U.S. patent application number 10/954968 was filed with the patent office on 2006-11-23 for flexible hybrid defect classification for semiconductor manufacturing.
Invention is credited to Sandeep Bhagwat, Cecelia Anne Campochiaro, Lisheng Gao, Tong Huang, Patrick Huet, Vivekanand Kini, Michal Kowalski, Ashok Kulkarni, Sharon McCauley, David Randall, Maruti Shanbhag, Ariel Tribble, Kenong Wu, Jianxin Zhang.
Application Number | 20060265145 10/954968 |
Document ID | / |
Family ID | 36142891 |
Filed Date | 2006-11-23 |
United States Patent
Application |
20060265145 |
Kind Code |
A1 |
Huet; Patrick ; et
al. |
November 23, 2006 |
FLEXIBLE HYBRID DEFECT CLASSIFICATION FOR SEMICONDUCTOR
MANUFACTURING
Abstract
Hybrid methods for classifying defects in semiconductor
manufacturing are provided. The methods include applying a flexible
sequence of rules for defects to inspection data. The sequence of
rules includes deterministic rules, statistical rules, hybrid
rules, or some combination thereof. The rules included in the
sequence may be selected by a user using a graphical interface The
method also includes classifying the defects based on results of
applying the sequence of rules to the inspection data.
Inventors: |
Huet; Patrick; (San Jose,
CA) ; Shanbhag; Maruti; (Bangalore, IN) ;
Bhagwat; Sandeep; (Milpitas, CA) ; Kowalski;
Michal; (Santa Cruz, CA) ; Kini; Vivekanand;
(Sunnyvale, CA) ; Randall; David; (Sunnyvale,
CA) ; McCauley; Sharon; (San Jose, CA) ;
Huang; Tong; (Sunnyvale, CA) ; Zhang; Jianxin;
(Santa Clara, CA) ; Wu; Kenong; (Davis, CA)
; Gao; Lisheng; (Morgan Hill, CA) ; Tribble;
Ariel; (Fremont, CA) ; Kulkarni; Ashok; (San
Jose, CA) ; Campochiaro; Cecelia Anne; (Sunnyvale,
CA) |
Correspondence
Address: |
DAFFER MCDANIEL, LLP
P.O. BOX 684908
AUSTIN
TX
78768
US
|
Family ID: |
36142891 |
Appl. No.: |
10/954968 |
Filed: |
September 30, 2004 |
Current U.S.
Class: |
702/35 |
Current CPC
Class: |
G06T 2200/24 20130101;
G06T 2207/30148 20130101; G06T 7/0004 20130101; G01R 31/2846
20130101 |
Class at
Publication: |
702/035 |
International
Class: |
G01B 5/28 20060101
G01B005/28 |
Claims
1. A flexible computer-implemented method for classifying defects,
comprising: applying a sequence of rules for defects to inspection
data generated by inspection of a semiconductor specimen, wherein
the sequence of rules comprises statistical rules, deterministic
rules, hybrid statistical and deterministic rules, or some
combination thereof, wherein a portion of the sequence of rules is
applied as the defects are found during the inspection, and wherein
another portion of the sequence of rules is applied at the end of
the inspection; classifying the defects based on results of said
applying, wherein results of said classifying comprise multiple
output classifications for the defects; and illustrating the
results of said classifying in an interactive user interface.
2. The method of claim 1, wherein the deterministic rules apply one
or more tests to characteristics of the defects, and wherein the
characteristics comprise whether the defects are bright or dark,
contrast of the defects with respect to background, measured size,
detection method, information about defects on other levels of the
specimen, location of the defects on the specimen, proximity to
other events, or some combination of attributes that can be used
deterministically to classify defects.
3. The method of claim 1, wherein the statistical rules are based
on characteristics of the defects comprising color, size, edge
sharpness, eccentricity, roundness, transparency, texture, context,
or some combination thereof, and wherein the statistical rules
apply the characteristics statistically to bin defects.
4. The method of claim 1, wherein characteristics of the defects
input to the statistical rules and the deterministic rules used for
said applying are selected by a user.
5. The method in claim 1, wherein the another portion of the
sequence of rules comprises rules based on proximity of the defects
to other defects on the specimen or previous specimen history, and
wherein dependent rules are applied after the another portion is
applied.
6. The method of claim 1, wherein said applying is performed while
a user is reviewing the defects.
7. The method of claim 1, further comprising tuning inspection
recipes based on the results of said classifying.
8. The method of claim 1, further comprising using the results of
said classifying in sampling defects for a subsequent activity.
9. The method of claim 1, further comprising performing engineering
analysis using the results of said classifying.
10. The method of claim 1, further comprising performing the
computer-implemented method on data generated by different
inspection or review tools having different hardware
configurations.
11. (canceled)
12. The method of claim 1, wherein said classifying comprises
determining if the defects are nuisance defects based on results of
said applying the deterministic rules or a combination of the
deterministic and statistical rules.
13. The method of claim 1, wherein the statistical rules and the
hybrid statistical and deterministic rules are organized into
groups for selection to aid in user understanding of these rules
and to provide classifications that reflect intent of a user.
14. The method of claim 1, wherein the statistical rules are
weighted separately.
15. The method of claim 1, wherein the sequence of rules is
organized by a user working with the interactive user interface,
and wherein the sequence of rules is represented in the interactive
user interface as a tree having different levels.
16. The method of claim 15, wherein the tree comprises nodes that
produce one or more branches, one or more terminating bins, or some
combination thereof.
17. The method of claim 15, wherein the tree comprises
deterministic nodes, statistical nodes, or hybrid deterministic and
statistical nodes.
18. The method of claim 15, wherein the tree comprises
deterministic nodes designated by characteristic name, statistical
nodes designated by name, and hybrid nodes designated by name.
19. The method of claim 15, wherein the interactive user interface
illustrates the results of said classifying graphically and with
sample images.
20. The method in claim 15, further comprising building one of the
deterministic rules using the interactive user interface through
applying unrestricted Boolean operators to defect characteristics.
