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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 20, 2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-10 have been considered but are moot because the new ground of rejection and interpretation of prior art.
Applicant's arguments filed January 20, 2026 have been fully considered but they are not persuasive. Applicant argues on pages 5-6 that Pandev does not disclose or teach “metrology equipment that formats raw measurement data into distinct data types base on whether the data requires local analysis or not”… “sending raw measurement data to an external system without performing analysis prior to sending based on a categorization that the data does not require local analysis”. The Examiner respectfully disagrees. First, the Applicant does not teach or disclose “metrology equipment that formats raw measurement data into distinct data types base on whether the data requires local analysis or not” nor “sending raw measurement data to an external system without performing analysis prior to sending based on a categorization that the data does not require local analysis”. Second, the Applicant’s original disclosure does not have support for the recitation/phrase “raw measurement data” which is pointed out in the rejection below. Examiner notes that Pandev discloses the Applicant’s claimed invention, but need not disclose “metrology equipment that formats raw measurement data into distinct data types base on whether the data requires local analysis or not” or “sending raw measurement data to an external system without performing analysis prior to sending based on a categorization that the data does not require local analysis”, which has no support in the Applicant’s original disclosure. The Examiner takes the position the rejection is proper.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1 and 6 both recite “raw measurement data”. There is no support in the Applicant’s original disclosure for this recitation. Applicant only has support for “data”. Applicant also has not given a special definition to the term “data”. Therefore, for the purpose of examination, the Examiner has taken “data” and/or “raw measurement data” to be fundamental (quantitative [categorical, numerical], binary date/time data]), predetermined data (integer, float/decimal, string, Boolean), analyzed/calculated data (aggregated, descriptive statistics, calculated metrics/KPIs, modeled/predicted, banded or metadata), structured, unstructured, transactional or geospatial data.
Claims 2-5 and 7-10 inherit these deficiencies due to their dependency. Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 2, 4, 6, 7 and 9 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pandev et al. (PANDEV) (US 10,365,225 B1).
Re: Independent Claim 1 (Currently Amended), PANDEV discloses a metrology system (i.e., PANDEV ,Fig. 3, 300), comprising: metrology equipment configured to generate a stream of raw measurement data relating to inspected wafers, the metrology equipment configured to format raw measurement data into at least a first data type the comprises raw measurement data that does not require local analysis and a second data type that comprises raw measurement data that requires local analysis for adjusting fabrication (PANDEV ¶¶[0006]-[0007] disclose metrology equipment to collect raw measurement data from wafer for training an input-output Signal Response Model (SRM). The input data corresponds to a first data type based on raw measurement collection data, and the output data corresponds to a second data type used to perform measurements based on the raw measurement collection data);
a remote communication link configured to communicate with an external system (PANDEV, Fig. 3, 330 ¶[0013] disclose one or more computing system communicatively coupled to the spectrometer 304 to receive measurement data);
a local communication link (PANDEV, Fig. 3, 330 ¶[0013] disclose a spectrometer 304 controlled directly by a computer system coupled suitable to 330 ; and
a data processing unit (DPU) configured to:
using the remote communication link, send the data belonging to the raw measurement data type directly to the external system without performing analysis, using local processing resources, on the raw measurement data belonging to the first data type prior to sending to the external system (PANDEV ¶¶ [0006]-[0007] and [0013] expressly describes forwarding raw measurement data for training and external model generation and communicating measurement data between local equipment and external computing system(s). The Examiner finds that these disclosures teach the DPU sending the first data type (raw measurement data) to an external system via a remote communication link and that the data is not analyzed locally prior to transmission, as evidenced by PANDEV’s teaching, ¶¶ [0006]–[0007], of transferring raw measurement data to external systems for SRM training and model generation.);
perform analysis on the raw measurement data belonging to the second data type using local processing resources, and, using the local communication link, provide results of the analysis performed on the raw measurement data belonging to the second data type to the metrology equipment for adjusting the fabrication process (PANDEV, Fig. 3, 330 ¶[0013]) disclose performing analysis (e.g., applying SRMs) locally and providing results/feedback to metrology equipment or control systems, which corresponds to the claimed DPU performing analysis on the second data type using local processing resources and providing analysis results via a local communication link).
