DETAILED ACTION
This Office action is in response to application filed on 11/25/2025.
Notice of Pre-AIA or AIA Status
1. 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
2. Applicant’s amendments filed 11/25/2025 to the specification, drawings and claims are accepted and entered. In this amendment:
Claims 1, 9, 11-12, 19, and 22 have been amended.
Claims 1-22 are examined.
Response to Argument
3. Applicant’s arguments filed on 11/25/2025 have been fully considered. Thus:
The drawings and the specification are withdrawn.
The 112 rejection is withdrawn.
The claim objection is withdrawn.
Regarding effective date that is persuasive and is withdrawn. The effective filing
date of June 5th, 2024 is accepted.
Regarding the 101 rejection, the arguments are not persuasive. Applicant argues
that the specific technological improvements in metrology systems that solve concrete problems in semiconductor manufacturing. The claims are not directed to abstract ideas under Step 2A, Prong One. The claims recite steps that are integrated into practical applications in a manner sufficient to satisfy the analysis of Step 2A, Prong Two, by providing specific technological improvements that solve concrete problems in semiconductor metrology. In Step 2B, the claims recite significantly more than any abstract idea. The claims are patent eligible under 35 U.S.C. § 101. and respectfully requests withdrawal of the rejection. See Remark, pages 13-17.
In response, the Examiner respectfully disagrees. As addressed in the previous
Office action, claims 1, 12, and 22 recited limitations fall into groupings of mental process and mathematical concepts (Step 2A -prong One is yes). The additional limitations of receiving data which are data gathering using conventional first and second metrology sub-systems, using controller and machine learning model are recited at a high level of generality, and thus, Step 2A-prong Two is no. When viewed as a whole, nothing in the claims add significant more. The claims are ineligible in Step 2B.
Regarding 103 rejection, the arguments are moot in view of new grounds of
rejection as necessitated by amendments. In response to the amended limitation that Sano does read on the new feature as recited in claims 1, 12 and 22. Sano discloses statistics or internal relationship of the original dataset is preserved. Synthetic partially synthetic data and hybrid data, see Sano, page 101 /right column, first 4l lines. It is noted “statistics” considered “real data” and “original data set” is “real training data”. Also, synthetic data included hybrid data “real data”.
Claim Objections
4. Claims 11 and 21 are objected to because of the following informalities:
Claim 11 recites “the first correlation threshold is greater to the second correlation threshold” should read “the first correlation threshold is greater than the second correlation threshold.”
Claim 21 recites “the second correlation threshold is greater to the second correlation threshold” that should be amended the same as amended claim 11 “the first correlation threshold is greater than the second correlation threshold.”
Appropriate correction is required.
Claim Rejections - 35 USC § 101
5. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
6. Claims 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
Under Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed to a process and apparatus (claims 1, 12 and 22, system and method), which are statutory categories.
However, evaluating claims 1, 12 and 22, under Step 2A, Prong One, the claims are directed to the judicial exception of an abstract idea using groupings of: mental processes including: “identifying one or more correlated principal components of the real training data that satisfy a first correlation threshold with the reference data; and
generating metrology measurements for one or more run-time samples;” mathematical/ relationship/calculation including: “performing a dimensionality reduction operation on the real training data; and extracting the one or more correlated principal components associated with the real training data from the synthetic training data as dimensionality-reduced synthetic training data;” and both mental process and mathematical concepts including: “generating the real training dataset by filtering the real training data to include portions of the real training data associated with the one or more correlated principal components; generating synthetic training data for a plurality of simulated test features having known simulated values of the metrology measurement; generating the synthetic training dataset by filtering the dimensionality-reduced synthetic training data to include portions of the dimensionality-reduced synthetic training data that satisfy a second correlation threshold with the reference data; and training a machine learning model to generate a value of the metrology measurement with the real training dataset and the synthetic training dataset.”
