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 Amendment
1. Amendments filed 3/17/2026 have been entered, wherein claims 1-10 are pending. Accordingly, claims 1-10 have been examined herein. The previous claim objections, 35 USC 112(b) rejections and 35 USC 101 rejections have been withdrawn due to Applicant’s amendments. This action is Final.
Claim Objections
2. Claims 9 and 10 are objected to because of the following informalities:
Claim 9, “a plurality of groups” should read “[[a]] the plurality of groups” to avoid the antecedent basis issue.
Claim 9, “a clustering algorithm” should read “[[a]] the clustering algorithm” to avoid the antecedent basis issue.
Claim 10, “a plurality of groups” should read “[[a]] the plurality of groups” to avoid the antecedent basis issue.
Claim 10, “a clustering algorithm” should read “[[a]] the clustering algorithm” to avoid the antecedent basis issue.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
3. Although claims 1 and 5 include at least one mental process of producing a spectrum, creating a three-dimensional data, and determining a film thickness, the claims recite at least one additional element of controlling a polishing operation for the substrate based on the determined film thickness which provides a practical application and significantly more because the actions are based on the determined film thickness.
Claim Rejections - 35 USC § 102
4. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim 1 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kobata et al. (US PGPUB 20150332943), hereinafter Kobata.
Regarding claim 1, Kobata teaches a polishing method (fig. 6) comprising:
polishing a surface of a substrate (substrate W, fig. 6) by pressing the substrate against a polishing pad (fig. 6, polishing pad 22) on a rotating polishing table (fig. 6, rotating polishing table 20);
producing a spectrum of reflected light from the surface of the substrate each time the polishing table makes one rotation (Kobata teaches light is applied to the surface of the substrate each time the polishing table makes one revolution. A spectroscope disperses the reflected light according to wavelength and measures the intensity of the reflected light at each wavelength [0053]. Additionally, Kobata teaches a processor receives the data from the spectroscope and produces a spectrum [0054]. Kobata also teaches the processor determines the film thickness form the obtained spectrum. Specifically, the obtained spectrum is compared with a prepared reference spectra. This method includes the steps of comparing the spectrum at each point of time during polishing with the plural reference spectra and determining a film thickness. Therefore, the current film thickness can be estimated from the reference spectrum having a shape that is most similar to that of the spectrum obtained during polishing [0057]. Therefore, Kobata teaches producing a spectrum of reflected light form the surface of the substrate each time the polishing table makes one rotation.);
creating a three-dimensional data containing a plurality of spectra arranged along polishing time (As noted above, Kobata teaches producing a spectrum each time the table makes one rotation and determining the current film thickness. As polishing progresses and the processor continues to produce spectra, Kobata teaches creating a three-dimensional data containing a plurality of spectra arranged along a polishing time);
determining a film thickness of the substrate based on a time-series change in shapes of the plurality of spectra included in the three-dimensional data (Kobata teaches the current film thickness can be estimated from the reference spectrum having a shape that is most similar to the spectrum obtained during polishing by comparing the spectrum at each point of time during polishing with the plural reference spectra [0057]. As polishing progresses, the processor continues to produce a spectrum with each rotation of the polishing table and comparing to determine the thickness. Therefore, Kobata teaches determining a film thickness of the substrate based on a time-series change in of shapes the plurality of spectra included in the three-dimensional data (the shapes of the collected spectra change as polishing progresses, wherein the thickness is determined by comparing the changing shape with reference spectra)); and
controlling a polishing operation for the substrate based on the determined film thickness (Kobata teaches the processor determines the film thickness of the substrate from the spectrum and determines a polishing end point and the processor determines an optimum polishing load [0054] and [0058]).
Claim Rejections - 35 USC § 103
5. 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.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Kobata et al. (US PGPUB 20150332943), hereinafter Kobata, in view of Kobayashi et al. (US PGPUB 20170190020), hereinafter Kobayashi.
