U.S. patent application number 15/204461 was filed with the patent office on 2016-11-03 for multi-model metrology.
This patent application is currently assigned to KLA-Tencor Corporation. The applicant listed for this patent is KLA-Tencor Corporation. Invention is credited to Meng Cao, In-Kyo Kim, Liequan Lee, Xin Li, Sangbong Park, Leonid Poslavsky, Andrei V. Shchegrov, Sungchul Yoo.
Application Number | 20160322267 15/204461 |
Document ID | / |
Family ID | 52481579 |
Filed Date | 2016-11-03 |
United States Patent
Application |
20160322267 |
Kind Code |
A1 |
Kim; In-Kyo ; et
al. |
November 3, 2016 |
MULTI-MODEL METROLOGY
Abstract
Disclosed are apparatus and methods for characterizing a
plurality of structures of interest on a semiconductor wafer. A
plurality of models having varying combinations of floating and
fixed critical parameters and corresponding simulated spectra is
generated. Each model is generated to determine one or more
critical parameters for unknown structures based on spectra
collected from such unknown structures. It is determined which one
of the models best correlates with each critical parameter based on
reference data that includes a plurality of known values for each
of a plurality of critical parameters and corresponding known
spectra. For spectra obtained from an unknown structure using a
metrology tool, different ones of the models are selected and used
to determine different ones of the critical parameters of the
unknown structure based on determining which one of the models best
correlates with each critical parameter based on the reference
data.
Inventors: |
Kim; In-Kyo; (San Jose,
CA) ; Li; Xin; (Shanghai, CN) ; Poslavsky;
Leonid; (Belmont, CA) ; Lee; Liequan;
(Fremont, CA) ; Cao; Meng; (Union City, CA)
; Yoo; Sungchul; (San Jose, CA) ; Shchegrov;
Andrei V.; (Los Gatos, CA) ; Park; Sangbong;
(Union City, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
KLA-Tencor Corporation |
Milpitas |
CA |
US |
|
|
Assignee: |
KLA-Tencor Corporation
Milpitas
CA
|
Family ID: |
52481579 |
Appl. No.: |
15/204461 |
Filed: |
July 7, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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14459516 |
Aug 14, 2014 |
9412673 |
|
|
15204461 |
|
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61869434 |
Aug 23, 2013 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H01L 22/20 20130101;
H01L 22/12 20130101; G06F 30/39 20200101; G03F 7/70625 20130101;
H01L 21/67288 20130101; G03F 7/70616 20130101 |
International
Class: |
H01L 21/66 20060101
H01L021/66; G06F 17/50 20060101 G06F017/50; G03F 7/20 20060101
G03F007/20; H01L 21/67 20060101 H01L021/67 |
Claims
1. A method of characterizing a plurality of structures of interest
on a sample, the method comprising: generating a plurality of
models that are different from each other in that they have
different combinations of floating and fixed critical parameters
for outputting simulated spectra, wherein the models are generated
as final models to determine different one or more critical
parameters for unknown structures based on spectra collected from
such unknown structures; after generating the models and without
generating another model, determining which one of the models best
correlates with each critical parameter based on reference data
that includes a plurality of known values for each of a plurality
of critical parameters and corresponding known spectra; and for
spectra measured from an unknown structure using a metrology tool,
selecting and using different ones of the models to determine
different ones of the critical parameters of the unknown structure
based on determining which one of the models best correlates with
each critical parameter based on the reference data.
2. The method of claim 1, wherein the models have different sets of
one or more critical parameters that are fixed and different sets
of one or more critical parameters that are floating.
3. The method of claim 1, wherein each of the models has a low
degree of freedom and is configured to provide a different subset
of the critical parameters of the unknown structure.
4. The method of claim 1, wherein at least one of the models is
configured to utilize a same geometric model with a plurality of
different constraint conditions that correspond to a plurality of
sub-models or utilize different geometric models that correspond to
a plurality of sub-models.
5. The method of claim 1, wherein at least a first one of the
models is configured to send a selected critical parameter to a
second one of the models using a transform function.
6. The method of claim 1, wherein the spectra from the known
structure and the unknown structure, is acquired using one or more
of the following: spectroscopic ellipsometry, Mueller matrix
spectroscopic ellipsometry, spectroscopic reflectometry,
spectroscopic scatterometry, beam profile reflectometry, beam
profile ellipsometry, a single wavelength, a single discrete
wavelength range, or multiple discrete wavelength ranges.
7. The method of claim 1, wherein each of the models is generated
using a rigorous wave coupling analysis technique.
8. The method of claim 1, wherein the critical parameters include a
middle critical dimension (MCD), top CD (TCD), bottom CD (BCD),
height (HT) and side wall angle (SWA).
9. The method of claim 1, wherein a first one of the models is
selected and used to determine a first one of the critical
parameters and a second one of the models is selected and used to
determine a second one of the critical parameters, wherein the
first model has a higher correlation for determining the first
critical parameter than the second model, and wherein the second
model has a higher correlation for determining the second critical
parameter than the first model.
10. The method of claim 1, wherein selecting and using different
models includes selecting between a plurality of sub-models of a
first model based on execution of the first model meeting a
condition, wherein each sub-model is configured for determining a
same set of critical parameters.
11. The method of claim 1, wherein each sub-model has different
sets of fixed and floating critical parameters and the first model
is initially executed with all its critical parameters
floating.
12. The method of claim 1, wherein selecting and using different
models includes selecting between a plurality of sub-models of a
first model and the first model based on execution of the first
model meeting a condition, wherein each sub-model is configured for
determining a different subset of a base set of critical parameters
and the first model is configured for determining the base set of
critical parameters.