Next, Step 2A, Prong Two evaluates whether additional elements of the claim “integrate the abstract idea into a practical application” in a manner that imposes a meaningful limit on the judicial exception, such that the claims are more than a drafting effort designed to monopolize the exception. The additional limitations of “receiving real training data and receiving reference data” are data gathering using conventional equipment, i.e. a first and second metrology sub-systems, which correspond to insignificant extra-solution activities. A controller is recited at a high level of generality, i.e., as a generic computer component performing generic computer function of processing data. This generic controller limitation is no more than mere instructions to apply the exception using a generic computer component, and reciting a machine learning model which is built using machine algorithms is a generic computer function. Accordingly, these additional elements/limitations do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are not patent eligible.
At Step 2B, consideration is given to additional elements that may make the abstract idea significantly more. Under Step 2B, there are no additional elements that make the claims significantly more than the abstract idea.
The additional limitations as recited above in step 2A prong Two, are considered
Insignificant extra-solution activities that are not sufficient to integrate the claims into a particular practical application.
Dependent claims 2-11 and 13-21 do not disclose limitations considered to be significantly more which would render the claimed invention a patent eligible application of the abstract idea. The claim merely extends (or narrow) the abstract idea which do not amount for "significant more" because it merely adds details to the algorithm which forms the abstract idea as discussed above.
Claim Rejections - 35 USC § 103
7. The following is a quotation under AIA of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action.
A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
8. Claims 1-5, 7, 9-15, 17, and 19-22 are rejected under 35 U.S.C. 103 as being obvious over US 2022/0352041 of Pandev et al., “Pandev” (of record) in view of “Improvement of virtual metrology performance by removing metrology noise in the training dataset (of record) of Kim et al “Kim” (of record), and “Synthetic Data by Principal Component Analysis” of Natsuki Sano “Sano” (of record).
As for Claims 1, 12, and 22, Pandev teaches metrology systems and method [0002], comprising: a controller (Fig 2, control module 210) including one or more processors configured to execute program instructions causing the one or more processors [0110] to implement a metrology recipe by:
generating a real training dataset for a metrology measurement (Fig 2: a training dataset of measurement data Sj DOE 203 is generated considered “real training dataset” [0054]) by: receiving real training data from test features on one or more training samples from a first metrology sub-system (Fig 2: machine learning “ML” module 206 considered “a first metrology subsystem” receives measurement data Sj DOE 203 [0054], [0016]-[0017]);
receiving reference data associated with the metrology measurement for the test features from a second metrology sub-system (Fig 2: error evaluation “EE” module 208 considered “a second metrology subsystem” receives reference NCP DOE 205);
generating the real training dataset by filtering the real training data (optimization function drives changes to the weighting values and bias value in neural network that minimizes the optimization function considered a form of filtering from real training data [0066], [0055]);
generating a synthetic training dataset for the metrology measurement by: generating synthetic training data for a plurality of simulated test features having known simulated values of the metrology measurement (synthetic generated based on a process simulation tool [0047], the training dataset is generated by metrology simulation considered “synthetic dataset” [0051]);
training a machine learning model to generate a value of the metrology measurement with the real training dataset and the synthetic training dataset (Fig 3: trained parameter conditioned measurement considered synthetic training dataset [0069]-[0070], i.e., Fig 2: ML module 206 generated trained measurement 213); and
Pandev does not explicitly teach performing a dimensionality reduction operation on the real training data; identifying one or more correlated principal components of the real training data that satisfy a first correlation threshold with the reference data; the real training data includes portions of the real training data associated with the one or more correlated principal components; extract the one or more correlated principal components associated with the real training data from the synthetic training data as dimensionality-reduced synthetic training data; generating the synthetic training dataset by filtering the dimensionality-reduced synthetic training data to include portions of the dimensionality-reduced synthetic training data that satisfy a second correlation threshold with the reference data; and generating metrology measurements for one or more run-time samples using the machine learning model and measurement data associated with the one or more run-time samples.