Description
[0001] Examples of fully rule-based approaches include Run Time
Classification (RTC) provided on the AIT II, AIT III, and AIT XP
systems, which are commercially available from KLA-Tencor, San
Jose, Calif., the early release of on-the-fly (OTF) classification
methods on the Compass tools commercially available from Applied
Materials Inc., Santa Clara, Calif., and gray level binning for
voltage contrast defects, which is commercially available from
Hermes MicroVision, Milpitas, Calif. The setup of such a classifier
is relatively simple and easy for the user to understand. Many of
these approaches provide some user assistance by showing how the
defects have been separated through a variety of graphical means
and by showing examples of defects in each bin. Deterministic
rule-based classifiers generally have a high throughput.
[0002] Examples of statistical (trained) classification are the
current automatic defect classification (ADC) and inline ADC (iADC)
products on the 23xx, AIT, eSxx, and eV300 tools commercially
available from KLA-Tencor. These particular examples use a
statistical classification (e.g., nearest neighbor) approach to
separate defects. An additional example of a trained classifier is
the current release of OTF called "OTF Grouping" commercially
available from Applied Materials Inc., Santa Clara, Calif. These
classification algorithms use a mathematical representation of the
defects' appearance and context (sometimes called "defect
features") in a "black box" fashion, matching the defects to a
training set, although the user may have control of the importance
of low false positive or false negative assessments for each
bin.
[0003] One example of a hybrid approach is SEMVision ADC, which is
commercially available from Applied Materials Inc., Santa Clara,
Calif., and which has a fixed set of bins called core classes that
are based mainly on defect boundary analysis, segmentation of
background, and depth of defect through multi-perspective imaging.
While the tree structure, which defines the order and type of
decisions to be made, is fixed in this approach, the thresholding
for classifying can be set by the user.
[0004] Although the above-described methods are modestly successful
at defect classification, each of these methods can be improved.
For example, many deterministic methods do not include all of the
characteristics of the defects that are relevant to good
classification. In addition, fixed boundaries often do not work
well over time on different specimens. The deterministic rule based
methods are also generally inflexible in the usage of rules and
defect characteristics. In addition, these methods generally
include some restrictions on the number and kinds of
characteristics and how they are combined. Furthermore, these
methods generally have user interface deficiencies in being able to
create the classification recipe. For example, the user interface
can be complex to navigate, and the final results may not be
clear.
[0005] One disadvantage of the fully trained approaches is that
these methods generally rely on having a sufficient population of
the defects for each bin available for training. These methods also
need to be maintained and updated as defects that look different
are found or as processing conditions change. In addition, these
methods work in a way that may not reflect the intentions of the
user because these methods function as a black box (i.e., the user
is unable to select the characteristics or characteristic groups to
be used to do the classification). Furthermore, these methods often
neglect non-appearance characteristics that can be important in
separating defects for purposes of analysis. Lastly, fully trained
classifiers are generally slower to execute than deterministic
rules, particularly ones trained with a large number of
characteristics.
[0006] The inflexible, hybrid methods have disadvantages such as
that these methods often do not account for novel ways that the
user might want to separate defects for a particular image or
specimen. In addition, these methods rigidly restrict the paths
used to bin the defects.
[0007] Accordingly, it may be advantageous to develop
computer-implemented methods for classifying defects that eliminate
one or more of the disadvantages described above.
SUMMARY OF THE INVENTION
[0008] An embodiment of the invention relates to a flexible
computer-implemented method for classifying defects found in
semiconductor manufacturing. The term "flexible" as used herein can
be generally defined as user configurable or user defined. In other
words, a "flexible computer-implemented method" may be defined as a
computer-implemented method in which parameters may be defined
and/or configured by a user. The manner in which
computer-implemented methods described herein are flexible and the
benefits of this flexibility will be apparent upon further reading
of the description of the invention provided herein.
[0009] The method includes applying a sequence of rules for defects
to inspection data generated by inspection of a semiconductor
specimen. The sequence of rules include statistical rules,
deterministic rules, hybrid statistical and deterministic rules, or
some combination thereof.
[0010] The deterministic rules apply one or more tests to
characteristics of the defects, herein also called "attributes."
For example, the attributes or characteristics may include whether
the defects are dark or bright, contrast of the defects with
respect to background, measured size, detection method (e.g., how
the defect was detected), information about defects on other levels
of the specimen, location of the defects on the specimen, proximity
to other events, or some combination of attributes that are used
deterministically to bin defects.
[0011] In contrast, the statistical rules may be based on
characteristics of the defects including color, size, edge
sharpness, eccentricity, roundness, transparency, texture, or some
combination thereof. The statistical rules apply the
characteristics statistically to bin defects. The characteristics
of the defects input to the statistical rules and the deterministic
rules used for the application of the sequence of the rules may be
selected by a user. A hybrid rule may use both measurable
characteristic (e.g., measured size) and statistical characteristic
(e.g., statistical size) information together for
classification.
[0012] The statistical rules, the deterministic rules, and the
hybrid rules may be user defined. In one embodiment, the
statistical rules, the deterministic rules, and/or the hybrid rules
used for application to the inspection data are selected by a user
to create a sequence or "recipe" for performing classification. In
another embodiment, the deterministic, statistical, and/or hybrid
rules are applied during the inspection. In an additional
embodiment, applying the sequence of rules may be performed after a
testing operation, after complete wafer inspection, or after the
inspection of several wafers.
[0013] In another embodiment, a portion of the sequence of rules is
applied as the defects are found during inspection. Another portion
of the sequence of rules is applied at the end of the inspection.
This other portion of the sequence of rules may include rules based
on proximity of the defects to other defects on the specimen or
previous specimen history. Dependent rules may be applied after the
other portion is applied. For example, some of the deterministic
and statistical rules may be applied during the inspection, while
other deterministic rules, such as the proximity of other defects
(as in a scratch) or the fact that a defect type repeats across a
wafer, would be applied at the end of the inspection. In this
manner, rules that depend upon results or information that is not
available until the end of inspection will be executed at that
time. In an additional embodiment, applying the sequence of rules
to the inspection data may be performed while a user is reviewing
the defects (e.g., on the same tool or a different tool).
