DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed 03/02/2026 have been fully considered are addressed below:
Applicant’s amendments overcome the previous objections to claim 1, 13, and 18. However, new objections have been made below.
Applicant's arguments filed 03/02/2026 regarding the 103 rejections have been fully considered but they are not persuasive.
Applicant argues that the cited prior art does not teach the newly amended limitations. Specifically, the applicant argues Bringoltz is silent about performing the combining and regressing until a convergence condition is satisfied. Furthermore, Bringoltz does not teach applying this technique to thickness and composition of different optical measurements as recited in amended claims 1, 13, and 18 (see remarks page 8).
The examiner respectfully disagrees that the cited prior art does not teach "combining the thickness and the composition in the first optical measurements and the thickness and the composition in the second optical measurements to form combined measured data, wherein the combining includes regressing the first optical measurements and the second optical measurements, wherein the combining and the regressing occur until a convergence condition is satisfied". Bringoltz was relied upon to teach the use of a physical model with a transfer matrix instead of a machine learning model. Pandev was relied upon to teach wherein the combining includes regressing the first optical measurements and the second optical measurements.
Pandev further addresses the newly amended limitation:
In operation 314 raw data is collected from product (i.e. target) wafers utilizing the metrology tools. Then in operation 316, the previously trained transformation is then applied to the raw data to extract parameters of the product wafers (i.e. to measure the desired parametric values). Further, in operation 318 the parametric values measured for the product wafers are sent to models of one or more other metrology tools for use in performing regression. For example, these models may be regressed in a well known manner using the measured parametric values. The parameters are determined by applying techniques described in U.S. patent application Ser. No. 14/223,045 filed Mar. 24, 2014 and U.S. patent application Ser. No. 14/252,323 filed Apr. 14, 2014. (Pandev [0064])
Pandev cites US20140316730A1 Shchegrov et al (U.S. patent application Ser. No. 14/252,323) which further teaches that the combining and the regressing occur until a convergence condition is satisfied ([0044] "The floating parameters are resolved by a fitting process (e.g., regression, library matching, etc.) that produces the best fit between theoretical predictions and measured data. The unknown specimen parameters, Pspecimen, are varied and the model output values are calculated until a set of specimen parameter values are determined that results in a close match between the model output values and the measured values."; this describes regressing until a convergence condition is satisfied).
As for the applicant's argument regarding the criticality of the physical model (see remarks page 2), the examiner points out the rejection relied upon Bringoltz to teach the physical model, and the statement regarding the criticality was provided as further evidence that the models are functionally equivalent and interchangeable.
Further, Shchegrov can be relied upon to teach wherein the combining and the determining use a physical model that includes a transfer matrix ([0042]-[0042] The target-specific model includes a parameterization of the structure in terms of the physical properties of the measurement target of interest; thus a physical model; [0091] Mueller matrix), and wherein the physical model is configured for both the first optical measurements and the second optical measurements ([0091] "measurement data collected by different measurement technologies and analyzed in accordance with the methods described herein may be collected from multiple tools"; [0110]).
Additionally, the applicant's specification states "a theoretical model for Raman scattering and SE may use the theory of light propagation and scattering that can be described by using transfer matrix method." ([0062]). Shchegrov and Pandev both use Mueller matrix ellipsometry (Pandev [0021]; Shchegrov [0091]) A Mueller matrix is a 16-element, 4x4 transfer matrix that fully describes how an object will change the polarization state of a beam of light upon interaction (see Axometrics "Overview" cited in the conclusion).
Further, the examiner notes that additional references are provided in the conclusion regarding the processing of regressing until a convergence condition is satisfied.
Claims 1, 2, 5, 7-10, 13, 14, 16-18, 21-23, and 26-36 are now rejected in view of Pandev, Arnold, Chouaib, Bringoltz, and Shchegrov.