13. The method of claim 1, wherein selecting and using different
models is further based on an expected critical dimension
range.
14. The method of claim 1, wherein different models are also
selected and used for different subsystems of the metrology
tool.
15. A semiconductor metrology tool, comprising: an illuminator for
generating illumination; illumination optics for directing the
illumination towards an unknown structure; collection optics for
directing a plurality of spectra from the unknown structure to a
sensor; the sensor for acquiring the plurality of spectra signals
from the unknown structure; and a processor and memory configured
for performing the following operations: generating a plurality of
models that are different from each other in that they have
different combinations of floating and fixed critical parameters
for outputting simulated spectra, wherein the models are generated
as final models to determine different one or more critical
parameters for unknown structures based on spectra collected from
such unknown structures; and after generating the models and
without generating another model, determining which one of the
models best correlates with each critical parameter based on
reference data that includes a plurality of known values for each
of a plurality of critical parameters and corresponding known
spectra.
16. The metrology tool of claim 15, wherein at least one of the
models is configured to utilize a same geometric model with a
plurality of different constraint conditions that correspond to a
plurality of sub-models or utilize different geometric models that
correspond to a plurality of sub-models.
17. The metrology tool of claim 15, wherein at least a first one of
the models is configured to send a selected critical parameter to a
second one of the models using a transform function.
18. The metrology tool of claim 15, wherein the spectra from the
known structure and the unknown structure is acquired using one or
more of the following: spectroscopic ellipsometry, Mueller matrix
spectroscopic ellipsometry, spectroscopic reflectometry,
spectroscopic scatterometry, beam profile reflectometry, beam
profile ellipsometry, a single wavelength, a single discrete
wavelength range, or multiple discrete wavelength ranges.
19. The metrology tool of claim 15, wherein the critical parameters
include a middle critical dimension (MCD), top CD (TCD), bottom CD
(BCD), and side wall angle (SWA).
20. The metrology tool of claim 15, wherein a first one of the
models is selected and used to determine a first one of the
critical parameters and a second one of the models is selected and
used to determine a second one of the critical parameters, wherein
the first model has a higher correlation for determining the first
critical parameter than the second model, and wherein the second
model has a higher correlation for determining the second critical
parameter than the first model.
21. The metrology tool of claim 15, wherein selecting and using
different models includes selecting between a plurality of
sub-models of a first model based on execution of the first model
meeting a condition, wherein each sub-model is configured for
determining a same set of critical parameters.
22. The metrology tool of claim 15, wherein each sub-model has
different sets of fixed and floating critical parameters and the
first model is initially executed with all its critical parameters
floating.
23. The metrology tool of claim 15, wherein selecting and using
different models includes selecting between a plurality of
sub-models of a first model and the first model based on execution
of the first model meeting a condition, wherein each sub-model is
configured for determining a different subset of a base set of
critical parameters and the first model is configured for
determining the base set of critical parameters.
24. The metrology tool of claim 15, wherein selecting and using
different models is further based on an expected critical dimension
range.
25. The metrology tool of claim 15, wherein different models are
also selected and used for different subsystems of the metrology
tool.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 14/459,516, filed 14 Aug. 2014 by In-kyo Kim et al., which
claims the benefit of prior application U.S. Provisional
Application No. 61/869,434, filed 23 Aug. 2013 by In-kyo Kim et al.
Both applications are herein incorporated by reference in their
entireties for all purposes.
TECHNICAL FIELD OF THE INVENTION
[0002] The present invention relates generally to methods and
systems for characterization of semiconductor wafers and, more
specifically, to model-based metrology.
BACKGROUND
[0003] Photolithography or optical lithography systems used in the
manufacture of integrated circuits have been around for some time.
Such systems have proven extremely effective in the precise
manufacturing and formation of very small details in the product.
In some photolithography systems, a circuit image is written on a
substrate by transferring a pattern via, a light or radiation beam
(e.g., UV or ultraviolet light). For example, the lithography
system may include a light or radiation source that projects a
circuit image through a reticle and onto a silicon wafer coated
with a material sensitive to irradiation, e.g., photoresist. The
exposed photoresist typically forms a pattern that after
development masks the layers of the wafer during subsequent
processing steps, as for example deposition and/or etching.
[0004] Due to the large scale of circuit integration and the
decreasing size of semiconductor devices, the reticles and
fabricated devices have become increasingly sensitive to critical
dimension (CD) variations, as well as other critical parameter
variations such as film thickness and composition, etc. These
variations, if uncorrected, can cause the final device to fail to
meet the desired performance due to electrical timing errors. Even
worse, these errors can cause final devices to malfunction and
adversely affect yield.
[0005] In one metrology technique, critical dimension is measured
by scanning electron microscope CD-SEM images at each location on
the wafer and examining each image for pattern quality. This
technique is time consuming (e.g., several hours). Other techniques
have their own disadvantages.
[0006] In view of the foregoing, improved metrology apparatus and
techniques for determining critical parameters are needed.
SUMMARY
[0007] The following presents a simplified summary of the
disclosure in order to provide a basic understanding of certain
embodiments of the invention. This summary is not an extensive
overview of the disclosure and it does not identify key/critical
elements of the invention or delineate the scope of the invention.
Its sole purpose is to present some concepts disclosed herein in a
simplified form as a prelude to the more detailed description that
is presented later.