Regarding claim 5, Kobata teaches a polishing apparatus (fig. 6) comprising:
a polishing table (fig. 6, rotating polishing table 20) for supporting a polishing pad (fig. 6, polishing pad 22), the polishing table being rotatable (fig. 6, rotating polishing table 20);
a polishing head (see Kobata’s annotated fig. 6 below) configured to press a substrate (fig. 6, substrate W) against the polishing pad to polish a surface of the substrate (fig. 6);
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a sensor head (fig. 6, first optical head 13a and second optical head 13b are being interpreted as the sensor head) located in the polishing table (fig. 6), the sensor head being configured to direct light to the surface of the substrate and receive reflected light from the surface of the substrate [0069];
a processing system (processor 15) performing a program configured to cause the processing system to produce a spectrum of the reflected light, create a three-dimensional data containing a plurality of spectra arranged along polishing time, and determine a film thickness of the substrate based on a time-series change in shapes of the plurality of spectra included in the three-dimensional data (Kobata teaches light is applied to the surface of the substrate each time the polishing table makes one revolution. A spectroscope disperses the reflected light according to wavelength and measures the intensity of the reflected light at each wavelength [0053]. Additionally, Kobata teaches a processor receives the data from the spectroscope and produces a spectrum [0054]. Kobata also teaches the processor determines the film thickness form the obtained spectrum. Specifically, the obtained spectrum is compared with a prepared reference spectra. This method includes the steps of comparing the spectrum at each point of time during polishing with the plural reference spectra and determining a film thickness. Therefore, the current film thickness can be estimated from the reference spectrum having a shape that is most similar to that of the spectrum obtained during polishing [0057]. As noted above, Kobata teaches producing a spectrum each time the table makes one rotation and determining the current film thickness. As polishing progresses and the processor continues to produce spectra, Kobata teaches creating a three-dimensional data containing a plurality of spectra arranged along a polishing time. Kobata teaches the current film thickness can be estimated from the reference spectrum having a shape that is most similar to the spectrum obtained during polishing by comparing the spectrum at each point of time during polishing with the plural reference spectra [0057]. As polishing progresses, the processor continues to produce spectra with each rotation of the polishing table. Therefore, Kobata teaches determining a film thickness of the substrate based on a time-series change in shapes of the plurality of spectra included in the three-dimensional data (the shapes of the collected spectra change as polishing progresses, wherein the thickness is determined by comparing the changing shape with reference spectra)); and
a polishing controller (controller 19) configured to control a polishing operation for the substrate based on the film thickness determined by the processing system (Kobata teaches the processor determines the film thickness of the substrate from the spectrum and determines a polishing end point and the processor determines an optimum polishing load [0054] and [0058] and [0019]).
Additionally, Kobata teaches a general purpose computer or a dedicated computer can be used as the processor [0054].
Kobata does not explicitly teach the processing system having a memory storing therein a program.
However, Kobayashi teaches a polishing method and polishing apparatus concerned with generating a spectrum and comparing it to reference spectrum. Additionally, Kobayashi teaches a processor for determining the film thickness from an optical signal [0046], wherein the processor is coupled to a storage device [0066].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Kobata to incorporate the teachings of Kobayashi to provide wherein the processing system has a memory storing therein a program configured to cause the processing system to produce a spectrum of the reflected light, create a three-dimensional data containing a plurality of spectra arranged along polishing time, and determine a film thickness of the substrate based on the three-dimensional data. Specifically it would have been obvious to incorporate a memory for cooperating with the processor of Kobata. Doing so would allow the device to continue to function as intended and determine the film thickness of the substrate.
Claims 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Kobata et al. (US PGPUB 20150332943), hereinafter Kobata, in view of Yennie et al. (US PGPUB 20190286111), hereinafter Yennie.
Regarding claims 2-3, Kobata teaches the claimed invention as rejected above in claim 1. However, Kobata does not explicitly teach
wherein determining the film thickness of the substrate comprises:
inputting the three-dimensional data into a film-thickness calculation model that has been constructed according to an artificial intelligence algorithm; and
outputting the film thickness from the film-thickness calculation model,
wherein the film-thickness calculation model is a trained model that has been constructed with use of a training data set containing a combination of a plurality of training three-dimensional data and a plurality of film thicknesses associated with the plurality of training three-dimensional data, respectively, and each of the plurality of training three-dimensional data includes a plurality of reference spectra arranged along polishing time.