Claim Objections
Claims 1, 13, and 18 are objected to because of the following informalities:
Regarding claims 1, 13, and 18, in the third indentation, the newly amended limitation "the thickness and the compositions in the second optical measurements" should read "the thickness and the composition in the second optical measurements" to correct “composition” being plural since the rest of the limitations in these claims refer to “composition” as singular.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 2, 5, 7-10, 13, 14, 16-18, 21-23, and 26-36 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1, 13, and 18, the claims each similarly recite in the last paragraph “determining the thickness of the film stack and the composition of the film stack using the combined measured data”. With the addition of the newly amended limitations of “the thickness and the composition in the first optical measurements and the thickness and the composition in the second optical measurements”, it is now unclear if “the thickness of the film stack and the composition of the film stack” is the same value that was measured in the first two steps, or if it’s a new determined value. From the claim alone, it doesn’t make sense how the same thickness/composition is measured twice and combined to then also determine the same thickness/composition. It appears that a new value for thickness or composition of the film stack is determined in the last step using the combined measured data. In this case, the claim would be clearer if the final thickness/composition values were introduced as new values. For the purposes of examination, the first two steps are measuring a first and second thickness/composition and then a third thickness/composition is determined in the final step. The examiner notes that the above examples are simply interpretations, and that the claims, including the dependent claims, should be amended with support from the specification. Appropriate correction is required.
Claims 2, 5, 7-10, 14, 16-14, 21-23, and 26-36 are rejected based on their dependencies.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 5, 7-10, 13, 14, 16-18, 21-23, and 26-36 are rejected under 35 U.S.C. 103 as being unpatentable over US20160141193A1 by Pandev (previously cited) in view of US20210231500A1 by Arnold (previously cited) in further view of US20200200525A1 by Chouaib (previously cited) and US20230023634A1 by Bringoltz (previously cited) and US20140316730A1 Shchegrov et al (newly cited; referenced as U.S. patent application Ser. No. 14/252,323 in Pandev)
Regarding claim 1, Pandev teaches a method comprising (at least Figure 2):
measuring a thickness of a film stack of a workpiece and a composition of the film stack ([0017]; [0032]; [0034]; [0046]; [0052]) using spectroscopic ellipsometry (SE), spectroscopic reflectometry (SR), and/or angle resolved reflectometry (ARR) in a system ([0019]; [0023]; [0052]) thereby generating first optical measurements ([0050] operation 206, first set of signals);
measuring the thickness of a film stack and the composition of the film stack ([0017]; [0032]; [0034]; [0046]; [0052]) using Raman spectroscopy in the system ([0052] "Raman spectroscopy device"; [0031] light source may have multiple wavelengths) thereby generating second optical measurements ([0050] operation 208, second set of signals);
combining the thickness and the composition in the first optical measurements and the thickness and the compositions in the second optical measurements to form combined measured data ([0045] " raw data resulting therefrom is combined"; [0054] operation 210, third set of signals made by combining the first and second set of signals; [0052] this raw data contains the thickness and composition measurements from above), wherein the combining includes regressing the first optical measurements and the second optical measurements ([0054] "resulting combined data set may be transformed to the third set of signals, optionally with a smaller number of parameters/columns", Principle component analysis is regression) and
and determining the thickness of the film stack and the composition of the film stack using the combined measured data ([0058] operation 218 measures parametric values; [0046] parameters include thickness and composition); wherein the combining and the determining use a model that includes a transfer matrix ([0054] Principal component analysis (PCA) can be used for creating a model that transforms the combined data to the third set of signals or principal components; [0052] a Mueller matrix is a transfer matrix), and wherein the model is configured for both the first optical measurements and the second optical measurements ([0058] signals from a target component are collected, utilizing at least the first metrology tool and the second metrology tool, model is applied to the signals collected from the target component to measure parametric values for the target component; [0061]; [0064] these models may be regressed in a well known manner using the measured parametric values).
Pandev is silent as to using multiwavelength Raman spectroscopy.
However, Arnold does address this limitation. Pandev and Arnold are considered to be analogous to the present invention as they are in the same field of spectroscopy.
Arnold teaches multiwavelength Raman spectroscopy ([0083] multi-wavelength Raman spectroscopic system; [0087]).
It would have been well known to someone of ordinary skill in the art before the effective filing date of the claimed invention to use multiwavelength Raman to collect a Raman spectrum at multiple excitation wavelengths. Therefore, it would have been obvious to modify Pandev to incorporate multiwavelength Raman spectroscopy in order to mitigate the difficulties from trying to balance which regions of the spectrum to collect versus the information sought (Arnold [0083]).
Further, Pandev is silent as to wherein the first optical measurements and the second optical measurements are performed at least partly simultaneously.
However, Chouaib does address this limitation. Pandev, Arnold, and Chouaib are considered to be analogous to the present invention as they are in the same field of spectroscopy.
Chouaib teaches wherein the first optical measurements and the second optical measurements are performed at least partly simultaneously ([0148]-[0149] “specimen includes a single site having one or more measurement targets whose simultaneous, combined measurement is treated as a single specimen measurement or reference measurement”).