[0008] In one embodiment, a method of characterizing a plurality of
structures of interest on a semiconductor wafer is disclosed. A
plurality of models having varying combinations of floating and
fixed critical parameters and corresponding simulated spectra is
generated. Each model is generated to determine one or more
critical parameters for unknown structures based on spectra
collected from such unknown structures. It is determined which one
of the models best correlates with each critical parameter based on
reference data that includes a plurality of known values for each
of a plurality of critical parameters and corresponding known
spectra. For spectra obtained from an unknown structure using a
metrology tool, different ones of the models are selected and used
to determine different ones of the critical parameters of the
unknown structure based on determining which one of the models best
correlates with each critical parameter based on the reference
data.
[0009] In a specific implementation, the models have different sets
of one or more critical parameters that are fixed and different
sets of one or more critical parameters that are floating. In
another aspect, each of the models has a low degree of freedom and
is configured to provide a different subset of the critical
parameters of the unknown structure. In yet another aspect, at
least one of the models is configured to utilize a same geometric
model with a plurality of different constraint conditions that
correspond to a plurality of sub-models or utilize different
geometric models that correspond to a plurality of sub-models. In
another example, at least a first one of the models is configured
to send a selected critical parameter to a second one of the models
using a transform function. In a specific implementation, the
spectra from the known structure and the unknown structure is
acquired using one or more of the following: spectroscopic
ellipsometry, Mueller matrix spectroscopic ellipsometry,
spectroscopic reflectometry, spectroscopic scatterometry, beam
profile reflectometry, beam profile ellipsometry, a single
wavelength, a single discrete wavelength range, or multiple
discrete wavelength ranges. In another specific example, the models
are generated using a rigorous wave coupling analysis
technique.
[0010] In one embodiment, the critical parameters include a middle
critical dimension (MCD), top CD (TCD), bottom CD (BCD), profile
height (HT), side wall angle (SWA) and material properties. In
another aspect, different models have higher correlations for
different one or more of the critical parameters than other models,
and different models are selected and used based on which models
have a highest correlation for each critical parameter. In another
aspect, selecting and using different models includes selecting
between a plurality of sub-models of a first model based on
execution of the first model meeting a condition, and each
sub-model is configured for determining a same set of critical
parameters. In another aspect, each sub-model has different sets of
fixed and floating critical parameters and the first model is
initially executed with all its critical parameters floating. In an
alternative embodiment, selecting and using different models
includes selecting between a plurality of sub-models of a first
model and the first model based on execution of the first model
meeting a condition, and each sub-model is configured for
determining a different subset of a base set of critical parameters
and the first model is configured for determining the base set of
critical parameters. In another implementation, selecting and using
different models is further based on an expected critical dimension
range. In another aspect, different models are also selected and
used for different subsystems of the metrology tool.
[0011] In an alternative embodiment, the invention pertains to a
system for inspecting or measuring a specimen. This system
comprises an illuminator for generating illumination and
illumination optics for directing the illumination towards an
unknown structure, The system also includes collection optics for
directing a plurality of spectra signals in response to the
illumination from the unknown structure to a sensor of the system.
The system further includes a processor and memory configured for
performing any of the above described operations.
[0012] These and other aspects of the invention are described
further below with reference to the figures.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] FIG. 1 is a diagrammatic illustration of a hybrid multi-tool
metrology system.
[0014] FIG. 2 is a diagrammatic representation of a model that
simulates a spectral response for a representative semiconductor
structure having one or more feature characteristics.
[0015] FIG. 3A is a graph of an example model's spectra results as
a function of wavelength for uncorrelated varying feature
parameters.
[0016] FIG. 3B is a graph of a second example model's spectra
results as a function of wavelength for correlated varying feature
parameters.
[0017] FIG. 4 is a diagrammatic representation of a multiple model
system in accordance with one embodiment of the present
invention.
[0018] FIG. 5 is a diagrammatic representation of a multiple model
system in accordance with an alternative implementation of the
present invention.
[0019] FIG. 6 is a flow chart illustrating a multi-model setup
process for determining a set of models for determining critical
parameters in accordance with one embodiment of the present
invention.
[0020] FIG. 7A illustrates two different models' correlation for
height with respect to reference data.
[0021] FIG. 7B illustrates two different models' correlation for
MCD with respect to reference data.
[0022] FIG. 8 is an example metrology flow for selecting a
sub-model based on a condition in accordance with a specific
implementation of the present embodiment.
[0023] FIG. 9 is a second example metrology flow for selecting a
single model or a multi-model based on a condition in accordance
with another embodiment of the present embodiment.
[0024] FIG. 10 is a third example metrology flow for selecting a
sub-model based on a condition in accordance with another
embodiment of the present embodiment.
[0025] FIG. 11 illustrates improved critical parameter correlation
by use of multiple models in accordance with one embodiment of the
present invention.
[0026] FIG. 12 illustrates an example metrology system in
accordance with one embodiment of the present invention.
DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS
[0027] In the following description, numerous specific details are
set forth in order to provide a thorough understanding of the
present invention. The present invention may be practiced without
some or all of these specific details. In other instances, well
known process operations have not been described in detail to not
unnecessarily obscure the present invention. While the invention
will be described in conjunction with the specific embodiments, it
will be understood that it is not intended to limit the invention
to the embodiments.
Introduction
[0028] FIG. 1 is a diagrammatic illustration of a hybrid multi-tool
metrology system 122. As shown the hybrid system 122 may utilize a
primary tool 126 to combine results from multiple tools, e.g., 124a
and 124b, to improve the measurements of one or more critical
parameters via wafer fabrication control system 128, including the
photolithography process tool. By way of examples, the various
metrology tools can include any of the following tools: CD-SEM
(critical dimension scanning electron microscopy), CD-TEM (CD
transmission electron microscopy), CD-AFM (CD) atomic force
microscopy), and/or SCD (scatterometry critical dimension). Each
type of tool may have associated strengths and weaknesses, For
instance, CD-SEM, CD-TEM, and CD-AFM are destructive and
time-consuming.