[0014] The method also includes classifying the defects based on
results of the application of the sequence of rules. In one
embodiment, the classification may result in defects being put in
the same bin through a variety of rules. In one embodiment, results
of classification may include multiple output classifications for
the defects. In addition, the method may include determining if the
defects are nuisance defects based on results of the application of
the deterministic rules or a combination of deterministic and
statistical rules. In another embodiment, classifying the defects
may include filtering the defects (e.g., removing data
representative of these defects) that are determined to be nuisance
defects based on the results of applying the statistical and/or
deterministic rules to the inspection data.
[0015] In one embodiment, the method may include tuning inspection
recipes based on results of the classification. In another
embodiment, the method may include performing engineering analysis
using results of the classification. In an additional embodiment,
the method may include using results of the classification in
sampling defects for a subsequent activity. For example, the method
may include using the bins as an input into other analytical
algorithms such as a sampling algorithm for manual or automatic
review. In another embodiment, the results of the classification
could be used to assist a user during manual classification of
defects based on data from the inspection itself or from a
different review tool, such as a review scanning electron
microscope (SEM). In another embodiment, the method may include
performing the computer-implemented method for inspection data
generated by different inspection tools having different hardware
configurations.
[0016] In one embodiment, the deterministic rules, the statistical
rules, and the hybrid rules used for applying the sequence of rules
to the inspection data are selected by a user. In another
embodiment, the sequence of rules may be organized by a user
working with an interactive user interface. In an additional
embodiment, the method may include building a deterministic rule
using the interactive user interface through applying unrestricted
Boolean operators to defect attributes.
[0017] The sequence of rules may be represented in the interactive
user interface as a tree having different levels. In one
embodiment, the tree may include nodes that produce one or more
branches, one or more terminating bins, or some combination
thereof. In some embodiments, the tree may include deterministic
nodes, statistical nodes, hybrid deterministic and statistical
nodes, or some combination thereof. In another embodiment, the tree
may include deterministic nodes designated by attribute name,
statistical nodes designated by name, hybrid nodes designated by
name, or some combination thereof.
[0018] In one embodiment, the sequence of rules may include only
statistical rules. In one such embodiment, these rules may be
organized into groups to aid in user understanding of the rules and
to allow selectivity in the characteristics to be used. This
selectivity has three major advantages: classification can be done
with significantly fewer examples, which may include abstract
examples; classification can be stable over more specimens; and
classification can be executed more quickly. In another embodiment,
the statistical rules may be weighted separately. In an additional
embodiment, the statistical rules and the hybrid rules may be
organized into groups for selection to aid in user understanding of
these rules and to provide classifications that reflect the intent
of the user.
[0019] The interactive user interface described above may
illustrate results of classification graphically and with sample
images. Each of the embodiments of the computer-implemented method
described above may include any other step(s) described herein.
BRIEF DESCRIPTION OF THE DRAWINGS
[0020] Further advantages of the present invention may become
apparent to those skilled in the art with the benefit of the
following detailed description of the preferred embodiments and
upon reference to the accompanying drawings in which:
[0021] FIG. 1 is a flow chart illustrating one embodiment of a
flexible computer-implemented method for classifying defects;
[0022] FIGS. 2 and 3 are prototype screenshots illustrating one
example of a user interface that can be used to perform one or more
of the computer-implemented methods described herein for
classifying defects;
[0023] FIG. 4 is a detailed example of a user interface that
demonstrates a hybrid classification tree; and
[0024] FIG. 5 is a schematic diagram illustrating a side view of
one embodiment of a system that can be used to perform one or more
of the computer-implemented methods described herein.
[0025] While the invention is susceptible to various modifications
and alternative forms, specific embodiments thereof are shown by
way of example in the drawings and may herein be described in
detail. The drawings may not be to scale. It should be understood,
however, that the drawings and detailed description thereto are not
intended to limit the invention to the particular form disclosed,
but on the contrary, the intention is to cover all modifications,
equivalents and alternatives falling within the spirit and scope of
the present invention as defined by the appended claims.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0026] As used herein, the term "defects" refers to any anomalies
that may be found on a semiconductor specimen. As used herein, the
term "semiconductor specimen" is used to refer to a wafer or any
other specimen known in the art such as a reticle or photomask.
Although embodiments are described herein with respect to a wafer,
it is to be understood that the embodiments may be used to classify
defects detected on any other specimen known in the art of
semiconductor manufacturing.
[0027] As used herein, the term "wafer" generally refers to
substrates formed of a semiconductor or non-semiconductor material.
Examples of such a semiconductor or non-semiconductor material
include, but are not limited to, monocrystalline silicon, gallium
arsenide, and indium phosphide. Such substrates may be commonly
found and/or processed in semiconductor fabrication facilities.
[0028] A wafer may include only the substrate such as an upatterned
virgin wafer. Alternatively, a wafer may include one or more layers
formed upon a substrate. For example, such layers may include, but
are not limited to, a resist, a dielectric material, and a
conductive material. A resist may include any material that may be
patterned by an optical lithography technique, an e-beam
lithography technique, or an X-ray lithography technique. Examples
of a dielectric material may include, but are not limited to,
silicon dioxide, silicon nitride, silicon oxynitride, and titanium
nitride. Additional examples of a dielectric material include
"low-k" dielectric materials such as Black Diamond.TM. which is
commercially available from Applied Materials, Inc., Santa Clara,
Calif., and CORAL.TM. commercially available from Novellus Systems,
Inc., San Jose, Calif., "ultra-low k" dielectric materials such as
"xerogels," and "high-k" dielectric materials such as tantalum
pentoxide. In addition, examples of a conductive material include,
but are not limited to, aluminum, polysilicon, and copper.
[0029] One or more layers formed on a wafer may be patterned or
unpatterned. For example, a wafer may include a plurality of dies,
each having repeatable pattern features. Formation and processing
of such layers of material may ultimately result in completed
semiconductor devices. As such, a wafer may include a substrate on
which not all layers of a complete semiconductor device have been
formed or a substrate on which all layers of a complete
semiconductor device have been formed. The term "semiconductor
device" is used interchangeably herein with the term "integrated
circuit." In addition, other devices such as microelectromechanical
(MEMS) devices and the like may also be formed on a wafer.
