scanning device, said display and said input device, said
processor being configured to control said scanning device to scan
the manufacturing device, display on said display a plurality of
images corresponding to respective scanned areas of the
manufacturing device, input at least two user selected desired
scanned images from the input device, derive a spatial relationship
between the user selected images and to form a pattern to be
recognized on the manufacturing device from the user selected
images and the derived spatial relationship, wherein said user
selected images corresponding to scanned images displayed on the
display.
53. The apparatus of claim 52 further comprising: a computer
readable storage medium coupled to said processor, wherein said
processor is configured to store information associated with the
user selected images and the derived spatial relationship on said
computer readable medium.
54. The apparatus of claim 53, wherein said stored selected images
and the stored derived spatial relationship are used by the
processor in a pattern recognition analysis to detect defects in
the manufacturing device.
55. The apparatus of claim 53, wherein said stored selected images
and the stored derived spatial relationship are used by the
processor in a pattern recognition analysis to detect desired
patterns in the manufacturing device.
56. The apparatus of claim 52, wherein said processor is configured
to input the user selected images being configured to display on
said display at least two image selection windows with the
displayed images and being configured to input data corresponding
to images selected by the user by placement of the selection
windows via said input device.
57. The apparatus of claim 56, wherein the user of the apparatus is
prompted for a number of images windows to be displayed.
58. The apparatus of claim 52, wherein the derived spatial
relationship comprises respective spatial relationships between
pairs of the selected images.
59. An inspection apparatus for use in inspecting a semiconductor
wafer, said apparatus comprising: a scanning device for obtaining
images of a wafer; a display; an input device for use by a user of
the apparatus for selecting desired features of an image to be
inspected; and a processor coupled to said scanning device, said
display and said input device, said processor for controlling said
scanning device to scan a wafer, for displaying on said display a
plurality of images corresponding to areas of the scanned wafer,
for inputting at least two user selected images from the input
device, for deriving a relationship between the user selected
images and forming a pattern to be recognized on the scanned wafer
from the user selected images and the derived relationship, wherein
the derived relationship is determined by forming vectors in at
least two dimensions between the user selected images.
60. The apparatus of claim 59 further comprising: a computer
readable storage medium coupled to said processor, wherein said
processor is configured to store information associated with the
selected images and the derived relationship on said computer
readable medium.
61. The apparatus of claim 60, wherein said stored selected images
and the stored derived relationship are used by the processor in a
pattern recognition analysis to detect defects in the scanned
wafer.
62. The apparatus of claim 60, wherein said stored selected images
and the stored derived relationship are used by the processor in a
pattern recognition analysis to detect desired patterns in the
scanned wafer.
Description
BACKGROUND OF THE INVENTION
[0001] 1. Field of the Invention
[0002] The present invention relates to the field of semiconductor
fabrication and, more particularly to a method and apparatus that
uses multiple images in a pattern recognition process used to
detect defects in the manufacture of a semiconductor device.
[0003] 2. Description of the Related Art
[0004] In the semiconductor industry, there is a continuing
movement towards higher integration, density and production yield,
all without sacrificing throughput or processing speed. The making
of today's integrated circuits (ICs) requires a complex series of
fabrication, inspection and testing steps interweaved throughout
the entire process to ensure the proper balance between throughput,
processing speed and yield. The inspections and tests are designed
to detect unwanted variations in the wafers produced, as well as in
the equipment and masks used in the fabrication processes. One
small defect in either the devices produced or the process itself
can render a finished device inoperable.
[0005] Many of the inspection steps once done manually by skilled
operators have been automated. Automated systems increase the
process efficiency and reliability as the machines performing the
inspection are more consistent than human operators who vary in
ability and experience and are subject to fatigue when performing
repetitive tasks. The automated systems also provide greater
amounts of data regarding the production and equipment, which
enables process engineers to both better analyze and control the
process.
[0006] One such automated inspection step is known as pattern
recognition or pattern inspection. Many different "patterns" appear
on both the wafer and the masks used to produce the ICs. Typical
pattern inspection systems are image based, as described, for
example, in U.S. Pat. Nos. 4,794,646; 5,057,689; 5,641,960; and
5,659,172. In U.S. Pat. No. 4,794,646, for example, the wafer, or
part thereof,.is scanned and a highly resolved picture or image of
the pertinent "pattern" is obtained. This pattern image is compared
to other pattern images retrieved from the same or other wafers, or
is compared to an ideal image stored in the inspection system
database. Differences highlighted in this comparison identify
possible defects in the IC or wafer.