[0029] SCD is a non-destructive metrology technique that is based
on optical scatterometry signals or spectra measurements from
various semiconductor targets on a product or test wafer. In some
implementations, a model is implemented to simulate expected
spectra results from various target structures, FIG. 2 is a
diagrammatic representation of a model that simulates a spectral
response for a representative semiconductor structure 202 having
one or more feature characteristics. For example, the modeled
target structure 202 may have a profile height (HT) 204a, sidewall
angle (SWA) 204b, pitch 204c, middle critical dimension (MCD) 204d,
material composition, etc. The model simulates incident light 206
that is directed by a particular tool towards the structure and
scattered light 208 from such structure.
[0030] A model typically simulates results from structures having
various parameter configurations. FIG. 3A is a graph of an example
model's spectra results as a function of wavelength for
uncorrelated varying feature parameters. For instance, spectra
results 302 result from an initial set of values for top CD (TCD)
and bottom CD (BCD). Spectra portion 304 results from a change in
TCD, while spectra portion 306 results from a change in BCD. The
parameters TCD and BCD are uncorrelated in this example and the
results from changing these parameters are easily distinguishable
from each other.
[0031] However, an SCD model typically has correlations between
parameters, and the accuracy of CD can be adversely affected by
such correlations. FIG. 3B is a graph of a second example model's
spectra results as a function of wavelength for correlated varying
feature parameters. Spectra results 352 correspond to a particular
set of TCD and BCD values. In this model, the spectra changes
corresponding to TCD and BCD (e.g., 354a and 354b) are difficult to
distinguish from each other and are highly correlated.
[0032] Parameter correlations can be broken by fixing one or more
parameters. If there are many critical parameters, each critical
parameter can have a best accuracy with different parameters being
fixed. One method, referred to herein as a "Pass strategy",
provides only one fixing condition to obtain a more accurate value
on a specific critical CD.
Example Multiple Model Embodiments
[0033] Certain embodiments of the present invention expand the pass
strategy by using multiple models for multiple critical parameter
measurement accuracy. The different models will generally fix
different sets of one or more parameters, while varying one or more
other parameters. Each model can be configured to determine one or
more parameter values that result in a best match between the
model-determined spectra and spectra that is measured from an
unknown target structure. Different models can be selected to
obtain optimum accuracy for determined parameter results under
different conditions. Parameter values that are output by such set
of differently configured models will tend to correlate well with
reference data, which includes known feature parameter values for
reference structures.
[0034] Different models can generally have different degrees of
freedom with respect to varying/floating or fixing specific model
critical parameters. Some models may have a DOF (e.g., 10 or 15
DOF) and can report many different CPs, but not easily determine
all CPs accurately since there is likely to be high correlation
between parameters. Another model may have low DOF and cannot
report many CPs, but can provide accurate solutions for a subset of
CPs. A low DOF model may show better parameter in-wafer variation
due to its low parameter correlation. Finally, multiple low DOF
models can be utilized to obtain better solutions for many CPs,
while providing improved monitoring of in-wafer variation.
[0035] FIG. 4 is a diagrammatic representation of a multiple model
system 400 in accordance with one embodiment of the present
invention. As shown, the system may utilize any number and type of
models, such as model1 404, model2 406, and mode13 408. Each model
can include a same geometric model with one or more different
constraint conditions or different geometric models. For instance,
model1 404 may include sub-model1_1 through sub-model1_i.
[0036] A particular server or metrology tool 402 may selectively
utilize these models for different types of critical parameter
measurements. That is, different models and/or sub-models will
provide output that correlates better to different critical
parameter measurements, For example, critical parameter CP1 is more
accurately determined by Model1_1; CP2 and CP3 are more accurately
determined with sub-model2_1 and model 2_2, respectively; and CP4
is more accurately reported by model3_1. Data may optionally be
transferred between models. For example, SWA, not necessarily a CP,
determined from Model 1 could be transferred to Model2 for the
purpose of the better accuracy in MCD (CP2) and FIT (CP3) by
breaking correlation in Model2. In an alternative embodiment, one
or more CPs may be fed from one model to another so as to obtain
better CP results. FIG. 5 is a diagrammatic representation of a
multiple model system 500 in accordance with an alternative
implementation of the present invention. This implementation
provides the flexibility of providing an arbitrary transfer
function f for feeding one or more parameters from one model to
another. (e.g., M2_3=a*M1_3+b), In this case, M1_3 from Model1 is
fed to M2_3 in model 2 by a simple linear transfer function. In
similar, the function F(M) can be an arbitrary transform function
for feeding a particular parameter from one model to the final
result in a server. In the illustrated example, CP3 is obtained by
a combination of outputs transferred from model2 with the transfer
function F2, and CP4 is obtained from a combination of outputs from
model3 with different transfer function F3.
[0037] FIG. 6 is a flow chart illustrating a multi-model setup
process 600 for determining a set of models for determining
critical parameters in accordance with one embodiment of the
present invention. Initially, reference data for targets structures
having known critical parameter values and known spectra are
obtained in operation 602. For instance, the reference data may
have been obtained by a metrology optical tool collecting spectra
from targets having varying profiles, e.g., different MCD and WA,
etc. In a specific example, varying critical parameter values for
different targets are obtained by varying process conditions across
a design of experiment (DOE) wafer. To determine dimensional
parameters (such as profile characteristics (bottom or top CD,
sidewall angle, etc.) for the reference or training targets, these
targets may be characterized by any suitable reference metrology,
e.g. cross-section TEM, atomic force microscopy (AFM), or
CD.about.SEM. The reference data may then be provided in the form
of spectra and matching critical parameter values.