[0030] A flexible user interface combining trained and
deterministic rules is known as Rule Based Binning (RBB), which
will be commercially available from KLA-Tencor on the 23xx Release
10.1, eS3x Version 1.3, and 9xxx Version 9.0. RBB is a
deterministic rule based approach that allows the results of iADC
(trained approach) to be used as an input to the deterministic
rules. The capabilities of RBB are included and extended in this
application.
[0031] Turning now to the drawings, FIG. 1 illustrates one
embodiment of a flexible computer-implemented method for
classifying defects. It is noted that the steps shown in FIG. 1 are
not essential to practice of the method. One or more steps may be
omitted or added to the method illustrated in FIG. 1, and the
method can still be practiced within the scope of this
embodiment.
[0032] The method includes applying a sequence of rules for defects
to inspection data, as shown in step 10. The inspection data is
generated by inspection of a semiconductor specimen. The sequence
of rules includes statistical rules, deterministic rules, hybrid
statistical and deterministic rules, or some combination thereof.
In one embodiment, applying the sequence of rules may include
applying the statistical rules and using the results of application
of the statistical rules as input to the deterministic rules or
vice versa.
[0033] In some embodiments, the statistical rules, the
deterministic rules, and/or the hybrid rules that are applied to
the inspection data may be user defined. In other words, the
statistical, deterministic, and hybrid rules may be essentially
written and/or edited by the user. Alternatively, the statistical
rules, the deterministic rules, and/or the hybrid rules may be
generated by the computer-implemented method. The
computer-implemented method may include generating the statistical,
deterministic, and/or hybrid rules based on, for example, defect
images selected by a user. In addition, the computer-generated
rules may be edited by a user. The statistical rules, the
deterministic rules, and the hybrid rules may have any form known
in the art.
[0034] The deterministic rules are based on one or more measurable
characteristics of the defects. In some embodiments, these
characteristics may include attributes such as whether the defect
is dark or bright, how the defect was detected or detection method
(e.g., the level of tolerance in the threshold used to detect
defects), contrast of the defects with respect to background,
measured size, information about defects on other levels of the
specimen, location of the defects on the specimen, proximity to
other events, other attributes depending on the capabilities of the
inspection tool, or some combination of attributes that are used
deterministically to classify defects. The deterministic rules
apply one or more tests to the characteristics of the defects. For
example, the deterministic rules use one or more of these
measurable properties of the defects in a deterministic manner to
separate the defects for further processing and/or to assign them
to bins.
[0035] One example of a deterministic rule is that if light is
scattered from a defect at particular collection angles (e.g., at
substantially opposite collection angles, but not at other
collection angles), then the defect is a scratch. The collection
angles at which the light is scattered from the scratch may also
indicate the direction of the scratch (e.g., the direction in which
the scratch extends lengthwise). Another example of a deterministic
rule would be that defects that are located in areas with geometry
that is unimportant or redundant may be binned as nuisance defects.
The deterministic rules that are included in the sequence of rules
and are applied to the inspection data may vary depending on, for
example, the defects of interest, the defects that are expected to
be formed on the specimen, the specimen characteristics, the
process history of the specimen, and the like. The rules may also
vary depending on one or more characteristics of the inspection
tool that is used to generate the inspection data (e.g., inspection
tool type such as e-beam or optical, inspection tool configuration
such as wavelength, optical configuration, etc.).
[0036] The statistical rules are based on characteristics of the
defects such as color, size, edge sharpness, eccentricity,
roundness, transparency, texture ("roughness"), context, or some
combination thereof. Obviously, the statistical rules that are
applied to the inspection data may vary depending on, for example,
defects of interest, defects expected on the specimen,
characteristics of the defects of interest or the expected defects,
characteristics of the specimen, and process history of the
specimen. In addition, the statistical rules may vary depending on
the type of inspection tool that is used to generate the inspection
data and other characteristics of the inspection tool such as
wavelength, optical configurations, etc. The statistical rules
apply the characteristics statistically to bin defects.
[0037] The size of the defect may include various dimensional
characteristics such as height, width, length, aspect ratio, area,
and the like. Some of these characteristics can be used directly as
attributes, or they may be put through a statistical rule to find
defects that are similar in size to members of a training set or an
abstract concept of size. The characteristics of the defects input
to the statistical rules (and the deterministic rules) used for
application to the defect information may be selected by a user as
described further herein.
[0038] The characteristics used by the statistical rules may be
organized into groups to aid in user understanding of the
statistical rules. For example, the statistical characteristics may
be organized in sensible groups such as size and context to match
human understanding. Underlying each of the rules may, or may not,
be training sets that can be adjusted, but do not need to be
trained for every sample set. Therefore, the user is able to
classify defects using groups of characteristics that the user
selected thereby providing more control to the user than existing
trained classifiers without encumbering the user with fine
details.
[0039] The statistical rules may be based on any statistical
parameters known in the art. Examples of statistical rules include
a nearest neighbor type rule and a neural net type rule.
Statistical rules may be used, for example, to describe very
complex situations. In one such example, a statistical rule may be
used to classify background data into different types such as open,
sparse, and dense. These complex situations are difficult to
envision and develop into deterministic rules. The statistical
rules can be used to develop new attributes that can be used to
generate a new rule. The statistical rules may use one or more
characteristics of the defects as input or parameters. Such
characteristics may be used as a single group or in any
combination. Fewer samples are needed for a restricted sequence of
characteristics than for large set of characteristics. In addition,
one or more of the deterministic rules or one or more of the
statistical rules may be modified to account for other levels on
the specimen.
[0040] Typically, statistical rules are generated using training
data. The training data may include inspection data generated for a
number of specimens, which is analyzed statistically by the
computer-implemented method. The statistical analysis may then be
associated with various parameters of the specimen such as open,
sparse, and dense backgrounds. The associations may be assigned by
a user. Alternatively, the associations may be generated by the
computer-implemented method.