It would have been well known to someone of ordinary skill in the art before the effective filing date of the claimed invention to perform simultaneous or partly simultaneous measurements of a target. Therefore, it would have been obvious to modify Pandev in view of Arnold to be configured such that the first optical measurements and the second optical measurements are performed at least partly simultaneously as suggested by Chouaib as a simultaneous measurement reduces measurement time and thus increases efficiency.
Further, even if Pandev does not explicitly teach wherein the combining and the regressing occur until a convergence condition is satisfied, Pandev teaches the parameters are determined by applying techniques described in Shchegrov (Pandev [0064] Ser. No. 14/252,323).
Shchegrov does address this limitation. Shchegrov and Pandev are considered to be analogous to the present invention as they are in the same field of metrology.
Shchegrov teaches wherein the combining and the regressing occur until a convergence condition is satisfied ([0044] "The floating parameters are resolved by a fitting process (e.g., regression, library matching, etc.) that produces the best fit between theoretical predictions and measured data. The unknown specimen parameters, Pspecimen, are varied and the model output values are calculated until a set of specimen parameter values are determined that results in a close match between the model output values and the measured values."; a close match indicates that convergence is satisfied).
It would have been well known to someone of ordinary skill in the art before the effective filing date of the claimed invention to perform regression until convergence. Therefore, it would have been obvious to modify Pandev to explicitly include wherein the combining and the regressing occur until a convergence condition is satisfied as suggested by Shchegrov in order to achieve the most accurate results.
Further, even if Pandev does not explicitly teach wherein the combining and the determining use a physical model that includes a transfer matrix, and wherein the physical model is configured for both the first optical measurements and the second optical measurements, Pandex does teach measurement of parameters of interest usually involves a number of algorithms. For example, optical interaction of the incident beam with the sample is 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 is usually modeled (parameterized) using a geometric engine, or in some cases, process modeling engine or a combination of both ([0035]). The examiner also notes every mention of a physical model in the applicant’s specification indicates that it’s functionally equivalent to using a machine learning model and that either can be used (applicant’s specification [0019]).
Additionally, Shchegrov (which Pandev references) teaches model-based semiconductor metrology consists of formulating a metrology model that attempts to predict the measured optical signals based on a model of the interaction of the measurement target with the particular metrology system. The target-specific model includes a parameterization of the structure in terms of the physical properties of the measurement target of interest ([0042]-[0043]).
Further, Bringoltz does address this limitation. Bringoltz and Pandev are considered to be analogous to the present invention as they are in the same field of optical metrology.
Bringoltz teaches using a physical model ([0004] optical model is a physical model; [0011] calculating multiple sets of model pattern parameters by applying the optical model) for SE and Raman measurements ([0007] Exemplary scatterometric tools for measuring (acquiring) scatterometry data (e.g., spectrograms) may include spectral ellipsometers (SE)… Additional methods for measuring critical dimensions X-ray Raman spectrometry.) Bringoltz also teaches that the optical models are designed according to physical laws ([0010]) and the examiner notes that a transfer matrix in a physical model is an expression of the physical or optical laws of the system.
It would have been well known to someone of ordinary skill in the art before the effective filing date of the claimed invention to use a physical model, with a transfer matrix such as a Mueller matrix, to combine and determine values from provided data. Therefore, it would have been obvious to modify Pandev in view of Arnold and Chouaib to use a physical model instead of a machine learning model such that the combining and the determining use a physical model that includes a transfer matrix, and wherein physical model is configured for both the first optical measurements and the second optical measurement as suggested by Bringoltz as the direct association between physical parameters and theoretical optical properties means that optical model results are typically more easily interpretable than machine learning results (Bringoltz [0009]).
Regarding claim 2, Pandev modified by Arnold, Chouaib, Shchegrov, and Bringoltz teach the method of claim 1, and Pandev further teaches measuring a stress of the film stack ([0040] "stress"), but is silent as to measuring a strain of the film stack.
However, Chouaib does address this limitation.
Chouaib teaches measuring a strain of the film stack ([0104] "state of strain" [0105] methods include SE, SR, Raman, and x-ray techniques).
It would have been well known to someone of ordinary skill in the art before the effective filing date of the claimed invention to measure strain as part of standard materials testing. Therefore, it would have been obvious to modify Pandev to incorporate measuring a strain of the film stack in order to provide early estimation of the various electrical characteristics (Chouaib [0017]).