[0038] The spectra acquired from various targets may include any
suitable metrology signal that can be correlated with one or more
critical parameters. Example spectra signals include, but are not
limited to, any type of scatterometry, spectroscopic, ellipsometry,
and/or reflectometry signals, including: .PSI., .DELTA., Rs
(complex reflectivity of the s polarization), Rp (complex
reflectivity of the p polarization), Rs (|r.sub.s|.sup.2), Rp
(|r.sub.p|.sup.2), R (unpolarized reflectivity), .alpha.
(spectroscopic "alpha" signal), .beta. (spectroscopic "beta"
signal), and functions of these parameters, such as tan(.PSI.),
cos(.DELTA.), ((Rs-Rp)/(Rs+Rp)), Mueller matrix elements
(M.sub.ij), etc. The signals could alternatively or additionally be
measured as a function of incidence angle, polarization, azimuthal
angle, angular distribution, phase, or wavelength or a combination
of more than one of these parameters. The signals could also be a
characterization of a combination of signals, such as an average
value of a plurality of any of the above described ellipsometry
and/or reflectometry signal types. Other embodiments may use
monochromatic or laser light sources where at least one of the
signals may be obtained at a single wavelength, instead of multiple
wavelengths. The illumination wavelengths could be any range,
starting from X-ray wavelengths and going to far infra-red
wavelengths. The type of acquired signals may be selected based on
signal sensitivity to the structure of interest. For instance,
certain wavelengths may be more sensitive to certain particular
structure dimensions.
[0039] Referring back to FIG. 6, a plurality of models may then be
generated in operation 604. These models have varying combinations
of floating and fixed critical parameters. Each model will
generally represent complex profile shapes formed from different
materials, but a same underlying structure. The model also
simulates the scattering and output spectra with respect to each
different floating parameter change, as well as fixed parameters.
Example model generation techniques can include an EM
(electro-magnetic) solver and use such algorithms as RCWA (rigorous
coupled wave analysis), FEM (finite element method), method of
moments, surface integral method, volume integral method, FDTD
(finite difference time domain), etc. One example RCWA software is
AcuShape available from KLA-Tencor of Milpitas, Calif.
[0040] It may then be determined which model output correlates best
with each critical parameter based on the reference data in
operation 606. For instance, it may be determined which model
output correlates best with each critical parameter with respect to
the reference data. More specifically, the reference data will
include different spectra for varying values of a particular
parameter, such as height. The optimum model outputs spectra for
such particular parameter values (height) that best correlate to
the reference spectra for such particular parameter values
(height). This process is repeated for each particular parameter
type, e.g., MCD, TCD, BCD, etc. In one example, a first model will
correlate better for height than a second model, while the second
model correlates better for MCD than the first model. FIG. 7A
illustrates two different models' correlation for height with
respect to reference data. Line 702 represents perfect correlation
between reference height and model height. In the current example,
model1's height correlation 704a with reference height is better
than model2's correlation 704b. Similarly, FIG. 7B illustrates two
different models' correlation for MCD with respect to reference
data. As shown, model2's MCD correlation 708b with reference MCD is
better than model1's correlation (708a).
[0041] Based on these different correlations, different models may
then be set up for determining different critical parameters during
metrology in operation 608. in the current example, the first model
is selected for determining height, while the second model is
selected for determining MCD. The multiple model setup procedure
600 may then end. Of course, setup procedure 600 may be executed
again, for example, when a process changes.
[0042] Each model may include any suitable type and number of
parameters. Example parameters include MCD, TCD, BCD, HT and SWA
for the resist structure, composition of layer, layer roughness,
etc. One or more parameters may float or have varying values, while
other parameter may remain fixed. For instance, MCD may be set to a
particular value or be set to equal the floating parameter TCD
value.+-.an offset value e.g., MCD=TCD+2.
[0043] In another embodiment, one or more conditions may be set up
during metrology for model selection. FIG. 8 is an example
metrology flow for selecting a sub-model based on a condition in
accordance with a specific implementation of the present
embodiment. Initially, a first model1 802 may be selected and
executed for determining one or more critical parameters. In the
illustrated example, model1 802 has two sub-models for determining
critical parameters CP1, CP2, CP3, and CP4 (e.g., 810 and 812). The
different sub-models (e.g., model1a and model1b) may include
different combinations of fixed and floating parameters, including
floating parameters CP1.about.CP4,
[0044] It may then be determined whether a condition has been met
in operation 804. Any suitable condition may be used to determine
which model is best as described further below. If the condition
has been met, a sub-model1a may be used in operation 806 to
determine parameters CP1.about.CP4. Otherwise, sub-model1b may be
used in operation 808 to determine such parameters.
[0045] FIG. 9 is a second example metrology flow for selecting a
single model or a multi-model based on a condition in accordance
with another embodiment of the present embodiment. In this example,
model1 is selected and executed for determining parameters
CP1.about.CP4. It is then determined whether a condition is met in
operation 904. if the condition is met, the selected model1 may be
used in operation 906 to determine critical parameters
CP1.about.CP4 results 910. Otherwise, sub-model2a may be used in
operation 908a. and sub-model2b used in operation 908b to determine
a first set of the parameters CP1 and CP2 (912a) and a second set
of parameters CP3 and CP4 (912b), respectively.