However, Yennie teaches a machine learning system for monitoring of semiconductor processing wherein during polishing of a substrate in the polishing system, the substrate can be monitored with an in-situ spectrographic monitoring system to generate a plurality of measured spectra of the substrate being polished. The plurality of measured spectra are passed to the trained machine learning model to generate a plurality of characterizing values, e.g., thickness measurements, and at least one processing parameter of the polishing system is controlled based on the plurality of characterizing values [0099]. Specifically, Yennie teaches
wherein determining the film thickness of the substrate comprises:
inputting the three-dimensional data into a film-thickness calculation model that has been constructed according to an artificial intelligence algorithm [0099] and [0025]; and
outputting the film thickness from the film-thickness calculation model [0099],
wherein the film-thickness calculation model is a trained model that has been constructed with use of a training data set containing a combination of a plurality of training three-dimensional data and a plurality of film thicknesses associated with the plurality of training three-dimensional data, respectively, and each of the plurality of training three-dimensional data includes a plurality of reference spectra arranged along polishing time [0037-0038].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Kobata to incorporate the teachings of Yennie to provide
wherein determining the film thickness of the substrate comprises:
inputting the three-dimensional data into a film-thickness calculation model that has been constructed according to an artificial intelligence algorithm; and
outputting the film thickness from the film-thickness calculation model,
wherein the film-thickness calculation model is a trained model that has been constructed with use of a training data set containing a combination of a plurality of training three-dimensional data and a plurality of film thicknesses associated with the plurality of training three-dimensional data, respectively, and each of the plurality of training three-dimensional data includes a plurality of reference spectra arranged along polishing time.
Specifically, it would have been obvious to incorporate wherein the measured spectra are input into the trained machine learning model to generate a plurality of characterizing values such as thickness, wherein the machine learning model is an artificial neural network, wherein a physical process model is utilized to generate training values for the entire set of spectra for the model (as taught by paragraphs 0037-0038 of Yennie). Doing so would promote increased quality of the workpiece by allowing the process to adapt to the instant configuration of the system through the artificial neural network. Additionally, doing so would provide the neural network with accurate training data which promotes the quality of the workpiece and the output of the model.
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Kobata et al. (US PGPUB 20150332943), hereinafter Kobata, in view of Kobayashi et al. (US PGPUB 20170190020), hereinafter Kobayashi, as applied to claim 5 above, and further in view of Yennie et al. (US PGPUB 20190286111), hereinafter Yennie.
Regarding claims 6-7, Kobata, as modified, teaches the claimed invention as rejected above in claim 5. However, Kobata, as modified, does not explicitly teach
wherein the memory stores therein a film- thickness calculation model that has been constructed according to an artificial intelligence algorithm, and
the processing system is configured to input the three-dimensional data into the film- thickness calculation model, and output the film thickness from the film-thickness calculation model,
wherein the film-thickness calculation model is a trained model that has been constructed with use of a training data set containing a combination of a plurality of training three-dimensional data and a plurality of film thicknesses associated with the plurality of training three-dimensional data, respectively, and each of the plurality of training three-dimensional data includes a plurality of reference spectra arranged along polishing time.
However, Yennie teaches a machine learning system for monitoring of semiconductor processing wherein during polishing of a substrate in the polishing system, the substrate can be monitored with an in-situ spectrographic monitoring system to generate a plurality of measured spectra of the substrate being polished. The plurality of measured spectra are passed to the trained machine learning model to generate a plurality of characterizing values, e.g., thickness measurements, and at least one processing parameter of the polishing system is controlled based on the plurality of characterizing values [0099]. Specifically, Yennie teaches
wherein the memory stores therein a film- thickness calculation model that has been constructed according to an artificial intelligence algorithm [0054 and 0025], and
the processing system is configured to input the three-dimensional data into the film- thickness calculation model, and output the film thickness from the film-thickness calculation model [0099],
wherein the film-thickness calculation model is a trained model that has been constructed with use of a training data set containing a combination of a plurality of training three-dimensional data and a plurality of film thicknesses associated with the plurality of training three-dimensional data, respectively, and each of the plurality of training three-dimensional data includes a plurality of reference spectra arranged along polishing time [0037-0038].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have further modified Kobata, as modified, to incorporate the teachings of Yennie to provide
wherein the memory stores therein a film- thickness calculation model that has been constructed according to an artificial intelligence algorithm, and
the processing system is configured to input the three-dimensional data into the film- thickness calculation model, and output the film thickness from the film-thickness calculation model,
wherein the film-thickness calculation model is a trained model that has been constructed with use of a training data set containing a combination of a plurality of training three-dimensional data and a plurality of film thicknesses associated with the plurality of training three-dimensional data, respectively, and each of the plurality of training three-dimensional data includes a plurality of reference spectra arranged along polishing time.