Regarding claim 5, Pandev modified Arnold, Chouaib, Shchegrov, and Bringoltz teach the method of claim 1, and Pandev further teaches wherein the combining further includes combining reference measurements with the first optical measurements and the second optical measurements ([0055] operation 212 "corresponding relationship between the third set of signals and the reference values is determined" where the third set of values are made from the first and second measurements).
Regarding claim 7, Pandev modified by Arnold, Chouaib, Shchegrov, and Bringoltz teach the method of claim 1, and Pandev further teaches wherein the combining and the determining further use a machine-learning algorithm ([0036]; [0049], [0056] "machine learning model").
Regarding claim 8, Pandev modified by Arnold, Chouaib, Shchegrov, and Bringoltz teach the method of claim 1, and Pandev further teaches wherein the film stack is a Si/SiGe film stack, a Si/SiC film stack, or a Si/GaN film stack ([0060] "silicon germanium composition").
Regarding claim 9, Pandev modified by Arnold, Chouaib, Shchegrov, and Bringoltz teach the method of claim 1, and Pandev further teaches wherein the film stack is used to fabricate one of a GAA FET, FinFET, ForkFET, CFET, or 2D structure ([0060] " finFET devices").
Regarding claim 10, Pandev modified by Arnold, Chouaib, Shchegrov, and Bringoltz teach the method of claim 1, and Pandev further teaches wherein the first optical measurements and the second optical measurements further include strain, stress, and/or defects ([0034] "measuring certain defects on (or within) the wafer"; [0040] "stress").
Regarding claim 26, Pandev modified by Arnold, Chouaib, Shchegrov, and Bringoltz teach the method of claim 1, and Pandev further teaches comprising measuring the thickness of the film stack and the composition of the film stack using an x-ray technique thereby generating reference measurements, wherein the x-ray technique is x-ray diffraction (XRD), x-ray reflectometry (XRR), or a soft x-ray technique ([0052] XRD, XRR are available as metrology tools; [0048] operation 204).
Regarding claim 27, Pandev modified by Arnold, Chouaib, Shchegrov, and Bringoltz teach the method of claim 5, and Pandev further teaches wherein the reference measurements use an x-ray technique, wherein the x-ray technique is XRD, XRR, or a soft x-ray technique ([0052] XRD, XRR are available as metrology tools; [0048] operation 204).
Regarding claim 28, Pandev modified by Arnold, Chouaib, Shchegrov, and Bringoltz teach the method of claim 1, and Pandev further teaches wherein the thickness of the film stack includes thickness of one or more layers in the film stack ([0032]” Targets can include multiple layers (or films) whose thicknesses can be measured by the metrology tool.”; [0034] "measuring the composition of one or more layers of the semiconductor stack").
Regarding claim 29, Pandev modified by Arnold, Chouaib, Shchegrov, and Bringoltz teach the method of claim 7, and Pandev further teaches wherein the machine-learning algorithm is trained on a theoretical model and/or measured data ([0047]; [0048] operation 204 collects reference data to use as a training component, thus measured data).
Regarding claim 13, Pandev teaches a non-transitory computer-readable storage medium, comprising one or more programs for executing the following steps on one or more computing devices (Fig. 2; Fig 4 processor 406; [0043]-[0044]):
receiving first optical measurements ([0050] operation 206, first set of signals) that include a thickness of a film stack of a workpiece and a composition of the film stack ([0017]; [0032]; [0034]; [0046]; [0052]) measured using SE, SR,and/or ARR ([0019]; [0023]; [0052]);
receiving second optical measurements ([0050] operation 208, second set of signals) that include the thickness of the film stack and the composition of the film stack ([0017]; [0032]; [0034]; [0046]; [0052]) measured using Raman spectroscopy ([0052] "Raman spectroscopy device"; [0031] light source may have multiple wavelengths);
combining the thickness and the composition in the first optical measurements and the thickness and the compositions in the second optical measurements to form combined measured data ([0045] " raw data resulting therefrom is combined"; [0054] operation 210, third set of signals made by combining the first and second set of signals; [0052] this raw data contains the thickness and composition measurements from above), wherein the combining includes regressing the first optical measurements and the second optical measurements ([0054] "resulting combined data set may be transformed to the third set of signals, optionally with a smaller number of parameters/columns", Principle component analysis is regression) and
determining the thickness of the film stack and the composition of the film stack using the combined measured data ([0058] operation 218 measures parametric values; [0046] parameters include thickness and composition); wherein the combining and the determining use a model that includes a transfer matrix ([0054] Principal component analysis (PCA) can be used for creating a model that transforms the combined data to the third set of signals or principal components; [0052] a Mueller matrix is a transfer matrix), and wherein the model is configured for both the first optical measurements and the second optical measurements ([0058] signals from a target component are collected, utilizing at least the first metrology tool and the second metrology tool, model is applied to the signals collected from the target component to measure parametric values for the target component; [0061]; [0064] these models may be regressed in a well known manner using the measured parametric values)
Pandev is silent as to using multiwavelength Raman spectroscopy.