[0041] Each of the rules may include parameters for a number of
different characteristics. Any combination of such characteristics
may be used, and groups of individual characteristics may be
weighted separately. For example, each of the statistical
characteristics may be weighted separately in each rule. The exact
characteristics that are available for each rule are only
restricted by the nature of the inspection or review tool.
[0042] As shown in FIG. 1, the method also includes classifying the
defects based on results of the application of the sequence of
rules, as shown in step 16. In particular, the defects may be
classified based on the results of the deterministic rules, the
statistical rules, the hybrid rules, or the combination thereof
that are applied to the inspection data. Results of the
classification may include multiple output classifications for the
defects. In another example, the output of the classification
operation may feed into other automatic operations, such as
determining a sample plan for automatic or manual review of the
defects. In other words, the results of classification may be used
in sampling defects for a subsequent activity.
[0043] In one embodiment, classifying the defects may include
determining if the defects are nuisance defects based on the
results of the application of the deterministic rules or a
combination of deterministic and statistical rules to the
inspection data. For example, nuisance defects may be identified
using one or more deterministic rules that are based on information
about another level on the specimen or the process used to form the
specimen. In one such example, the location of a defect in a die on
a wafer may be compared to locations of nuisance or permissible
defects in the die, and if the location coincides, then the defect
may be identified as a nuisance defect. This comparison may be
performed based on various data sources, such as design data or
user input. Any other method may be used to detect nuisance defects
in inspection data. For example, if defects do not fall into at
least one of the bins, then the defects may be identified as
nuisance defects. In other words, the deterministic and statistical
rules may be created and selected to identify only those defects
which are of interest. Defects that are identified as nuisance
defects can be filtered (e.g., removed) from the inspection
data.
[0044] The methods described herein provide flexible, rule-based
approaches to classifying defects. One advantage of the methods
described herein is that the methods provide significant
flexibility and selectivity in the rules that are applied to the
inspection data. For example, the statistical rules, the
deterministic rules, and the hybrid rules that are used for
application to the inspection data may be defined by a user. The
user may write the rules or may edit existing rules or rules that
are computer generated. For example, these flexible recipes may be
stored in files as extensible markup language (XML) documents or
other usable formats as simple recipes or collections of rule based
recipes, and the recipes can be created and edited through off-line
or online user interfaces.
[0045] In addition, the sequence of rules that is applied to the
inspection data may be selected by a user. For example, the
flexible recipes can be reused and recombined as templates. In
particular, the methods can be implemented with a flexible user
interface (UI) that provides an extendable number of attributes and
relationships. The methods described herein allow the user to
evaluate defect attributes and combine results using Boolean or
arithmetic operators. New attributes can be added to the sequence
of rules by editing a configuration rather than having to
re-implement the software. In another example, the user may select
the rules from a rules database or library that contains all of the
existing rules that are available for application to inspection
data. Alternatively, the database or library may present only a
subset of the rules that are available for application to the
inspection data (e.g., based on the type of specimen that was
inspected or the history of the specimen). The user may then create
a sequence of rules that is to be applied to the inspection data by
selecting one or more rules from those presented. The user may also
create the sequence of rules using the UI that is described in
detail below.
[0046] The methods are also flexible in the manner in which they
may be performed. For example, the architecture used to implement
the methods described herein may be configured to allow the
sequence of rules to be invoked at any time that inspection data is
available. In this manner, the sequence of rules may be applied to
inspection data after testing operation 12 has been performed on a
specimen. The term "testing operation" as used herein is intended
to refer to one of the processes that may be performed on a
specimen by an inspection tool during inspection or a review tool
in the process of performing an automated review. In addition, or
alternatively, the sequence of rules may be applied to the
inspection data after complete wafer inspection or review 14.
Advantageously, the sequence of rules applied after each testing
operation and complete wafer inspection or review may be selected
based on their sensitivity to the type of data generated and the
types of defects being detected.
[0047] In another example, a portion of the sequence of the rules
may be applied as the defects are found during inspection. Another
portion of the sequence of rules may be applied at the end of the
inspection. This other portion of the sequence of rules may include
rules based on proximity of the defects to other defects on the
specimen or previous specimen history. Dependent rules may also be
applied after this other portion of the sequence of rules is
applied.
[0048] In another example, the methods are also flexible with
respect to when the sequence of rules is applied to the inspection
data. For example, a sequence of rules may be applied to the
inspection data after one testing operation while another testing
operation is being performed on the specimen. Therefore, the
sequence of rules may be applied to inspection data while the
inspection is still being performed. Additionally, the sequence of
rules may be applied to the inspection data while a user is
reviewing the defects. In this manner, the methods described herein
may improve the throughput of the overall defect classification
process.
[0049] In a further example of the flexibility of the methods
described herein, different sequences of rules may be applied to
the inspection data. In addition, the results of the application of
one sequence of rules may be used to determine if another sequence
of rules should be applied to the inspection data, and if so, then
which sequence of rules will be applied. The different sequences of
rules that are applied may also be selected by a user. Each of the
different sequences of rules may be configured as described above.
In particular, each of the different sequences of rules includes
one or more statistical rules, one or more deterministic rules, one
or more hybrid rules, or some combination thereof. In addition, the
different sequences of rules may be generated in different manners
(e.g., one sequence user defined, another sequence computer
generated) or in the same manner.
[0050] In addition, the user can be selective in the aspects or
characteristics of the defects that are to be used in the
statistical, deterministic, and/or hybrid rules based on known
areas of interest. For example, if the user is primarily interested
in defects because of the appearance of the background (its density
or geometry) rather than the appearance of the defect itself, rules
can be selected that focus on context. Alternatively, if size is
more important than shape to the user, size characteristics can be
weighted more heavily than shape.
[0051] Furthermore, the methods described herein can be performed
for inspection data generated by different inspection or review
tools having different hardware configurations. For example, as
described above, the deterministic rules, the statistical rules,
and the hybrid rules that are applied to inspection data may vary
depending on the configuration of the inspection tool that was used
to generate the inspection data. In addition, the deterministic
rules, the statistical rules, and the hybrid rules that are applied
may be varied relatively easily and quickly as described herein.