Specifically, it would have been obvious to incorporate wherein the measured spectra are input into the trained machine learning model to generate a plurality of characterizing values such as thickness, wherein the machine learning model is an artificial neural network, wherein a physical process model is utilized to generate training values for the entire set of spectra for the model (as taught by paragraphs 0037-0038 of Yennie). Doing so would promote increased quality of the workpiece by allowing the process to adapt to the instant configuration of the system through the artificial neural network. Additionally, doing so would provide the neural network with accurate training data which promotes the quality of the workpiece and the output of the model.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Kobata et al. (US PGPUB 20150332943), hereinafter Kobata, in view of Yennie et al. (US PGPUB 20190286111), hereinafter Yennie, as applied to claims 2-3 above, and further in view of Shrestha (US PGPUB 20140273296).
Regarding claim 4, Kobata, as modified, teaches the claimed invention as rejected above in claim 3. Additionally, Kobata, as modified, teaches
wherein the film-thickness calculation model is the trained model obtained by:
creating the plurality of training three-dimensional data each containing the plurality of reference spectra produced when a plurality of reference substrates are polished (paragraph 0038 of Yennie, wherein the teachings of paragraph 0038 of Yennie were incorporated as detailed in the rejection of claims 2-3, wherein Yennie teaches different situations may need different physical process models to generate the characterizing values);
creating test three-dimensional data by arranging a plurality of spectra along polishing time, the plurality of spectra of the test three-dimensional data being produced when a test substrate is polished (paragraphs 0037-0038 of Yennie, wherein the teachings of paragraphs 0037-0038 of Yennie were incorporated as detailed in the rejection of claims 2-3, wherein Yennie teaches different situations may need different physical process models to generate the characterizing values as a function of processing time; these characterizing values can be associated with the spectra; this permits the spectra (with the characterizing values) to be used as training data.),
constructing the film-thickness calculation model with use of the training data set (paragraphs 0037-0038 of Yennie, wherein the teachings of paragraphs 0037-0038 of Yennie were incorporated as detailed in the rejection of claims 2-3).
Kobata, as modifieds, does not explicitly teach
dividing the plurality of training three-dimensional data into a plurality of groups according to a clustering algorithm;
selecting, from the plurality of groups, one group including the training three-dimensional data that best matches the test three-dimensional data; and
constructing the film-thickness calculation model with use of the training data set containing the combination of the plurality of training three-dimensional data belonging to the selected group and the plurality of film thicknesses associated with the plurality of training three- dimensional data, respectively.
However, Shrestha teaches a metric for recognizing correct library spectrum in a wafer processing system, wherein the database 350 can store a plurality of libraries 310 of reference spectra 320. In this case, each library of reference spectra can be a collection of reference spectra which represent substrates that share a property in common. However, the property shared in common in a single library may vary across multiple libraries of reference spectra. For example, two different libraries can include reference spectra that represent substrates with two different underlying thicknesses. For a given library of reference spectra, variations in the upper layer thickness, rather than other factors (such as differences in wafer pattern, underlying layer thickness, or layer composition), can be primarily responsible for the differences in the spectral intensities [0047]. Overall, Shrestha teaches
dividing the plurality of training three-dimensional data into a plurality of groups according to a clustering algorithm (each of the libraries of Shrestha is interpreted as a different group of the plurality of groups, wherein the libraries are divided via different substrate properties which is interpreted as a clustering algorithm);
selecting, from the plurality of groups, one group including the training three-dimensional data that best matches the test three-dimensional data (paragraph 0053, Shrestha teaches for selecting a matching reference spectrum according to the measured spectrum and the corresponding library is identified); and
utilizing the combination of the plurality of training three-dimensional data belonging to the selected group and the plurality of film thicknesses associated with the plurality of training three- dimensional data, respectively (Shrestha teaches one of the potential libraries can be selected as the library of reference spectra to use for endpoint detection [0052]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have further modified Kobata, as modified, to incorporate the teachings of Shrestha to provide
dividing the plurality of training three-dimensional data into a plurality of groups according to a clustering algorithm;
selecting, from the plurality of groups, one group including the training three-dimensional data that best matches the test three-dimensional data; and
constructing the film-thickness calculation model with use of the training data set containing the combination of the plurality of training three-dimensional data belonging to the selected group and the plurality of film thicknesses associated with the plurality of training three- dimensional data, respectively.