[0007] FIG. 1 illustrates the conventional pattern recognition
method 10 currently performed by today's pattern recognition or
pattern inspection tools. The method 10 begins when a user places a
wafer or other object to be inspected into the inspection apparatus
(step 12). After scanning the wafer, the apparatus displays a
"field of view" containing images from a portion of the scanned
wafer (step 14). Theses images are to be inspected by the apparatus
and thus, are referred to herein as the "inspected images." The
apparatus then displays a single pattern box within the field of
view (step 16). This pattern box will be used by an operator of the
apparatus to select a desired image from the inspected image. The
selection is made by placing the pattern box over an image
currently displayed in the field of view (step 18). The apparatus
"learns" the pattern of the selected image and subsequently uses
the learned pattern in a pattern recognition analysis to determine
if the wafer has any defects (step 20). The use of "learns" or
"learned" herein refers to the process of obtaining pattern
information for the selected image and storing the information for
subsequent use in a pattern recognition or critical dimension (CD)
analysis. The process of learning a pattern and performing a
pattern recognition analysis is well known and can be carried out
in any known manner.
[0008] The method 10, however, is not without its shortcomings.
Referring to FIG. 2, exemplary inspected images 50 are illustrated.
In this example, the images 50 are contacts formed within a wafer
being inspected. Each image 50 contains a top surface 52 of the
contact and a bottom surface 54 of the contact. The bottom surfaces
52 are the desired features, which must be inspected for defects.
Typical defects include under etching and over etching of the
contacts and what is sometimes referred to as "closed contacts,"
which are partially etched contacts.
[0009] As can be seen from FIG. 2, the top surfaces 52 are larger
and much more prominent than the bottom surfaces 54. Often times,
the inspection apparatus receives such strong signals from the
contact top surfaces 52 that it is difficult to detect and properly
inspect the contact bottom surfaces 54, which, as described above,
are the desired features. By way of example, it is presumed that
during the method 10 (FIG. 1) the operator placed a pattern box 70
over an image comprised of contact images 80. The selected image
and its pattern found within box 70 will be hereinafter referred to
as the "pattern to recognize." Like the images 50 to be inspected,
the images 80 of the pattern to be recognized contain top surfaces
82 and bottom surfaces 84. In this example, the user selected
pattern to be recognized contains three contact bottom surfaces 84,
each with their own expected or desired shape. The user selected
pattern to be recognized also contains portions of three top
surfaces 82 that are much larger than the bottom surfaces 84. The
inspection apparatus learns the pattern to be recognized, which
includes large signals associated with the top surfaces 82, and
subsequently uses the learned pattern for comparison with the
inspected images, the apparatus detects three matches 60, 62,
64.
[0010] As can be seen from FIG. 2, the three declared matches 60,
62, 64 do not contain bottom surfaces 54 that match the bottom
surfaces 84 of the pattern to be recognized. Moreover, some of the
matches 60, 62, 64 contain defects, e.g., under etched bottom
surfaces 54a, 54b, 54c. Thus, the apparatus has incorrectly
detected three matches 60, 62, 64, when there should have been zero
matches and more importantly, the apparatus failed to detect three
defects 54a, 54b, 54c. This anomaly occurs since the apparatus
receives such strong signals from the much larger and much more
prominent top surfaces 52, 82, which substantially match each
other. By contrast, the apparatus receives weaker signals from the
much smaller and less prominent bottom surfaces 54, 84, which do
not match each other and also contain defects. Since there is much
more information associated with the top surfaces 52, 82 than the
bottom surfaces 54, 84, the apparatus detects the matches 60, 62,
64 based on the top surfaces 52, 82, which results in improper
pattern recognition results.
[0011] Typically, the inspection apparatus will allow a user to set
pattern recognition thresholds. These thresholds are designed to
reduce or increase the matching percentage required between the
pattern to be recognized and the inspected images. Thus, a user may
set a matching threshold to 100%, in which case, the apparatus will
only declare matches when the inspected images contain patterns
that exactly match the pattern to be recognized. This would ensure
that defective images 54a, 54b, 54c are not matched to desired and
non-defective images. However, due to variations in the
manufacturing process, a matching threshold of 100% would most
likely lead to no desired matches or too few desired matches than
are actually present. The apparatus would not detect all of the
proper matches, if it detects any at all (i.e., it is under
inclusive). On the other hand, if the matching threshold is set too
low, e.g., 50%, then too many matches will occur. These matches
will include defective images 54a, 54b, 54c whose patterns are
within the matching percentage (i.e., it is over inclusive).