[0046] FIG. 10 is a third example metrology flow for selecting a
sub-model based on a condition in accordance with another
embodiment of the present embodiment. In this example, model1 may
initially be executed with all its parameters floating in operation
1002. It may then be determined whether a particular condition is
met in operation 1004. If the condition is met, then a first
sub-model1a is used with parameters CP1 and CP2 being fixed in
operation 1006. Otherwise, sub-model1b is used with parameters CP1,
CP2, and CP3 being fixed in operation 1008.
[0047] Any suitable number and type of conditions may be used to
determine which model or sub-model to select for the above
processes. In one example, a condition may be a fitting quality
threshold being reached. For instance, a model's spectra output may
be required to fit the real spectra within a predetermined amount
of fitness as a condition. Various goodness-of-fit statistics may
be used. Examples include sum of squares due to error (SSE),
R-square, adjusted R-square, root mean squared error (RMSE),
normalized GOF (NGOF), etc. A residual analysis or set of
confidence and prediction bounds may alternatively or additionally
be used to evaluate a goodness-of-fit quality condition.
[0048] In another condition example, it may be determined whether a
particular CP of a model is within a predetermined range. For
instance, a particular model may be generated for larger CD values,
while a second model is generated for a smaller range of CD
values.
[0049] Different models can be used for different metrology tool
subsystems. That is, different metrology modules may have a
different associated model or sub-model. For instance, an SE 0 and
90 degree azimuth subsystem may use a first model; an SE and eUVR
subsystem may use a second model; and a SE and BPR subsystem may
use a third model. Each model can have its own state mechanism for
setting and controlling its state, thus, allowing multiple types of
operations (different sub-models) within a single model. in
general, each model may be a multi-model having a plurality of
sub-models for determining different sets of CPs or using different
algorithms to determine the same set of CPs. For instance, a model
may utilize a recursive multi-model structure.
[0050] Certain embodiments of the present invention include
apparatus and methods for determining critical parameters of
structures of interest on a semiconductor wafer by the use of
multiple models, which are associated with improved CP
measurements. A conventional model may provide improvements for a
particular CP, but have degradation of other CPs. Using multiple
models allow improvement to all CPs. These techniques allow a more
accurate means of measuring CP variation across an entire wafer
based on analyzing the spectra signals using differently configured
models. In certain embodiments, these techniques are applicable to
determine CP of lines, trenches, resist or directed self-assembly
(DSA) structures, film, periodic and aperiodic structures, etc.
[0051] FIG. 11 illustrates improved critical parameter correlation
by use of multiple models in accordance with one embodiment of the
present invention. Correlation between model results for height
(HT), SWA, BCD, MCD, and TCD and reference data is shown for three
different models M1, M2, and M3. Table 1102 shows the correlation
for model M1. Table 1104 shows correlation for model M2. Table 1106
shows correlation for model M3. Different models result in
different levels of correlation for different CPs. For instance,
model M1 has the highest correlation for MCD, while model M2 has
the highest correlation for BCD. The best results from all of the
models may be reported. As shown, table 1108 reports the best
correlated results for each CP from the different models. Thus, the
reported CP results are together more highly correlated to the
reference data than the individual models M1.about.M3.
[0052] Any suitable combination of hardware and/or software may be
used to implement any of the above described techniques. In a
general example, a metrology tool may comprise an illumination
system which illuminates a target, a collection system which
captures relevant information provided by the illumination system's
interaction (or lack thereof) with a target, device or feature, and
a processing system which analyzes the information collected using
one or more algorithms. Metrology tools can generally be used to
measure various radiation signals pertaining to structural and
material characteristics (e.g., material composition, dimensional
characteristics of structures and films such as film thickness
and/or critical dimensions of structures, overlay, etc.) associated
with various semiconductor fabrication processes. These
measurements can be used to facilitate process controls and/or
yield efficiencies in the manufacture of semiconductor dies.
[0053] The metrology tool can comprise one or more hardware
configurations which may be used in conjunction with certain
embodiments of this invention. Examples of such hardware
configurations include, but are not limited to, the following:
Spectroscopic ellipsometer (SE), SE with multiple angles of
illumination, SE measuring Mueller matrix elements (e.g. using
rotating compensator(s)), single-wavelength ellipsometers, beam
profile ellipsometer (angle-resolved ellipsometer), beam profile
reflectometer (angle-resolved reflectometer), broadband reflective
spectrometer (spectroscopic reflectometer), single-wavelength
reflectometer, angle-resolved reflectometer, imaging system, and
scatterometer (e.g. speckle analyzer)
[0054] The hardware configurations can be separated into discrete
operational systems. On the other hand, one or more hardware
configurations can be combined into a single tool. One example of
such a combination of multiple hardware configurations into a
single tool is further illustrated and described U.S. Pat. No.
7,933,026, which patent is herein incorporated by reference in its
entirety for all purposes. FIG. 12 shows, for example, a schematic
of an exemplary metrology tool that comprises: a) a broadband SE
(e.g., 18); b) an SE (e.g., 2) with rotating compensator (e.g.,
98); c) a beam profile ellipsometer (e.g., 10); d) a beam profile
reflectometer (e.g., 12); e) a broadband reflective spectrometer
(e.g., 14); and f) a deep ultra-violet reflective spectrometer
(e.g., 16). In addition, there are typically numerous optical
elements (e.g., 92, 72, 94, 70, 96, 74, 76, 80, 78, 98, 100, 102,
104, 32/33, 42, 84, 60, 62, 64, 66, 30, 82, 29, 28, 44, 50, 52, 54,
56, 46, 34, 36, 38, 40, and 86) in such systems, including certain
lenses, collimators, mirrors, quarter-wave plates, polarizers,
detectors, cameras, apertures, and/or light sources. The
wavelengths for the optical systems can vary from about 120 nm to 3
microns. The azimuth angle for the optical systems can also vary.