Specifically, it would have been obvious to incorporate the teachings of Shrestha and provide a plurality of libraries of spectra as potential training data, wherein the plurality of libraries coordinate to different workpiece structural properties, wherein a single library is selected as the training data according to a best matching spectrum of a measured test spectrum. Doing so would promote the quality of the workpiece by providing libraries including multiple combinations of workpiece properties in order to establish a more accurate match. Additionally, doing so would increase the utility of the system by allowing the system to operate on an increased number of types of workpieces having different combinations of structural properties.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Kobata et al. (US PGPUB 20150332943), hereinafter Kobata, in view of Kobayashi et al. (US PGPUB 20170190020), hereinafter Kobayashi, and further in view of Yennie et al. (US PGPUB 20190286111), hereinafter Yennie, as applied to claims 6-7 above, and further in view of Shrestha (US PGPUB 20140273296).
Regarding claim 8, Kobata, as modified, teaches the claimed invention as rejected above in claims 6-7. Additionally, Kobata, as modified, teaches
wherein the film-thickness calculation model is the trained model obtained by:
creating the plurality of training three-dimensional data each containing the plurality of reference spectra produced when a plurality of reference substrates are polished (paragraph 0038 of Yennie, wherein the teachings of paragraph 0038 of Yennie were incorporated as detailed in the rejection of claims 6-7, wherein Yennie teaches different situations may need different physical process models to generate the characterizing values);
creating test three-dimensional data by arranging a plurality of spectra along polishing time, the plurality of spectra of the test three-dimensional data being produced when a test substrate is polished (paragraphs 0037-0038 of Yennie, wherein the teachings of paragraphs 0037-0038 of Yennie were incorporated as detailed in the rejection of claims 6-7, wherein Yennie teaches different situations may need different physical process models to generate the characterizing values as a function of processing time; these characterizing values can be associated with the spectra; this permits the spectra (with the characterizing values) to be used as training data.),
constructing the film-thickness calculation model with use of the training data set (paragraphs 0037-0038 of Yennie, wherein the teachings of paragraphs 0037-0038 of Yennie were incorporated as detailed in the rejection of claims 6-7).
Kobata, as modifieds, does not explicitly teach
dividing the plurality of training three-dimensional data into a plurality of groups according to a clustering algorithm;
selecting, from the plurality of groups, one group including the training three-dimensional data that best matches the test three-dimensional data; and
constructing the film-thickness calculation model with use of the training data set containing the combination of the plurality of training three-dimensional data belonging to the selected group and the plurality of film thicknesses associated with the plurality of training three- dimensional data, respectively.
However, Shrestha teaches a metric for recognizing correct library spectrum in a wafer processing system, wherein the database 350 can store a plurality of libraries 310 of reference spectra 320. In this case, each library of reference spectra can be a collection of reference spectra which represent substrates that share a property in common. However, the property shared in common in a single library may vary across multiple libraries of reference spectra. For example, two different libraries can include reference spectra that represent substrates with two different underlying thicknesses. For a given library of reference spectra, variations in the upper layer thickness, rather than other factors (such as differences in wafer pattern, underlying layer thickness, or layer composition), can be primarily responsible for the differences in the spectral intensities [0047]. Overall, Shrestha teaches
dividing the plurality of training three-dimensional data into a plurality of groups according to a clustering algorithm (each of the libraries of Shrestha is interpreted as a different group of the plurality of groups, wherein the libraries are divided via different substrate properties which is interpreted as a clustering algorithm);
selecting, from the plurality of groups, one group including the training three-dimensional data that best matches the test three-dimensional data (paragraph 0053, Shrestha teaches for selecting a matching reference spectrum according to the measured spectrum and the corresponding library is identified); and
utilizing the combination of the plurality of training three-dimensional data belonging to the selected group and the plurality of film thicknesses associated with the plurality of training three- dimensional data, respectively (Shrestha teaches one of the potential libraries can be selected as the library of reference spectra to use for endpoint detection [0052]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have further modified Kobata, as modified, to incorporate the teachings of Shrestha to provide
dividing the plurality of training three-dimensional data into a plurality of groups according to a clustering algorithm;
selecting, from the plurality of groups, one group including the training three-dimensional data that best matches the test three-dimensional data; and
constructing the film-thickness calculation model with use of the training data set containing the combination of the plurality of training three-dimensional data belonging to the selected group and the plurality of film thicknesses associated with the plurality of training three- dimensional data, respectively.