However, Arnold does address this limitation. Pandev and Arnold are considered to be analogous to the present invention as they are in the same field of spectroscopy.
Arnold teaches multiwavelength Raman spectroscopy ([0083] multi-wavelength Raman spectroscopic system; [0087]).
It would have been well known to someone of ordinary skill in the art before the effective filing date of the claimed invention to use multiwavelength Raman to collect a Raman spectrum at multiple excitation wavelengths. Therefore, it would have been obvious to modify Pandev to incorporate multiwavelength Raman spectroscopy in order to mitigate the difficulties from trying to balance which regions of the spectrum to collect versus the information sought (Arnold [0083]).
Further, Pandev is silent as to wherein the first optical measurements and the second optical measurements are performed at least partly simultaneously.
However, Chouaib does address this limitation. Pandev, Arnold, and Chouaib are considered to be analogous to the present invention as they are in the same field of spectroscopy.
Chouaib teaches wherein the first optical measurements and the second optical measurements are performed at least partly simultaneously ([0148]-[0149] “specimen includes a single site having one or more measurement targets whose simultaneous, combined measurement is treated as a single specimen measurement or reference measurement”).
It would have been well known to someone of ordinary skill in the art before the effective filing date of the claimed invention to perform simultaneous or partly simultaneous measurements of a target. Therefore, it would have been obvious to modify Pandev in view of Arnold to be configured such that the first optical measurements and the second optical measurements are performed at least partly simultaneously as suggested by Chouaib as a simultaneous measurement reduces measurement time and thus increases efficiency.
Further, even if Pandev does not explicitly teach wherein the combining and the regressing occur until a convergence condition is satisfied, Pandev teaches the parameters are determined by applying techniques described in Shchegrov (Pandev [0064] Ser. No. 14/252,323).
Shchegrov does address this limitation. Shchegrov and Pandev are considered to be analogous to the present invention as they are in the same field of metrology.
Shchegrov teaches wherein the combining and the regressing occur until a convergence condition is satisfied ([0044] "The floating parameters are resolved by a fitting process (e.g., regression, library matching, etc.) that produces the best fit between theoretical predictions and measured data. The unknown specimen parameters, Pspecimen, are varied and the model output values are calculated until a set of specimen parameter values are determined that results in a close match between the model output values and the measured values."; a close match indicates that convergence is satisfied).
It would have been well known to someone of ordinary skill in the art before the effective filing date of the claimed invention to perform regression until convergence. Therefore, it would have been obvious to modify Pandev to explicitly include wherein the combining and the regressing occur until a convergence condition is satisfied as suggested by Shchegrov in order to achieve the most accurate results.
Further, even if Pandev does not explicitly teach wherein the combining and the determining use a physical model that includes a transfer matrix, and wherein the physical model is configured for both the first optical measurements and the second optical measurements, Pandex does teach measurement of parameters of interest usually involves a number of algorithms. For example, optical interaction of the incident beam with the sample is 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 is usually modeled (parameterized) using a geometric engine, or in some cases, process modeling engine or a combination of both ([0035]). The examiner also notes every mention of a physical model in the applicant’s specification indicates that it’s functionally equivalent to using a machine learning model and that either can be used (applicant’s specification [0019]).
Additionally, Shchegrov (which Pandev references) teaches model-based semiconductor metrology consists of formulating a metrology model that attempts to predict the measured optical signals based on a model of the interaction of the measurement target with the particular metrology system. The target-specific model includes a parameterization of the structure in terms of the physical properties of the measurement target of interest ([0042]-[0043]).
Further, Bringoltz does address this limitation. Bringoltz and Pandev are considered to be analogous to the present invention as they are in the same field of optical metrology.