Examiner notes that these are device claims which deal with structure. Applicant needs to claim the structure of the metrology equipment, remote communication link, the local communication link and the DPU in in order perform the “configured to” language above.
Re: Claim 2, PANDEV disclose(s) all the limitations of claim 1 on which this claim depends. PANDEV further discloses: wherein the DPU is configured to perform the analysis on the data belonging to the second data type using a machine learning algorithm (PANDEV, Fig. 3, 330 ¶¶[Abstract], [0001] disclose a computing system that uses a trained input-output (SRM) to perform analysis based on reference measurement data collected).
Re: Claim 4, PANDEV disclose(s) all the limitations of claim 1 on which this claim depends. PANDEV further discloses: wherein the second data type comprises one or more of: near-line secondary ion mass spectrometry; transmission electron microscopy; optical critical dimension metrology; optical overlay metrology; E-beam overlay metrology; and optical defect inspection (PANDEV, Fig. 1, 102 ¶¶[0015]-[0016] disclose reference parameter values of a metrology target including those of optical critical dimension).
Re: Independent Claim 6 (Currently Amended), PANDEV discloses a metrology method (PANDEV, 100), comprising: using metrology equipment, generating a stream of raw measurement data relating to inspected wafers, the metrology equipment configured to format the generated data into first and second data types, raw measurement data into at least a first data type the comprises raw measurement data that does not require local analysis and a second data type that comprises raw measurement data that requires local analysis for adjusting fabrication (PANDEV Fig. 3 and ¶¶[0006]-[0007] disclose metrology equipment to collect raw data from wafer for training an input-output Signal Response Model (SRM). The input data corresponds to a first data type based on raw measurement collection data, and the output data corresponds to a second data type used to perform measurements based on the raw measurement collection data); using a remote communication link, communicating with an external system (PANDEV, Fig. 3, 330 ¶[13] disclose one or more computing system communicatively coupled to the spectrometer 304 to receive measurement data);
using the remote communication link with a data processing unit (DPU), sending the raw measurement data belonging to the first data type directly to the external system without using local processing resources to perform analysis on the data belonging to the first data type. without performing analysis, using local processing resources, on the raw measurement data belonging to the first data type prior to sending to the external system (PANDEV, Fig. 3, 330 ¶¶ [0006]-[0007] and [13] disclose a spectrometer 304 controlled directly by a computer system coupled suitable to 330 and raw data is transmitted without prior local analysis, corresponding to sending the first data type directly without local processing); and
performing analysis on the raw measurement data belonging to the second data type using local processing resources, and, using a local communication link, providing results of the analysis performed on the raw measurement data belonging to the second data type to the metrology equipment for adjusting the fabrication process (PANDEV, Fig. 3, 330 ¶[0013]) disclose a computing system of one or more processors that receive and/or acquire data to the first data type corresponding to measurement data performing analysis and supply results from the spectrometer. PANDEV further discloses sending the results of this local analysis back to the metrology equipment via a local communication link).
Re: Claim 7, PANDEV disclose(s) all the limitations of claim 6 on which this claim depends. PANDEV further discloses: wherein performing the analysis on the data belonging to the second data type comprises using a machine learning algorithm (PANDEV, Fig. 3, 330 ¶¶[Abstract][0001] disclose a computing system that uses a trained SRM model to perform analysis based on reference measurement data collected).
Re: Claim 9, PANDEV disclose(s) all the limitations of claim 6 on which this claim depends. PANDEV further discloses: wherein the second data type comprises one or more of: near-line secondary ion mass spectrometry; transmission electron microscopy; optical critical dimension metrology; optical overlay metrology; E-beam overlay metrology; and optical defect inspection (PANDEV, Fig. 1, 102 ¶¶[0015]-[0016] disclose reference parameter values of a metrology target including those of optical critical dimension).