For non-ellipsometer systems, signals collected can be
polarization-resolved or unpolarized.
[0055] FIG. 12 provides an illustration of multiple metrology heads
integrated on the same tool. However, in many cases, multiple
metrology tools are used for measurements on a single or multiple
metrology targets. Several embodiments of multiple tool metrology
are further described, e.g., in U.S. Pat. No. 7,478,019 by Zangooie
et al, entitled "Multiple tool and structure analysis", which
patent is incorporated herein by reference in its entirety for all
purposes.
[0056] The illumination system of certain hardware configurations
may include one or more light sources. The one or more light
sources may generate light having only one wavelength (e.g.,
monochromatic light), light having a number of discrete wavelengths
(e.g., polychromatic light), light having multiple wavelengths
(e.g., broadband light), and/or light that sweeps through
wavelengths, either continuously or hopping between wavelengths
(e.g., tunable sources or swept sources). Examples of suitable
light sources are: a white light source, an ultraviolet (UV) laser,
an arc lamp or an electrode-less lamp, a laser sustained plasma
(LSP) source, for example, those commercially available from
Energetiq Technology, Inc. of Woburn. Mass., a supercontinuum
source (such as a broadband laser source) such as those
commercially available from NKT Photonics Inc. of Morganville,
N.J., or shorter-wavelength sources such as x-ray sources, extreme
UV sources, or some combination thereof. The light source(s) may
also be configured to provide light having sufficient brightness,
which in some cases may be a brightness greater than about 1 W/(nm
cm2 Sr). The metrology system may also include a fast feedback to
the light source for stabilizing its power and wavelength. Output
of the light source can be delivered via free-space propagation, or
in some cases delivered via optical fiber or light guide of any
type.
[0057] In turn, one or more detectors or spectrometers are
configured to receive via a collection optical elements
illumination reflected or otherwise scattered from the surface of
the specimen 4. Suitable sensors include charged coupled devices
(CCD), CCD arrays, time delay integration (TDI) sensors, TDI sensor
arrays, photomultiplier tubes (PMT), and other sensors. Measured
spectra or detected signal data (as a function of position,
wavelength, polarization, azimuth angle, etc.) may be passed from
each detector to the processor system 48 for analysis.
[0058] It should be recognized that the various steps described
throughout the present disclosure may be carried out by a single
processor system 48 or, alternatively, a multiple processor system
48. Moreover, different subsystems of the system of FIG. 12, such
as the spectroscopic ellipsometer, may include a computer system
suitable for carrying out at least a portion of the steps described
herein. Therefore, the aforementioned description should not be
interpreted as a limitation on the present invention but merely an
illustration. Further, the one or more processor system 48 may be
configured to perform any other step(s) of any of the method
embodiments described herein.
[0059] In addition, the processor system 48 may be communicatively
coupled to a detector system in any manner known in the art. For
example, the one or more processor system 48 may be coupled to
computing systems associated with the detector system. In another
example, the detector system may be controlled directly by a single
computer system coupled to processor system 48.
[0060] The processor system 48 of the metrology system may be
configured to receive and/or acquire data or information from the
subsystems of the system by a transmission medium that may include
wireline and/or wireless portions. In this manner, the transmission
medium may serve as a data link between the processor system 48 and
other subsystems of the system of FIG. 12.
[0061] Processor system 48 of the integrated metrology system may
be configured to receive and/or acquire data or information (e.g.,
measurement spectra, difference signals, statistical results,
reference or calibration data, training data, models, extracted
features or transformation results, transformed datasets, curve
fittings, qualitative and quantitative results, etc.) from other
systems by a transmission medium that may include wireline and/or
wireless portions. In this manner, the transmission medium may
serve as a data link between the processor system 48 and other
systems (e.g., memory on-board metrology system, external memory,
reference measurement source, or other external systems). For
example, processor system 48 may be configured to receive
measurement data from a storage medium (e.g., internal or external
memory) via a data link. For instance, spectral results obtained
using the detection system may be stored in a permanent or
semipermanent memory device (e.g., internal or external memory). In
this regard, the spectral results may be imported from on-board
memory or from an external memory system. Moreover, the processor
system 48 may send data to other systems via a transmission medium.
For instance, qualitative and/or quantitative results determined by
processor system 48 may be communicated and stored in an external
memory. In this regard, measurement results may be exported to
another system.
[0062] Processor system 48 may include, but is not limited to, a
personal computer system, mainframe computer system, workstation,
image computer, parallel processor, or any other device known in
the art, In general, the term "processor system" may be broadly
defined to encompass any device having one or more processors,
which execute instructions from a memory medium. Program
instructions implementing methods such as those described herein
may be transmitted over a transmission medium such as a wire,
cable, or wireless transmission link. Program instructions may be
stored in a computer readable medium (e.g., memory). Exemplary
computer-readable media include read-only memory, a random access
memory, a magnetic or optical disk, or a magnetic tape.
[0063] The metrology tool may be designed to make many different
types of measurements related to semiconductor manufacturing.