Bringoltz teaches using a physical model ([0004] optical model is a physical model; [0011] calculating multiple sets of model pattern parameters by applying the optical model) for SE and Raman measurements ([0007] Exemplary scatterometric tools for measuring (acquiring) scatterometry data (e.g., spectrograms) may include spectral ellipsometers (SE)… Additional methods for measuring critical dimensions X-ray Raman spectrometry.) Bringoltz also teaches that the optical models are designed according to physical laws ([0010]) and the examiner notes that a transfer matrix in a physical model is an expression of the physical or optical laws of the system.
It would have been well known to someone of ordinary skill in the art before the effective filing date of the claimed invention to use a physical model, with a transfer matrix such as a Mueller matrix, to combine and determine values from provided data. Therefore, it would have been obvious to modify Pandev in view of Arnold and Chouaib to use a physical model instead of a machine learning model such that the combining and the determining use a physical model that includes a transfer matrix, and wherein physical model is configured for both the first optical measurements and the second optical measurement as suggested by Bringoltz as the direct association between physical parameters and theoretical optical properties means that optical model results are typically more easily interpretable than machine learning results (Bringoltz [0009]).
Regarding claim 14, Pandev modified by Arnold, Chouaib, Shchegrov, and Bringoltz teach the device of claim 13, and Pandev further teaches wherein the combining further includes combining the reference measurements with the first optical measurements and the second optical measurements ([0055] operation 212 "corresponding relationship between the third set of signals and the reference values is determined" where the third set of values are made from the first and second measurements).
Regarding claim 16, Pandev modified by Arnold, Chouaib, Shchegrov, and Bringoltz teach the device of claim 13, and Pandev further teaches wherein the combining and the determining further use a machine-learning algorithm ([0036]; [0049], [0056] "machine learning model").
Regarding claim 17, Pandev modified by Arnold, Chouaib, Shchegrov, and Bringoltz teach the device of claim 13, and Pandev further teaches wherein the first optical measurements and the second optical measurements further include strain, stress, and/or defects ([0034] "measuring certain defects on (or within) the wafer"; [0040] "stress").
Regarding claim 30, Pandev modified by Arnold, Chouaib, Shchegrov, and Bringoltz teach the device of claim 13, and Pandev further teaches receiving reference measurements that include the thickness of the film stack and the composition of the film stack measured using an x-ray technique, wherein the x-ray technique is XRD, XRR, or a soft x-ray technique ([0052] XRD, XRR are available as metrology tools; [0048] operation 204).
Regarding claim 31, Pandev modified by Arnold, Chouaib, Shchegrov, and Bringoltz teach the device of claim 14, and Pandev further teaches wherein the reference measurements use an x-ray technique, wherein the x-ray technique is XRD, XRR,or a soft x-ray technique ([0052] XRD, XRR are available as metrology tools; [0048] operation 204).
Regarding claim 32, Pandev modified by Arnold, Chouaib, Shchegrov, and Bringoltz teach the device of claim 13, and Pandev further teaches wherein the thickness of the film stack includes thickness of one or more layers in the film stack ([0032] ”Targets can include multiple layers (or films) whose thicknesses can be measured by the metrology tool.”; [0034] "measuring the composition of one or more layers of the semiconductor stack").
Regarding claim 33, Pandev modified by Arnold, Chouaib, Shchegrov, and Bringoltz teach the device of claim 16, and Pandev further teaches wherein the machine-learning algorithm is trained on a theoretical model and/or measured data ([0047]; [0048] operation 204 collects reference data to use as a training component, thus measured data).
Regarding claim 18, Pandev teaches a system comprising (at least Figs. 2,4):
a measurement unit in a system (metrology tool A; [0066]) configured to measure a thickness of a film stack of a workpiece and the composition of the film stack ([0017]; [0032]; [0034]; [0046]; [0052]) using SE, SR, and/or ARR ([0019]; [0023];[0052]) thereby generating first optical measurements ([0050] operation 206, first set of signals);
a Raman spectroscopy unit in a system configured to measure the thickness of the film stack and the composition of the film stack thereby generating second optical measurements (metrology tool B; [0052] "Raman spectroscopy device";[0031] light source may have multiple wavelengths);
and a processor in electronic communication with the measurement unit and the multiwavelength Raman spectroscopy unit (processor 406; [0067]), wherein the processor is configured to:
combine the thickness and the composition in the first optical measurements and the thickness and the compositions in the second optical measurements to form combined measured data ([0045] " raw data resulting therefrom is combined"; [0054] operation 210, third set of signals made by combining the first and second set of signals; [0052] this raw data contains the thickness and composition measurements from above), wherein the combining includes regressing the first optical measurements and the second optical measurements ([0054] "resulting combined data set may be transformed to the third set of signals, optionally with a smaller number of parameters/columns", Principle component analysis is regression) and
determine the thickness of the film stack and the composition of the film stack using the combined measured data ([0058] operation 218 measures parametric values; [0046] parameters include thickness and composition) wherein the combining and the determining use a model that includes a transfer matrix ([0054] Principal component analysis (PCA) can be used for creating a model that transforms the combined data to the third set of signals or principal components; [0052] a Mueller matrix is a transfer matrix), and wherein the model is configured for both the first optical measurements and the second optical measurements ([0058] signals from a target component are collected, utilizing at least the first metrology tool and the second metrology tool, model is applied to the signals collected from the target component to measure parametric values for the target component; [0061]; [0064] these models may be regressed in a well known manner using the measured parametric values).