Kim teaches performing a dimensionality reduction operation on the real training data (multivariate statistical process control “SPC” uses a dimensionality reduction method, see page 174/ left column/ second para. SPC uses real data because the statistical data of each sensor value, see page 177/ section 2.4);
identifying one or more correlated principal components of the real training data that satisfy a first correlation threshold with the reference data (Table 4 shows full dataset as performance reference, Table 5 shows parameter that controls the percentage of normal data of all the training data, and training data parameter settings considered a type of correlation threshold, i.e., identified principal component analysis “PCA” with a threshold of 90% considered “satisfy a first correlation threshold”, see page 180/ left column),
the real training data includes portions of the real training data associated with the one or more correlated principal components (a real-world photolithography data considered “real training data”, page 173/ right column/ first para, employed PCA in a real-world semiconductor manufacturing dataset collected, page 176/ left column/ second para, page 179/ left column/ second para);
generating the synthetic training dataset by filtering the dimensionality-reduced synthetic training data to include portions of the dimensionality-reduced synthetic training data (neural network “NN” dimensionally-reduction considered a form of filtering such as reducing the number of features in a dataset, see page 174/ left column/ second para, page 179/ left column/ second para. It is noted the output of NN for dimensionally reduction considered a type of synthetic data) that satisfy a second correlation threshold with the reference data (Table 5 shows Gaussian with a threshold satisfied a percentage of 90% considered “satisfy a second correlation threshold”, see page 180/ left column); and
generating metrology measurements for one or more run-time samples using the machine learning model and measurement data associated with the one or more run-time samples (predicts the metrology values and applied to real-world semiconductor manufacturing data “real data or samples” and “run-to-run“ considered “a type of runtime control”, page 174/ right column/ first 14 lines, machine learning-based methods “models”, page 178/ right column, first 3 lines, page 179/left column, lines 6-10).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to modify the teaching of Pandev generating real training dataset from metrology system and synthetic training dataset of machine learning as taught by Kim that would condense the fault detection and classification data and improve the performance of the virtual metrology model (Kim, Conclusion).
Pandev in view of Kim does not explicitly teach extracting the one or more correlated principal components associated with the real training data from the synthetic training data as dimensionality-reduced synthetic training data.
Sano teaches extracting the one or more correlated principal components associated with the real training data from the synthetic training data as dimensionality-reduced synthetic training data (principal component analysis “PCA” is a statistical method to extract intrinsic information from multivariate data by dimensional reduction, page 102/ sections A-B. A certain statistics or internal relationships of the original dataset, and synthetic data partially synthetic data and hybrid data “real data”, page 101/ right column first 4 lines. It is noted statistics considered “real data” or “original dataset” considered “real training data”).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to modify the teachings of Pandev and Kim to extract information by dimensional reduction as taught by Sano that would apply neural network for performing non-linear PCA to generate synthetic data.
As for Claim 2, Pandev in view of Kim and Sano teaches the metrology system of claim 1, Pandev does not teach wherein the dimensionality reduction operation comprises a principal component analysis. Kim teaches the dimensionality reduction operation comprises a principal component analysis (page 174/ left column/ second para). Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to modify the teaching of Pandev having dimensionally reduction operation includes a principal component analysis as taught by Kim that would condense the fault detection and classification data and improve the performance of the virtual metrology model (Kim, Conclusion).
As for Claim 3, Pandev in view of Kim and Sano teaches the metrology system of claim 1, Pandev teaches wherein the metrology measurement comprises at least one of an overlay measurement or a critical dimension measurement, [0038], [0074], [0121]-[0122].
As for Claim 4, Pandev in view of Kim and Sano teaches the metrology system of claim 1, Pandev teaches wherein the first metrology sub-system comprises an optical metrology tool (Fig 2, ML module 206 “first metrology subsystem” is a spectroscopy which is an optical metrology [0039]-[0040], [0104]).
As for Claim 5, Pandev in view of Kim and Sano teaches the metrology system of claim 1, Pandev teaches wherein the first metrology sub-system comprises at least one of a spectral ellipsometry tool or a spectral reflectometry tool (ellipsometer 101 considered “spectral ellipsometry [0007], [0040]).
As for Claim 7, Pandev in view of Kim and Sano teaches the metrology system of claim 1, Pandev further teaches wherein the second metrology sub-system comprises at least one of a transmission electron microscope, a transmission small-angle x-ray scattering tool, a scanning electron microscope, a critical dimension scanning electron microscope, or an atomic force microscope (a second metrology subsystem includes a transmission electron microscope or a scanning electron microscope, see [0104], [0040]). .