For example, a user is able to combine a configurable set of
characteristics together using Boolean expressions without a
limitation on the number of attributes or the types of attributes.
This flexibility makes it possible to use the same software on
multiple tools with different hardware. Therefore, the same method
may be applicable to many different semiconductor specimens and
many different inspection tools. In addition, these recipes may be
stored in files as extensible markup language (XML) documents or
other readable formats, which can be "ported" across different
inspection tools.
[0052] Moreover, the methods described herein are flexible and
advantageous in that they can be implemented using any software
constructs known in the art with any interface known in the art.
For example, the methods described herein may be configured as
plug-ins for various other defect analysis engines. The UI can be
implemented using any tool package known in the art and can be run
on any operating system known in the art.
[0053] The method shown in FIG. 1 may also include a number of
additional steps. For example, in one embodiment, the method may
include tuning one or more inspection recipes based on the results
of classification, as shown in step 18. Tuning the inspection
recipes may be performed by looking at defects in different
combinations of categories. In some embodiments, tuning the
inspection recipe(s) may include altering one or more parameters of
the inspection recipe(s) such that the inspection recipe(s) are
more sensitive to one or more selected types of defects. The one or
more parameters of the inspection recipe(s) that may be altered may
include, for example, the type of inspection tool, the wavelength
of illumination, the angle of incidence, the angle of collection,
the polarization of light, sampling rate, etc. In another
embodiment, tuning the inspection recipe(s) may include altering
one or more parameters of the inspection recipe(s) such that the
inspection recipe(s) are less sensitive to nuisance defects. In a
further embodiment, tuning the inspection recipe(s) may include
tuning an inspection recipe that will be used to re-inspect the
specimen. Re-inspection may be performed after classification of
the defects or after another process such as a repair or cleaning
process has been performed on the specimen. In another embodiment,
instead of tuning an existing recipe, the method may include
creating a new inspection recipe based on the results of the
classification as described above.
[0054] In another embodiment, the method may include performing
engineering analysis using the results of classifying the defects,
as shown in step 20. The engineering analysis may include using the
defect data generated by the classification methods described
herein and optionally running different classification recipes. The
engineering analysis may also include, for example, determining if
one or more of the processes used to fabricate the specimen are
outside of the established tolerances. In another example, the
engineering analysis may include monitoring one or more of the
processes used to fabricate the specimen. In addition, the
engineering analysis may include determining if one or more
parameters of one or more processes used to fabricate the specimen
should be altered and determining the new parameters or correction
factors if corrections are desired. In this manner, the engineering
analysis may include statistical process control, feedback control,
and any other type of control known in the art. For example, the
engineering analysis may include feedforward control for
determining one or more processes to be performed on the specimen
and/or one or more parameters for one or more processes to be
performed on the specimen. In one such example, the engineering
analysis may include determining if a cleaning or other repair
process should be performed on the specimen.
[0055] In another example, the method may include analyzing the
defects that have been binned. Analyzing the defects may include
determining the locations of defects in a bin, mapping the
locations of defects in a bin, or performing any other functions on
the inspection data corresponding to the defects in a bin. In an
additional example, the method may include analyzing the actual
defects on the wafer using one or more additional inspection and/or
review processes or tools such as energy dispersive X-ray
spectroscopy ("EDS") devices, which can be used to determine the
composition of a defect. In a further example, the method may
include sorting defects within a bin. The defects within a bin may
be sorted using one or more characteristics of the defects such as
location and/or size.
[0056] The deterministic rules, the statistical rules, the hybrid
rules, or some combination thereof that are applied to the
inspection data may be selected by a user as described above. For
example, the sequence of rules that is applied to the inspection
data may be selected and organized by a user working with an
interactive UI. In particular, the user may construct a flexible
classifier by working with an interactive UI that represents the
separation of defects as a tree that can have many different
levels. However, the separation of the defects may be represented
in the interactive UI using any other method, graphic, or structure
known in the art.
[0057] One example of such a UI is illustrated in FIG. 2. The
sequence of rules is represented in the interactive UI as tree 24
having different levels 26. Although four levels of the tree are
illustrated in the interactive UI shown in FIG. 2, it is to be
understood that more levels of the tree can be illustrated by
scrolling up and down. In addition, although four levels of the
tree are illustrated in the interactive UI shown in FIG. 2, it is
to be understood that the tree may include any suitable number of
levels, and the flexibility with which the tree may be generated
and edited advantageously provides significant advantages in the
different types and configurations of trees that may be
generated.
[0058] Users can construct the rule/decision tree by working with
the basic elements as nodes, which can be constructed as
deterministic or statistical nodes, or users can combine the
elements into more complex nodes that combine statistical
classifiers with attributes (i.e., hybrid nodes). As further shown
in FIG. 2, tree 24 includes nodes 28 that produce one or more
branches 30, one or more terminating bins 32, or some combination
thereof. Any node in the tree, regardless of type, can produce one
or more branches and/or one or more terminating bins. As such, the
output of statistical classifiers is the same as deterministic node
outputs. In other words, both types of nodes function as rules.
[0059] Each node in the tree represents a rule that is included in
a sequence. In this manner, the tree may include deterministic
nodes, statistical nodes, hybrid deterministic and statistical
nodes, or some combination thereof. In the tree, the deterministic
nodes may be designated by attribute name (e.g., the attribute
being evaluated and the rule being applied), and statistical and
other complex nodes (e.g., hybrid nodes) may be designated by name.
Such a graphical representation assists in seeing and comprehending
the outcome of choices made by the user. Each of the nodes, simple
or complex, may be saved as a sub-recipe and reused in other
classifiers.
[0060] The interactive UI may illustrate results of classifying
graphically and with sample images. For example, as shown in
classifier performance box 34, the UI may illustrate the number of
defects that are grouped in one node. Although the classifier
performance is illustrated in a confusion diagram, it is to be
understood that any suitable graphic or method may be used to
illustrate the classifier performance. The node for which the
performance is illustrated may be selected by selecting a node in
tree 24 such as the classifier node that is highlighted in tree 24.