Pandev is silent as to using multiwavelength Raman spectroscopy.
However, Arnold does address this limitation. Pandev and Arnold are considered to be analogous to the present invention as they are in the same field of spectroscopy.
Arnold teaches multiwavelength Raman spectroscopy ([0083] multi-wavelength Raman spectroscopic system; [0087]).
It would have been well known to someone of ordinary skill in the art before the effective filing date of the claimed invention to use multiwavelength Raman to collect a Raman spectrum at multiple excitation wavelengths. Therefore, it would have been obvious to modify Pandev to incorporate multiwavelength Raman spectroscopy in order to mitigate the difficulties from trying to balance which regions of the spectrum to collect versus the information sought (Arnold [0083]).
Further, Pandev is silent as to wherein the first optical measurements and the second optical measurements are performed at least partly simultaneously.
However, Chouaib does address this limitation. Pandev, Arnold, and Chouaib are considered to be analogous to the present invention as they are in the same field of spectroscopy.
Chouaib teaches wherein the first optical measurements and the second optical measurements are performed at least partly simultaneously ([0148]-[0149] “specimen includes a single site having one or more measurement targets whose simultaneous, combined measurement is treated as a single specimen measurement or reference measurement”).
It would have been well known to someone of ordinary skill in the art before the effective filing date of the claimed invention to perform simultaneous or partly simultaneous measurements of a target. Therefore, it would have been obvious to modify Pandev in view of Arnold to be configured such that the first optical measurements and the second optical measurements are performed at least partly simultaneously as suggested by Chouaib as a simultaneous measurement reduces measurement time and thus increases efficiency.
Further, even if Pandev does not explicitly teach wherein the combining and the regressing occur until a convergence condition is satisfied, Pandev teaches the parameters are determined by applying techniques described in Shchegrov (Pandev [0064] Ser. No. 14/252,323).
Shchegrov does address this limitation. Shchegrov and Pandev are considered to be analogous to the present invention as they are in the same field of metrology.
Shchegrov teaches wherein the combining and the regressing occur until a convergence condition is satisfied ([0044] "The floating parameters are resolved by a fitting process (e.g., regression, library matching, etc.) that produces the best fit between theoretical predictions and measured data. The unknown specimen parameters, Pspecimen, are varied and the model output values are calculated until a set of specimen parameter values are determined that results in a close match between the model output values and the measured values."; a close match indicates that convergence is satisfied).
It would have been well known to someone of ordinary skill in the art before the effective filing date of the claimed invention to perform regression until convergence. Therefore, it would have been obvious to modify Pandev to explicitly include wherein the combining and the regressing occur until a convergence condition is satisfied as suggested by Shchegrov in order to achieve the most accurate results.
Further, even if Pandev does not explicitly teach wherein the combining and the determining use a physical model that includes a transfer matrix, and wherein the physical model is configured for both the first optical measurements and the second optical measurements, Pandex does teach measurement of parameters of interest usually involves a number of algorithms. For example, optical interaction of the incident beam with the sample is 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 is usually modeled (parameterized) using a geometric engine, or in some cases, process modeling engine or a combination of both ([0035]). The examiner also notes every mention of a physical model in the applicant’s specification indicates that it’s functionally equivalent to using a machine learning model and that either can be used (applicant’s specification [0019]).
Additionally, Shchegrov (which Pandev references) teaches model-based semiconductor metrology consists of formulating a metrology model that attempts to predict the measured optical signals based on a model of the interaction of the measurement target with the particular metrology system. The target-specific model includes a parameterization of the structure in terms of the physical properties of the measurement target of interest ([0042]-[0043]).
Further, Bringoltz does address this limitation. Bringoltz and Pandev are considered to be analogous to the present invention as they are in the same field of optical metrology.