As for Claim 9, Pandev in view of Kim and Sano teaches the metrology system of claim 1, Pandev does not teach wherein at least one of the first correlation threshold or the second correlation threshold is an R2 value. Kim teaches at least one of the first correlation threshold or the second correlation threshold is an R2 value (the “statistical data” of each sensor value considered an R2 value, page 177/ section 2.4). Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to modify the teaching of Pandev using statistical data or R2 value as taught by Kim that would facilitate condensing the fault detection and classification data and improve the performance of the virtual metrology model (Kim, Conclusion).
As for Claim 10, Pandev in view of Kim and Sano teaches the metrology system of claim 1, Pandev does not teach wherein the first correlation threshold is equal to the second correlation threshold. Kim teaches the first correlation threshold is equal to the second correlation threshold (as shown in Table 5, PCA “first correlation threshold” and Gaussian “second correlation threshold” are equal to 90%). Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to modify the teaching of Pandev having the first and second correlation threshold as taught by Kim that would facilitate condensing the fault detection and classification data and improve the performance of the virtual metrology model (Kim, Conclusion).
As for Claim 11, Pandev in view of Kim and Sano teaches the metrology system of claim 1, Pandev does not teach wherein the second correlation threshold is greater to the second correlation threshold. Kim teaches the first correlation threshold is greater to the second correlation threshold (Table 8 shows “input space-based noise removal” considered “correlation threshold”, where input space-based noise removal of PCA “first correlation threshold” is 14 smaller than Gaussian “second correlation threshold” is 15). Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to modify the teaching of Pandev having the first and second correlation threshold as taught by Kim that would facilitate condensing the fault detection and classification data and improve the performance of the virtual metrology model (Kim, Conclusion).
Claim 13 is rejected for the same rationale as in claim 3.
Claim 14 is rejected for the same rationale as in claim 4.
Claim 15 is rejected for the same rationale as in claim 5.
Claim 17 is rejected for the same rationale as in claim 7.
Claim 19 is rejected for the same rationale as in claim 9.
Claim 20 is rejected for the same rationale as in claim 10.
Claim 21 is rejected for the same rationale as in claim 11.
9. Claims 6 and 16 are rejected under 35 U.S.C. 103 as being obvious over Pandev in view of Kim, Sano and further US 2020/0300790 of Gellineau et al., ”Gellineau” (of record).
As for Claim 6, Pandev in view of Kim and Sano teaches the metrology system of claim 1, Pandev teaches wherein the second metrology sub-system (Fig 2: error evaluation module 208 considered a second metrology subsystem), but the combination does not explicitly teach comprises at least one of a particle-beam metrology tool or an x-ray metrology tool. Gellineau teaches at least one of a particle-beam metrology tool or an x-ray metrology tool (metrology target considered a second metrology subsystem [0088], [0133]. Note: an error evaluation module is an integral part of metrology when dealing with targets). Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to modify the teachings of Pandev, Kim, Sano having a second metrology subsystem having beam as taught by Gellineau that would facilitate solving for values of a parametrized measurement model that minimize error between the measured scattered x-ray intensities and model results (Gellineau [0088]).
Claim 16 is rejected for the same rationale as in claim 6.
10. Claims 8 and 18 are rejected under 35 U.S.C. 103 as being obvious over Pandev in view of Kim, Sano and further US patent 9275484 of Wessman et al “Wessman” (of record).
As for Claim 8, Pandev in view of Kim and Sano teaches the metrology system of claim 1, but the combination does not teach wherein at least one of the first correlation threshold or the second correlation threshold is a goodness-of-fit threshold. Wessman teaches at least one of the first correlation threshold or the second correlation threshold is a goodness-of-fit threshold (a first goodness-of-fit 118 and a second goodness-of-fit 120 are “statistical models” considered “a goodness-of-fit threshold, see col 2 lines 31-40). Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to modify the teachings of Pandev, Kim, Sano having the correlation threshold is a good-of-fit threshold as taught by Wessman that would provide the goodness of fit based on the selected
error calculation and the identified fit type (Wessman, Abstract)
Claim 18 is rejected for the same rationale as in claim 8.
Conclusion
11. 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 extension fee 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.
12. Any inquiry concerning this communication or earlier communications from the
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/LYNDA DINH/Examiner, Art Unit 2857
/LINA CORDERO/Primary Examiner, Art Unit 2857
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