In addition, sample images of defects may be illustrated by
selecting a "show defects" option (not shown) in the classifier
performance box. The sample images may include the raw inspection
data. Other types of defect data may be illustrated in a similar
manner. The results of the classifying may be available graphically
and through example images as the user constructs the tree for a
sample population. Therefore, the UI provides feedback to the user
during the setup of the classifier.
[0061] The interactive UI may also illustrate other information
about the selected node. For example, the UI may display general
information about the node in node info box 38. In one example, the
node type may be listed in the node info box. In addition,
classifier information may be listed in the node info box.
Furthermore, any changes that are made to the selected node using
the UI may be saved by selecting the save button in the node info
box. In addition, the existing node in the tree may be replaced by
selecting the load button in the node info box. Selecting the load
button in the node info box may result in the presentation of a
number of nodes that are available to the user for insertion into
the tree.
[0062] The node info box also illustrates the characteristics of
the node that were manually selected for the node. For example, in
manual characteristics selection portion 40 of node info box 38,
the characteristics that are available for a node are illustrated.
In addition, the manual characteristics selection portion
illustrates both those characteristics that were selected as well
as characteristics that were not selected. In particular, for the
selected node, the selected characteristics are a subset of the
available characteristics for this particular type of inspection
equipment.
[0063] Although a number of particular characteristics are
illustrated in the UI of FIG. 2, it is to be understood that any
number of characteristics may be available and selected for each
node depending on the equipment, and the characteristics that are
available for each node may include any appropriate characteristics
known in the art. As further shown in the manual characteristics
selection portion, each characteristic can be manually weighted
individually, and the weighting is shown both graphically and
numerically. In addition, the user can select default button 42 to
set the individual weights assigned to the characteristics to their
default values.
[0064] The node info box also provides information about the
training set that was used to generate the node. Training set
portion 44 may only be illustrated for those nodes (e.g.,
statistical nodes) that were generated using a training set. The
contents of the training set may be illustrated by selecting the
file shown in the training set portion. In addition, the UI may be
configured such that the training set may be edited once the
training set has been opened by selecting this file. The training
set may be edited manually. Alternatively, the training set may be
altered automatically by the computer-implemented method, and such
alterations may not be illustrated in the UI until they are
finished.
[0065] The UI shown in FIG. 2 may also include available defects
box 46. The available defects box may illustrate the verification
defects. For example, the available defects box may illustrate the
defects as they were classified by another classification method
such as ADC. Therefore, the results of different methods may be
compared, and the individual classification functions may be edited
accordingly. As shown in FIG. 2, the available defects box may
illustrate images of the defects. Alternatively, the available
defects box may provide information about the verification defects
using any suitable method known in the art.
[0066] Although the UI is shown in FIG. 2 to include four different
boxes containing information about a tree and a selected node of
the tree, it is to be understood that the UI may include fewer than
four information boxes or more than four information boxes. In
general, the amount and organization of the information shown in
the UI may be designed to present the maximum amount of information
to a user in the most manageable and easy-to-comprehend manner
possible.
[0067] FIG. 3 illustrates another example of the user interface in
which a different node is selected in tree 24. For example, in the
prototype screenshot illustrated in FIG. 2, a statistical
classifier node was selected while in the prototype screenshot of
FIG. 3, a deterministic node is selected. In this manner, a
comparison of FIGS. 2 and 3 demonstrates that the information that
is displayed in the user interface will change depending on the
node and the type of node that is selected. For example, like FIG.
2, FIG. 3 includes classifier performance box, which may be
configured as described above. However, the classifier performance
box, in this example, may include the grouping or separation of the
defects by the deterministic node that is selected, instead of the
classifier node that was previously selected.
[0068] In addition, FIG. 3 includes node info box 38. However, like
the classifier performance box, the information that is displayed
in the node info box has changed to reflect the selected node. For
example, because a deterministic node was selected in tree 24, the
node info box displays description 48 of the original rule and
description 50 of the rule definition. Although the original rule
and rule definition descriptions are the same in FIG. 3, it is to
be understood that these descriptions may be different.
[0069] Node info box 38 also includes build the rule portion 52,
which provides list 54 of a number of different attributes that can
be selected or de-selected by a user. In addition, the build the
rule portion includes list 56 of possible operators that may be
combined with the selected attribute(s). In this manner, the method
may include building one or more of the deterministic rules
included in a sequence using the UI to apply unrestricted Boolean
operators to defect attributes. The list of possible operators may
be altered depending on which attribute(s) are selected by the
user.
[0070] The build the rule portion further includes input box 58 in
which a user can enter a value to be used with the selected
operator. The user can enter the value by clicking arrows next to
the input value box until the desired value is displayed, or the
user can type the value in the input value box. The build the rule
portion may also display histogram 60 to the user if histogram
option 62 is checked. The histogram may illustrate the number of
defects that have various values of the selected attribute(s). In
this manner, the build the rule box may provide information about
the defects to the user such that the user can use this information
to build a rule that will be useful for the defects on the
specimen.
[0071] As shown in FIG. 3, this user interface does not include a
training set box like training set box 44 shown in FIG. 2. A
training set box is not illustrated in the user interface of FIG. 3
since the selected node is a deterministic node. A training set
will not be available for a deterministic node.
[0072] As further shown in FIG. 3, the user interface includes
available defects box 46, which as described above may illustrate
verification defects. The verification defect information
illustrated in this box may include the information described
above. In addition, the verification defect information may provide
useful information to the user while building a rule. The available
defects box may be further configured as described above. The user
interface of FIG. 3 may be further configured as described above
and will have the same advantages as the user interface of FIG.
2.
[0073] FIG. 4 is a more detailed view of an example of a hybrid
tree. This example illustrates the concept that deterministic nodes
64 and 66 can branch to statistical nodes 68 and 70, respectively.
In addition, statistical nodes can branch to deterministic nodes.
For example, the deterministic rule nodes for defect area can
branch to statistical rule nodes, and some of these statistical
rule nodes can branch to deterministic rule nodes based on whether
the defect is bright or dark. In one such example illustrated in
FIG. 4, deterministic node 66 branches to statistical node 70, and
statistical node 70 branches to three different statistical nodes
72, 74, and 76. In addition, statistical node 74 branches to
deterministic nodes 78 and 80. Deterministic node 78 represents a
deterministic rule based on whether a defect is bright, and
deterministic node 80 represents a deterministic rule based on
whether a defect is dark. The tree may be further configured as
described above such that all defects 82 may be classified.