Bringoltz teaches using a physical model ([0004] optical model is a physical model; [0011] calculating multiple sets of model pattern parameters by applying the optical model) for SE and Raman measurements ([0007] Exemplary scatterometric tools for measuring (acquiring) scatterometry data (e.g., spectrograms) may include spectral ellipsometers (SE)… Additional methods for measuring critical dimensions X-ray Raman spectrometry.) Bringoltz also teaches that the optical models are designed according to physical laws ([0010]) and the examiner notes that a transfer matrix in a physical model is an expression of the physical or optical laws of the system.
It would have been well known to someone of ordinary skill in the art before the effective filing date of the claimed invention to use a physical model, with a transfer matrix such as a Mueller matrix, to combine and determine values from provided data. Therefore, it would have been obvious to modify Pandev in view of Arnold and Chouaib to use a physical model instead of a machine learning model such that the combining and the determining use a physical model that includes a transfer matrix, and wherein physical model is configured for both the first optical measurements and the second optical measurement as suggested by Bringoltz as the direct association between physical parameters and theoretical optical properties means that optical model results are typically more easily interpretable than machine learning results (Bringoltz [0009]).
Regarding claim 21, Pandev modified by Arnold, Chouaib, Shchegrov, and Bringoltz teach the system of claim 18, and Pandev further teaches wherein the processor is further configured to combine the reference measurements with the first optical measurements and the second optical measurements ([0055] operation 212 "corresponding relationship between the third set of signals and the reference values is determined" where the third set of values are from the first and second measurements).
Regarding claim 22, Pandev modified by Arnold, Chouaib, Shchegrov, and Bringoltz teach the system of claim 18, and Pandev further teaches wherein the combining and the determining further use a machine-learning algorithm ([0036]; [0049], [0056] "machine learning model").
Regarding claim 23, Pandev modified by Arnold, Chouaib, Shchegrov, and Bringoltz teach the system of claim 18, and Pandev further teaches wherein the first optical measurements and the second optical measurements further include strain, stress, and/or defects (([0034] "measuring certain defects on (or within) the wafer"; [0040] "stress").
Regarding claim 34, Pandev modified by Arnold, Chouaib, Shchegrov, and Bringoltz teach the system of claim 18, and Pandev further teaches wherein the thickness of the film stack includes thickness of one or more layers in the film stack ([0032]” Targets can include multiple layers (or films) whose thicknesses can be measured by the metrology tool.”; [0034] "measuring the composition of one or more layers of the semiconductor stack").
Regarding claim 35, Pandev modified by Arnold, Chouaib, Shchegrov, and Bringoltz teach the system of claim 21, and Pandev further teaches wherein the reference measurements use an x-ray technique, wherein the x-ray technique is XRD, XRR,or a soft x-ray technique ([0052] XRD, XRR are available as metrology tools; [0048] operation 204).
Regarding claim 36, Pandev modified by Arnold, Chouaib, Shchegrov, and Bringoltz teach the system of claim 22, and Pandev further teaches wherein the machine-learning algorithm is trained on a theoretical model and/or measured data ([0047]; [0048] operation 204 collects reference data to use as a training component, thus measured data).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“Axometrics: Experts in Mueller Matrix Polarization Metrology.” Axometrics.com, 2021, www.axometrics.com/articles/understanding-the-mueller-matrix.
US20190094711A1 by Atkins teaches methods and systems for performing spectroscopic measurements of asymmetric features of semiconductor structures and that estimates of critical dimensions are used to update the spectral response metrics, which, in turn, are used to generate improved estimates of the asymmetric parameters. This iteration continues until convergence on values of the asymmetry parameters ([0098])
US20060072807A1 by Bultman teaches methods and systems for monitoring semiconductor fabrication and that processor 270 may be configured to perform an iteration using one or more starting guesses through (possibly approximate) equations to converge to a good fit for one or more output signals from the measurement device. Suitable equations may include, but are not limited to, any non-linear regression algorithm known in the art ([0403]).
US20170176357A1 by Pois teaches systems and approaches for silicon germanium thickness and composition determination and that optimal values of both the SiGe thickness (t) and the Ge mixing fraction (f) that led to best agreement between the model and data are simultaneously determined via a non-linear regression analysis ([0038]).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KAITLYN E KIDWELL/Examiner, Art Unit 2877
/TARIFUR R CHOWDHURY/Supervisory Patent Examiner, Art Unit 2877
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