Typically, the matching threshold is set to approximately 65% to
balance between the over inclusive and under inclusive matching
thresholds.
[0012] Even with a threshold setting of 65%, the conventional
pattern recognition process is still unreliable. FIG. 3 illustrates
another set of exemplary inspected images 50. Four sample images
90, 92, 94, 96 are also illustrated. The sample images 90, 92, 94,
96 each contain three contact images 50. The first three images 90,
92, 94 contain top surfaces 52 and bottom surfaces 54, while the
fourth image 96 only contains bottom surfaces 54. The second and
third images 92,94 also contain defective bottom surfaces 92a, 94a,
respectively. Using the current pattern recognition process, the
first three images 90, 92, 94 would most likely match each other if
one of the images 90, 92, 94 were used as a pattern to be
recognized. This would happen even though the bottom surfaces 54 of
the images 90, 92, 94 do not match at all and some of the surfaces
92a, 94a are defective.
[0013] It would be desirable to use the fourth sample image 96 as
the pattern to be detected. As noted above, the fourth image 96
does not contain any top surfaces 52. However, the fourth sample
image 96, which has bottom surfaces 54 substantially matching the
bottom surfaces 54 of the first sample image 90, would not match
any of the other images 90, 92, 94 because the other images contain
both top and bottom surfaces 52, 54. Thus, even if it were possible
to select the fourth sample image 96 as a pattern to be recognized,
the pattern recognition analysis would be corrupted by the top
surfaces 52, of the inspected images (e.g., images 90, 92, 94)
which are not part of the desired features.
[0014] Accordingly, there is a desire and need for a pattern
recognition process that filters out undesirable features from the
object being inspected prior to performing a pattern recognition
analysis on the object. There is also a desire and need for a
pattern recognition process that allows a user to select multiple
desired images of the object being inspected to be used as a
pattern to be recognized during a pattern recognition analysis on
the object.
SUMMARY OF THE INVENTION
[0015] The present invention provides a pattern recognition
technique that substantially filters out undesirable features of
the object being inspected prior to performing a pattern
recognition analysis on the object.
[0016] The present invention also provides a pattern recognition
technique that allows multiple desired images of the object being
inspected to be used as a pattern to be recognized during a pattern
recognition analysis on the object.
[0017] The above and other features and advantages of the invention
are achieved by a pattern inspection apparatus and method that uses
multiple images in a pattern recognition process used to detect
defects in an object being inspected. A user is provided with
multiple image selection windows allowing the user to select
multiple desired images from the object to form a pattern to be
recognized within the object. The multiple desired images will be
substantially free from undesired features of the object. Once the
multiple desired images are selected, the relationship between them
is determined and used to learn the pattern to be recognized. The
relationships between the desired images further filters out
undesired features. The pattern to be recognized is used in a
subsequent pattern recognition analysis. Since the pattern to be
recognized includes only desired images and their relationship,
undesired features that could corrupt the pattern recognition
analysis are not present during the analysis. Thus, the apparatus
and method are more accurate than prior inspection tools.
BRIEF DESCRIPTION OF THE DRAWINGS
[0018] The foregoing and other advantages and features of the
invention will become more apparent from the detailed description
of the preferred embodiments of the invention given below with
reference to the accompanying drawings in which:
[0019] FIG. 1 illustrates in flowchart form a conventional pattern
recognition process;
[0020] FIG. 2 illustrates exemplary inspected images and a pattern
to be recognized used in the process illustrated in FIG. 1;
[0021] FIG. 3 illustrates exemplary inspected images used in the
process illustrated in FIG. 1;
[0022] FIG. 4 illustrates a pattern inspection apparatus
constructed in accordance with the present invention;
[0023] FIG. 5 illustrates in flowchart form an exemplary pattern
recognition method using multiple images in accordance with a first
embodiment of the present invention;
[0024] FIGS. 6a and 6b illustrate exemplary images to be detected
and their relationships;
[0025] FIG. 7 illustrates exemplary inspected images and a pattern
to be recognized used in the processes illustrated in FIGS. 5 and
8;
[0026] FIG. 8 illustrates in flowchart form another exemplary
pattern recognition method using multiple images in accordance with
a second embodiment of the present invention; and
[0027] FIGS. 9a-9e illustrate sample field of view images and a
resultant pattern to be recognized in accordance with any of the
embodiments of the present invention.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0028] In an exemplary implementation, a pattern inspection or
defect detection apparatus 100, as shown in FIG. 4, can be
constructed having a scanning electron-beam microscope (SEM) 102
used for viewing purposes, as is well known in the art. Although
the invention is described herein as used during final wafer
inspection of the IC dies, it can readily be appreciated that the
invention has application to any other stage of manufacturing,
e.g., inspection after the initial photomasking and baking of a
wafer known as "development inspection," where critical dimension
measurements or pattern inspections are contemplated.