Specifically, it would have been obvious to incorporate the teachings of Shrestha and provide a plurality of libraries of spectra as potential training data, wherein the plurality of libraries coordinate to different workpiece structural properties, wherein a single library is selected as the training data according to a best matching spectrum of a measured test spectrum. Doing so would promote the quality of the workpiece by providing libraries including multiple combinations of workpiece properties in order to establish a more accurate match. Additionally, doing so would increase the utility of the system by allowing the system to operate on an increased number of types of workpieces having different combinations of structural properties.
Allowable Subject Matter
6. Claims 9-10 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 9, Kobata et al. (US PGPUB 20150332943), hereinafter Kobata, in view of Yennie et al. (US PGPUB 20190286111), hereinafter Yennie, and further in view of Shrestha (US PGPUB 20140273296) is the closest prior art to the claimed invention but fails to teach or make obvious in combination with the additionally cited prior art the features of dividing the plurality of training three-dimensional data into a plurality of groups by performing arithmetic operations that classify the plurality of training three-dimensional data based on a feature as particularly claimed in combination with all other elements of claims 1-4.
Regarding claim 10, Kobata et al. (US PGPUB 20150332943), hereinafter Kobata, in view of Kobayashi et al. (US PGPUB 20170190020), hereinafter Kobayashi, and further in view of Yennie et al. (US PGPUB 20190286111), hereinafter Yennie, and further in view of Shrestha (US PGPUB 20140273296) is the closest prior art to the claimed invention but fails to teach or make obvious in combination with the additionally cited prior art the features of dividing the plurality of training three-dimensional data into a plurality of groups by performing arithmetic operations that classify the plurality of training three-dimensional data based on a feature as particularly claimed in combination with all other elements of claims 5-8.
Response to Arguments
7. Applicant requests reconsideration and withdrawal of the 35 USC 101 rejections. As noted above, the 35 USC 101 rejections have been withdrawn. See above rejection for more details.
Applicant's arguments filed 3/17/2026 have been fully considered but they are not persuasive.
Applicant argues that the three-dimensional structure itself must be used as the basis for the thickness determination and that the rejection theory effectively collapses the distinction between a sequence of independent snapshots and a temporally structured data set whose internal time-series relationships are used for analysis (page 11 of Applicant’s remarks). Applicant argues Kobata fails to teach storing spectra together as a unified three-dimensional dataset and processing the dataset as a whole. Applicant argues Kobata fails to teach analyzing spectral trajectories or temporal patterns. Applicant argues Kobata’s teaching of determining a film thickness on any one of the spectra is not the same as the previous claim language. Applicant argues the phrase based on the three-dimensional data requires that the plurality of spectra arranged along polishing time collectively inform the thickness determination, not merely that thickness determinations occur at multiple times. Kobata lacks any disclosure that thickness determination becomes more accurate, more robust or otherwise different by virtue of considering spectral change over time (page 12 of Applicant’s remarks). The examiner respectfully disagrees.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “a temporally structured data set whose internal time-series relationships are used for analysis”, “spectral trajectories or temporal patterns”, and “thickness determination becomes more accurate, more robust or otherwise different by virtue of considering spectral change over time”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Specifically, the claim language is not as specific as argued. The previous claim language required creating three-dimensional data containing a plurality of spectra arranged along polishing time and determining a film thickness of the substrate based on the three-dimensional data. The language did not recite internal time-series relationships are used for analysis. The previous claim language did not specifically recite how the three-dimensional data was utilized to determine the film thickness. The language does not recite determining the film thickness based on the entirety of the three dimensional data.
Kobata teaches
creating a three-dimensional data containing a plurality of spectra arranged along polishing time (As noted above, Kobata teaches producing a spectrum each time the table makes one rotation and determining the current film thickness. As polishing progresses and the processor continues to produce spectra, Kobata teaches creating a three-dimensional data containing a plurality of spectra arranged along a polishing time);
determining a film thickness of the substrate based on a time-series change in shapes of the plurality of spectra included in the three-dimensional data (Kobata teaches the current film thickness can be estimated from the reference spectrum having a shape that is most similar to the spectrum obtained during polishing by comparing the spectrum at each point of time during polishing with the plural reference spectra [0057]. As polishing progresses, the processor continues to produce a spectrum with each rotation of the polishing table and comparing to determine the thickness. Therefore, Kobata teaches determining a film thickness of the substrate based on a time-series change in of shapes the plurality of spectra included in the three-dimensional data (the shapes of the collected spectra change as polishing progresses, wherein the thickness is determined by comparing the changing shape with reference spectra)).