[0074] The methods described herein, therefore, increase the
effectiveness of semiconductor specimen inspection and review tools
by separating defects into bins or classes based on criteria that
combine deterministic, statistical, and/or hybrid rules in a
flexible, quick, and intuitive manner that emphasizes the user's
current priorities for doing the separation. In addition, the
classifier is faster and easier to set up than a trained classifier
with more power than the existing rule-based approaches used in the
industry. Each of the embodiments of the method described above may
include any other step(s) described herein.
[0075] Program instructions implementing methods such as those
described herein may be transmitted over or stored on a carrier
medium. The carrier medium may be a transmission medium such as a
wire, cable, or wireless transmission link, or a signal traveling
along such a wire, cable, or link. The carrier medium may also be a
storage medium such as a read-only memory, a random access memory,
a magnetic or optical disk, or a magnetic tape.
[0076] In an embodiment, a processor may be configured to execute
the program instructions to perform a computer-implemented method
according to the above embodiments. The processor may take various
forms, including a personal computer system, mainframe computer
system, workstation, network appliance, Internet appliance,
personal digital assistant ("PDA"), television system or other
device. In general, the term "computer system" may be broadly
defined to encompass any device having one or more processors,
which executes instructions from a memory medium.
[0077] The program instructions may be implemented in any of
various ways, including procedure-based techniques, component-based
techniques, and/or object-oriented techniques, among others. For
example, the program instructions may be implemented using ActiveX
controls, C++ objects, JavaBeans, Microsoft Foundation Classes
("MFC"), or other technologies or methodologies, as desired.
[0078] FIG. 5 illustrates one embodiment of a system configured to
perform one or more of the computer-implemented methods described
herein for classifying defects detected on semiconductor specimen
90. The system shown in FIG. 5 is configured to inspect a
semiconductor specimen such as a wafer. However, the system may
have any configuration known in the art that is suitable for
inspection of any other semiconductor specimen (e.g., a
reticle).
[0079] The system includes processor 92. The processor may include
any suitable processor known in the art. For example, the processor
may be an image computer or a parallel processor. In addition, the
processor may be configured as described above. The system also
includes carrier medium 94. The carrier medium may be configured as
described above. For example, carrier medium 94 includes program
instructions 96, which are executable on processor 92. The program
instructions may be executable for performing any of the
embodiments of the methods described above. The program
instructions may be further configured as described above.
[0080] In some embodiments, the system may also include inspection
and/or review tool 98. Tool 98 may be configured to detect defects
on semiconductor specimen 90 and to generate inspection data for
the semiconductor specimen that contains information about the
defects on the semiconductor specimen. Tool 98 may be coupled to
processor 92. For example, one or more components of tool 98 may be
coupled to processor 92 by a transmission medium (not shown). The
transmission medium may include "wired" and "wireless" portions. In
another example, detector 100 of tool 98 may be configured to
generate output 102. The output may be transmitted across a
transmission medium from detector 100 to processor 92. In some
embodiments, the output may also be transmitted through one or more
electronic components coupled between the detector and the
processor. Therefore, output 102 is transmitted from the tool to
the processor, and program instructions 96 may be executable on the
processor to bin defects detected on the semiconductor specimen
using the inspection data included in output 102. Program
instructions 96 may be further executable on the processor to
perform other functions described herein (e.g., perform
classification functions, sort defects within a bin, map defects
within a bin, etc.). The program instructions may also be
executable on the processor to detect defects on the semiconductor
specimen using any method known in the art (e.g., die-to-die
comparisons).
[0081] Inspection and/or review tool 98 may be configured to
perform inspection of the semiconductor specimen using any
technique known in the art. For example, the tool may be configured
to detect light scattered by the semiconductor specimen and/or to
form images of the specimen. In addition, the tool includes stage
104 upon which semiconductor specimen 90 may be disposed during
measurements. The stage may include any suitable mechanical or
robotic assembly known in the art. The tool also includes light
source 106. Light source 106 may include any appropriate light
source known in the art. In addition, the tool may include beam
splitter 108, which is configured to direct light from light source
106 onto specimen 90 at angles that are approximately normal to an
upper surface of specimen 90. The beam splitter may include any
suitable beam splitter known in the art. The tool further includes
detector 100, which is configured to detect light transmitted by
beam splitter 108. The detector is also configured to generate
output 102. The detector may include any of the detectors described
above or any other suitable detector known in the art.
[0082] Although one general configuration of the inspection and/or
review tool is shown in FIG. 5, it is to be understood that the
tool may have any suitable configuration known in the art. For
example, inspection and/or review tool 98 may be replaced with the
measurement head of the 2360 tool, one of the AIT family of tools,
the SL3UV tool, one of the Surfscan family of tools, the TeraScan
or TeraStar tool, and one of the Viper family of tools, all of
which are commercially available from KLA-Tencor. In addition, the
inspection and/or review tool may include other optical systems
such as optical imaging systems, ellipsometer-based systems,
scatterometer-based systems, etc. and/or e-beam systems such as a
CD SEM and the eS25 and eS30 systems, which are commercially
available from KLA-Tencor.
[0083] Further modifications and alternative embodiments of various
aspects of the invention may be apparent to those skilled in the
art in view of this description. For example, computer-implemented
methods for classifying defects are provided. Accordingly, this
description is to be construed as illustrative only and is for the
purpose of teaching those skilled in the art the general manner of
carrying out the invention. It is to be understood that the forms
of the invention shown and described herein are to be taken as the
presently preferred embodiments. Elements and materials may be
substituted for those illustrated and described herein, parts and
processes may be reversed, and certain features of the invention
may be utilized independently, all as would be apparent to one
skilled in the art after having the benefit of this description of
the invention. Changes may be made in the elements described herein
without departing from the spirit and scope of the invention as
described in the following claims.
* * * * *