[0029] The SEM 102 is provided with an object support structure 108
in the form of a stage or chuck, which may be moveable or
stationary. An object 120 under evaluation such as an IC sample die
or a wafer containing many IC dies rests on support structure 108.
Under control of computer 110, the structure 108 may be moveable in
two (X-Y) or three (X-Y-Z) dimensions to facilitate the proper
viewing of the object 120 (or parts thereof. A deflector 104 and
detector 106, whose operations will be described in detail below,
are also provided within the SEM 102 to assist in the viewing of
the object 120. An image processor 112, together with its
accompanying image memory 114 are provided to process the image
signals output by the SEM 102 and transform the signals into visual
representations or data which can be viewed on a display monitor
116 (e.g., cathode ray tube (CRT)) or used for processing in
computer 110.
[0030] In operation, the SEM 102 uses a finely focused electron
beam directed by deflector 104, preferably under the control of the
computer 110, to scan the surface of the object 120 resting on the
support 108, typically in two dimensions (X-Y). For the purposes of
discussion only, it will be assumed herein that the object 120
under evaluation is a silicon wafer having a plurality of contacts
formed therein, such as contacts 50 illustrated in FIGS. 2, 3 and
8. The electrons striking the semiconductor surface of the object
120 collide with inner shell electrons of the object atoms causing
inelastic collisions of low energy emitting so-called "secondary
electrons" which are serially detected by the detector 106.
[0031] The detected electron current is output as an image signal
to the computer 110 and image processor 112 where an image
representative of the surface of the object 120 can be formed based
on the image signal. This image may be stored in the image memory
114 and can be viewed on the monitor 116 or otherwise processed by
the computer 110. The high resolution of the image is attributed to
the small diameter (e.g., several nanometers) of the electron beam
illuminator. The visual contrast achieved in the image originates
mostly from variations in the extent of the secondary electron
emissions from the topographic features of the surface of the
object 120.
[0032] FIG. 5 illustrates an exemplary method 200 of using multiple
images in a pattern recognition process constructed in accordance
with an embodiment of the present invention. The method 200 (with
the exception of step 202, described below) is preferably
implemented in software and executed in the computer 110 of the
pattern inspection apparatus 100 illustrated in FIG. 4. It should
be noted that the method 200 of the present invention may also be
implemented in a conventional CD-SEM apparatus such as the
"IVS-200" made by IVS, Inc., the "Opal 7830si" made by Applied
Materials, or the "S-8820/8620" made by Hitachi, by modifying the
computer program used by the control computer within the CD-SEM
apparatus such that the apparatus implements the operations of the
method 200 (described below).
[0033] Referring to FIGS. 4 and 5, the method 200 begins when a
user places a wafer or other object 120 to be inspected into the
inspection apparatus 100 (step 202). It should be appreciated that
the object 120 can be a semiconductor wafer at any stage of the
manufacturing process or it can be a reticle used to create a mask,
such as a phase shifting mask and that the invention should not be
limited solely to wafers. However, to remain consistent with the
preceding discussion, the object 120 will be a wafer having
contacts formed therein. After scanning the wafer, the method 200
displays on the display 116 a field of view containing images from
a portion of the scanned wafer (step 204). As noted above with
reference to FIG. 1, theses images are to be inspected by the
apparatus 100 and thus, are referred to herein as the "inspected
images."
[0034] The apparatus 100 then prompts the user to input the number
of image selection windows required (step 206). Each image
selection window is used by the method 200 to select an image,
within the inspected images, to be used as part of the pattern to
be detected. In the prior art method one large box is used to
obtain an image having both desired (e.g., bottom surfaces of
contacts) and undesired features (e.g., top surfaces of contacts).