Overall, Kobata teaches determining the film thickness based on a change of shape of the plurality of spectra over time, wherein the plurality of spectra over time are interpreted as the three-dimensional data.
Applicant argues the amended language now requires the temporal evolution of the shape serves as the basis for thickness determination. Applicant argues Kobata does not teach how spectral shape changes over polishing time. Applicant argues repeated acquisition does not equal time-series analysis (page 12 of Applicant’s remarks). The examiner respectfully disagrees.
The shapes of the collected spectra change as polishing progresses, wherein the thickness is determined by comparing the changing shape with reference spectra. Overall, the changing spectra shapes due to continued polishing teaches a time-series change in shapes of the plurality of spectra. Kobata teaches each reference spectrum is associated with a film thickness at a point of time when that reference spectrum is obtained. Therefore, the current film thickness can be estimated from the reference spectrum having a shape that is most similar to that spectrum obtained during polishing [0057]. See above rejection for more details.
Regarding claim 5, Applicant argues the rejection rests on the same flawed premise underlying the 102 rejection and that the mere existence of multiple spectra at different times does not establish that Kobata’s apparatus is configured to generate and use the three-dimensional dataset as an analytic input. The examiner respectfully disagrees. See above response to arguments for more details. The shapes of the collected spectra change as polishing progresses, wherein the thickness is determined by comparing the changing shape with reference spectra. Overall, the changing spectra shapes due to continued polishing teaches a time-series change in shapes of the plurality of spectra. Kobata teaches each reference spectrum is associated with a film thickness at a point of time when that reference spectrum is obtained. Therefore, the current film thickness can be estimated from the reference spectrum having a shape that is most similar to that spectrum obtained during polishing [0057]. See above rejection for more details.
Applicant argues Kobayashi does not bridge the gap between Kobata’s teachings and the instant claim language. Specifically, Applicant argues Kobayashi does not teach a three-dimensional data set and determining thickness based on a temporal change. However, Kobayashi was only relied upon to teach the language of a memory storing therein a program. As detailed above, Kobata’s teachings were relied upon to teach the language of creating the three-dimensional data and determining the film thickness based on the three dimensional data. See above rejection for more details.
Applicant argues the prior art fails to teach the amended language of the instant claims. The examiner respectfully disagrees. Kobata teaches
creating a three-dimensional data containing a plurality of spectra arranged along polishing time (As noted above, Kobata teaches producing a spectrum each time the table makes one rotation and determining the current film thickness. As polishing progresses and the processor continues to produce spectra, Kobata teaches creating a three-dimensional data containing a plurality of spectra arranged along a polishing time);
determining a film thickness of the substrate based on a time-series change in shapes of the plurality of spectra included in the three-dimensional data (Kobata teaches the current film thickness can be estimated from the reference spectrum having a shape that is most similar to the spectrum obtained during polishing by comparing the spectrum at each point of time during polishing with the plural reference spectra [0057]. As polishing progresses, the processor continues to produce a spectrum with each rotation of the polishing table and comparing to determine the thickness. Therefore, Kobata teaches determining a film thickness of the substrate based on a time-series change in of shapes the plurality of spectra included in the three-dimensional data (the shapes of the collected spectra change as polishing progresses, wherein the thickness is determined by comparing the changing shape with reference spectra)); and
controlling a polishing operation for the substrate based on the determined film thickness (Kobata teaches the processor determines the film thickness of the substrate from the spectrum and determines a polishing end point and the processor determines an optimum polishing load [0054] and [0058]).
See above rejection for more details.
The dependent claims have been rejected accordingly.
Applicant argues Shrestha fails to teach dividing the plurality of training three-dimensional data into a plurality of groups according to a clustering algorithm. The examiner respectfully disagrees. Shrestha’s teaching of each of the libraries varying across the property shared in common in a single library is interpreted as wherein the libraries are divided via different substrate properties which is interpreted as a clustering algorithm. The language does not specifically recite features of the clustering algorithm. See above rejection for more details.
Applicant argues claims 9 and 10 clarify the nature of the claimed clustering algorithm, and that the new language requires that the grouping be performed through arithmetic operations that classify the training three dimensional data based on features of the data itself. Claims 9 and 10 have been indicated as allowable above. See above for more details.
Conclusion
8. 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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/MICHAEL A GUMP/ Primary Examiner, Art Unit 3723
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