Certain embodiments of the invention for determining quality and/or
quantitative values may utilize such measurements. In certain
embodiments, the tool may measure spectra and determine
characteristics of one or more targets, such as quality and defect
quantity values, critical dimensions, overlay, sidewall angles,
film thicknesses, process-related parameters (e.g., focus and/or
dose). The targets can include certain regions of interest that are
periodic in nature, such as for example gratings in a memory die.
Targets can include multiple layers (or films) whose thicknesses
can be measured by the metrology tool. Targets can include target
designs placed (or already existing) on the semiconductor wafer for
use, e.g., with alignment and/or overlay registration operations.
Certain targets can be located at various places on the
semiconductor wafer. For example, targets can be located within the
scribe lines (e.g., between dies) and/or located in the die itself.
In certain embodiments, multiple targets are measured (at the same
time or at differing times) by the same or multiple metrology tools
as described in U.S. Pat. No. 7,478,019. The data from such
measurements may be combined. Data from the metrology tool may be
used in the semiconductor manufacturing process, for example, to
feed-forward, feed-backward and/or feed-sideways corrections to the
process (e.g. lithography, etch) and therefore, might yield a
complete process control solution.
[0064] As semiconductor device pattern dimensions continue to
shrink, smaller metrology targets are often required. Furthermore,
the measurement accuracy and matching to actual device
characteristics increase the need for device-like targets as well
as in-die and even on-device measurements. Various metrology
implementations have been proposed to achieve that goal. For
example, focused beam ellipsometry based on primarily reflective
optics is one of them and described in the patent by Piwonka-Corle
et al. (U.S. Pat. No. 5,608,526, "Focused beam spectroscopic
ellipsometry method and system"). Apodizers can be used to mitigate
the effects of optical diffraction causing the spread of the
illumination spot beyond the size defined by geometric optics. The
use of apodizers is described in the patent by Norton, U.S. Pat.
No. 5,859,424, "Apodizing filter system useful for reducing spot
size in optical measurements and other applications." The use of
high-numerical-aperture tools with simultaneous multiple
angle-of-incidence illumination is another way to achieve
small-target capability. This technique is described, e.g. in the
patent by Opsal et al, U.S. Pat. No. 6,429,943, "Critical dimension
analysis with simultaneous multiple angle of incidence
measurements."
[0065] Other measurement examples may include measuring the
composition of one or more layers of the semiconductor stack,
measuring certain defects on (or within) the wafer, and measuring
the amount of photolithographic radiation exposed to the wafer. In
some cases, metrology tool and algorithm may be configured for
measuring non-periodic targets, see e.g. "The Finite Element Method
for Full Wave Electromagnetic Simulations in CD Metrology Using
Scatterometry" by P. Jiang et al (pending U.S. Ser. No. 61/830,536,
K-T disclosure P4063) or "Method of electromagnetic modeling of
finite structures and finite illumination for metrology and
inspection" by A. Kuznetsov et al. (pending U.S. Ser. No.
61/761,146 or KT disclosure P4082).
[0066] Measurement of parameters of interest can also involve a
number of algorithms. For example, optical interaction of the
incident beam with the sample can be modeled using EM
(electro-magnetic) solver and uses such algorithms as RCWA, FEM,
method of moments, surface integral method, volume integral method,
FDTD, and others. The target of interest can usually be modeled
(parameterized) using a geometric engine, or in some cases, process
modeling engine or a combination of both. The use of process
modeling is described in "Method for integrated use of model-based
metrology and a process model," by A. Kuznetsov et al. (pending
U.S. Ser. No. 61/738,760, P4025). A geometric engine may be
implemented, for example, in AcuShape software product of
KLA-Tencor of Milpitas, Calif.
[0067] Collected data can be analyzed by a number of data fitting
and optimization techniques an technologies including libraries,
Fast-reduced-order models; regression; machine-learning algorithms
such as neural networks, support-vector machines (SVM);
dimensionality-reduction algorithms such as, e.g., PCA (principal
component analysis), ICA (independent component analysis), LLE
(local-linear embedding); sparse representation such as Fourier or
wavelet transform; Kalman filter; algorithms to promote matching
from same or different tool types, and others.
[0068] Collected data can also be analyzed by algorithms that do
not include modeling, optimization and/or fitting e.g. provisional
patent application Ser. No. 61/745,981, which is incorporated
herein by reference, and as described herein.
[0069] Computational algorithms are usually optimized for metrology
applications with one or more approaches being used such as design
and implementation of computational hardware, parallelization,
distribution of computation, load-balancing, multi-service support,
dynamic load optimization, etc. Different implementations of
algorithms can be done in firmware, software, FPGA, programmable
optics components, etc.
[0070] The data analysis and fitting steps may be used to pursue
one of the following goals: measurement of quality, defect number,
CD, SWA, shape, stress, composition, films, bandgap, electrical
properties, focus/dose, overlay, generating process parameters
(e.g., resist state, partial pressure, temperature, focusing
model), and/or any combination thereof; modeling and/or design of
metrology systems; and modeling, design, and/or optimization of
metrology targets.
[0071] Certain embodiments of the present invention presented here
generally address the field of semiconductor metrology and process
control, and are not limited to the hardware, algorithm/software
implementations and architectures, and use cases summarized
above.
[0072] Although the foregoing invention has been described in some
detail for purposes of clarity of understanding, it will be
apparent that certain changes and modifications may be practiced
within the scope of the appended claims. It should be noted that
there are many alternative ways of implementing the processes,
systems, and apparatus of the present invention. Accordingly, the
present embodiments are to be considered as illustrative and not
restrictive, and the invention is not to be limited to the details
given herein.
* * * * *