The present method 200 uses multiple sizable image selection
windows. This way, instead of placing one large box over an image
containing both desired and undesired features, the method 200 uses
several multiple sizable image selection windows to select images
with only desired features. As will become apparent below, this
allows the method 200 to substantially filter out undesired
features from the pattern to be recognized and thus, the pattern
recognition analysis.
[0035] At step 208, the user inputs the number of desired windows.
By allowing the user to select more than one image selection
window, the method 200 of the present invention allows multiple
different images to be used in the pattern recognition analysis. It
should be noted that these steps differ from the prior art, which
only provides the user with one box, and thus, does not allow the
user to select multiple images (see step 16 of FIG. 1). Moreover,
the prior art does not filter out undesired features.
[0036] Referring also to FIGS. 9a-9e, the apparatus 100 displays
the appropriate number of image selection windows 300, 302, 304
within the field of view (step 210). FIGS. 9a-9e illustrate an
example where three image selection windows 300, 302, 304 have been
chosen by a user at step 208. These windows 300, 302, 304 are used
to select desired images 310a, 310b, 310c from the inspected
images. The selection is made by placing each image selection
window 300, 302, 304 over a separate desired image 310a, 310b, 310c
that is currently displayed in the field of view (step 212). It
should be understood that depending upon how the method 200 is
implemented, the user may be required to hit a "select" or enter
"button" after positioning and sizing the image selection windows
300. 302, 304 to select the images 310a, 310b, 310c. By using
multiple image selection windows 300, 302, 304 , the user can size
them so that they only select desired features (e.g., bottom
surfaces 54). That is, it is possible for the user to substantially
filter out unwanted features, such as the top surfaces 52 of the
contacts, from the pattern to be recognized at this step.
[0037] At step 214, the apparatus 100 learns the selected images
and the relationship between them. That is, the apparatus 100
obtains and stores information for the selected images. The
information can be stored in the memory 114 or other computer
readable medium connected to or contained within the apparatus 100.
The spatial relationship between the selected images is also
obtained and stored. Learning the spatial relationship between the
images will further filter out undesired features. This can best be
illustrated by the following example. Referring to FIG. 6a, five
exemplary images 250, 252, 254, 256, 258 are illustrated. These
images 250, 252, 254, 256, 258 correspond to potential desired
images within the inspected images. That is, these images 250, 252,
254, 256, 258 could represent the bottom surfaces of various
contacts found on the object 120. Referring to FIG. 6b, it can be
seen that a pattern 260 to be detected has been selected by a user.
The pattern 260 consists of three selected images 250, 254, 256,
the spatial relationship 266, 268 between image 250 and image 254
and the spatial relationship 262, 264 between image 250 and image
256. It is desired that the relationships 262, 264, 266, 268 be two
or three dimensional vectors. It should be appreciated, however,
that any means for indicating the relationship between the selected
images can be used. Another indicia of the relationship between the
images could include the distance from an origin or test point on
the object 120 (not shown). FIG. 9e illustrates the pattern 310 to
be recognized, the spatial relationships 312, 314 between images
310a and 310c and the spatial relationship between image 310a and
310b.
[0038] Referring again to FIGS. 4 and 5, once the images and their
relationship are learned, the method 200 continues at step 216,
where the pattern to be recognized is learned and subsequently used
a pattern recognition analysis to determine if the object 120 has
any defects. Here, unlike the prior art method 10 (FIG. 1), the
learned pattern includes the learned information of the multiple
images, the relationship between the images and any other
information required by the apparatus 100 to perform the pattern
recognition analysis. Any required pattern information can be
stored in the memory 114 or other computer readable medium
connected to or contained within the apparatus 100. At this point,
the process of learning the pattern and performing the pattern
recognition analysis can be carried out in any known manner.
[0039] FIG. 7 illustrates the exemplary inspected images 50
described above with reference to FIG. 2. However, a new pattern
310 to be recognized is illustrated. As illustrated in FIGS. 9a-9e,
the pattern 310 was created using the method 200 (FIG. 6) of the
present invention. Unlike the pattern contained within box 70 of
FIG. 2, the pattern 310 created in accordance with the present
invention consists solely of images of the bottom surface 384 of
the contacts. Each bottom surface 384 was individually selected by
respective pattern windows 300, 302, 304. As described above, the
present invention learns the individual images within these windows
300, 302, 304 and their relationships (FIG. 9e). By learning the
images 384 and their relationship, the learned pattern 310 to be
recognized is devoid of any undesired features, particularly the
top surfaces 52, 82 (illustrated in FIG. 2). As such, the present
invention using pattern 310 detects zero matches from the inspected
images 50. This is the correct result, since none of the bottom
surfaces 54 within the inspected images match the bottom surfaces
384 of the pattern 310 to be recognized.
[0040] FIG. 8 illustrates another exemplary method 400 of using
multiple images in a pattern recognition process constructed in
accordance with another embodiment of the present invention. The
method 400 (with the exception of step 402, described below) is
preferably implemented in software and executed in the computer 110
of the pattern inspection apparatus 100 illustrated in FIG. 4. As
with the method 200 (FIG. 5), the method 400 may also be
implemented in a conventional CD-SEM apparatus such as the
"IVS-200" made by IVS, Inc., the "Opal 7830si" made by Applied
Materials, or the "S-8820/8620" made by Hitachi, by modifying the
computer program used by the control computer within the CD-SEM
apparatus such that the apparatus implements the operations of the
method 400 (described below).
[0041] Referring to FIGS. 4 and 8, the method 400 begins when a
user places a wafer or other object 120 to be inspected into the
inspection apparatus 100 (step 402). After scanning the wafer, the
method 400 displays on the display 116 a field of view containing
images from a portion of the scanned wafer (step 404). As noted
above, these images are referred to as the inspected images. The
apparatus 100 then displays an image selection window within the
field of view (step 406). At step 408 the user uses the window to
select a desired image from the inspected images. The selection is
made by placing the image selection window over an image that is
currently displayed in the field of view. It should be understood
that depending upon how the method 400 is implemented, the user may
be required to hit a "select" or enter "button" after positioning
and sizing the window to select the image. At step 410, the user is
prompted to determine if more image selection windows are
required.
[0042] If at step 412, it is determined that more image selection
windows are required, the method 400 continues at step 406 where a
new image selection window is displayed on the field of view. This
way, the user may select multiple images using multiple image
selection windows. By using multiple image selection windows, the
user can size these windows so that they only select desired
features. That is, it is possible for the user to substantially
filter out unwanted features, such as the top surfaces of the
contacts, from the pattern to be recognized.
[0043] If at step 412 it is determined that no more image selection
windows are required, the method continues at step 414. At step
414, the apparatus 100 learns the selected images and the
relationship between them. That is, the apparatus 100 obtains and
stores information for the selected images. The information can be
stored in the memory 114 or other computer readable medium
connected to or contained within the apparatus 100. The spatial
relationship between the selected images is also obtained and
stored (as described above with reference to FIG. 6). Once the
images and their relationship are learned, the method 400 continues
at step 416, where the pattern to be recognized is learned and
subsequently used a pattern recognition analysis to determine if
the object 120 has any defects. Here, unlike the prior art method
10 (FIG. 1), the learned pattern includes the learned information
of the multiple images, the relationship between the images and any
other information required by the apparatus 100 to perform the
pattern recognition analysis. Any required pattern information can
be stored in the memory 114 or other computer readable medium
connected to or contained within the apparatus 100. At this point,
the process of learning the pattern and performing the pattern
recognition analysis can be carried out in any known manner.
[0044] The present invention improves the pattern recognition
process by allowing the user to create a pattern to be recognized
using multiple images. By select multiple images, the user can
substantially filter out unwanted features, such as the top
surfaces of contacts formed in a wafer. More importantly, the
present invention uses relationships between the selected images to
hone in on the exact pattern to be recognized. Thus, the present
invention can perform pattern recognition using the typical
thresholds currently used in today's inspection systems, but with
substantially better results.
[0045] It should be appreciated that the learned pattern created by
the methods of the present invention can be used in the pattern
recognition analysis to detect defects in the object as well as to
detect desired (i.e., properly etched) patterns in the object. It
should also be appreciated that the learned pattern created by the
methods of the present invention can be used in both pattern
recognition and critical dimension (CD) analysis processes.
Moreover, the methods of the present invention can be used to
inspect wafers, reticles or other semiconductor devices requiring
pattern inspection/recognition.
[0046] While the invention has been described in detail in
connection with the preferred embodiments known at the time, it
should be readily understood that the invention is not limited to
such disclosed embodiments. Rather, the invention can be modified
to incorporate any number of variations, alterations, substitutions
or equivalent arrangements not heretofore described, but which are
commensurate with the spirit and scope of the invention.
Accordingly, the invention is not to be seen as limited by the
foregoing description, but is only limited by the scope of the
appended claims.
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