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.
Claim(s) 3 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pandev et al. (PANDEV) (US 10,365,225 B2) in view of Fujimura et al (FUJIMURA) (US 2022/0128899 A1).
Re: Claim 3, PANDEV discloses the system according to claim 2.
PANDEV is silent regarding the machine learning algorithm is a digital twin type of neural network (NN).
FUJIMURA teaches a digital twin type of neural network (NN) (FUJIMURA, ¶¶[0066][0135])teaches machine learning calculations that can include determining manufacturable shapes for the logic gates, transistors, metal layers, and other items that are required to be found in a physical design such as that of an integrated circuit on a wafer. (FUJIMURA, fig 7 ¶[0056] further teaches steps, of measurement, specifically shape combinations that may be combined and substituted with a digital twin type of neural network to replicate physical entities by modeling the properties conditions and attributes of their real-world counterparts.
Therefore, it would have been obvious by one skilled in the art before the effective filing date to incorporate the use of digital Twin type of neural network to the system of PANDEV, since FUJIMURA teaches that simulation results may train a neural network resulting in an (NN) digital twin that performs much faster than with simulation alone (FUJIMURA, ¶[0066][0135]).
Re: Claim 8, PANDEV disclose the method according to claim 7, wherein the machine learning algorithm is a digital twin type of neural network (NN).
PANDEV is silent regarding the machine learning algorithm is a digital twin type of neural network (NN) in the method.
FUJIMURA teaches a digital twin type of neural network (NN) method. (FUJIMURA, ¶¶[0066][0135])teaches machine learning calculations that can include determining manufacturable shapes for the logic gates, transistors, metal layers, and other items that are required to be found in a physical design such as that of an integrated circuit on a wafer. (FUJIMURA, fig 7 ¶[0056] further teaches steps, of measurement, specifically shape combinations that may be combined and substituted with a digital twin type of neural network to replicate physical entities by modeling the properties conditions and attributes of their real-world counterparts.
Therefore, it would have been obvious by one skilled in the art before the effective filing date to incorporate the use of digital Twin type of neural network to the method of PANDEV, since FUJIMURA teaches that simulation results may train a neural network resulting in an (NN) digital twin that performs much faster than with simulation alone (FUJIMURA, ¶[0066][0135]).
Claim(s) 5 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pandev et al. (PANDEV) (US 10,365,225 B2) in view of SAH et al (SAH) (US 20180/321168A1).
Re: Claim 5, PANDEV discloses the system according to claim 1.
PANDEV is silent regarding the first data type comprising one or more of: defect clusters on wafer maps; outliers; and images of electrical power pins.
SAH teaches data type comprising one or more of: defect clusters on wafer maps; outliers; and images of electrical power pins, specifically defect clusters on wafer maps. (SAH, Fig. 4, ¶[0061] teaches the use of an optical inspection wafer map. SAH further teaches sampling of 5000 defects caught by SEM review and classification).
Therefore, it would have been obvious to one skilled in the art before the effectively filing date to incorporate optical inspection wafer maps to the classification of PANDEV’s system, since SAH teaches that a random sampling would not detect killer defects, however defect distribution is caught with the wafer mapping because the metrology data would focus/shape wafer review sampling plan to the edge of the wafer, rather than the center, where defects are more likely to be caught (SAH, ¶[0061).
Re: Claim 10, PANDEV discloses a method according to claim 6.
PANDEV is silent regarding a method wherein the first data type comprises one or more of: defect clusters on wafer maps; outliers; and images of electrical power pins.
SAH teaches a method, wherein the first data type comprises one or more of: defect clusters on wafer maps; outliers; and images of electrical power pins, specifically defect clusters on wafer maps. (SAH, [0061] teaches a method wherein an optical inspection wafer map is used. SAH further teaches sampling of 5000 defects caught by SEM review and classification).
Therefore, it would have been obvious to one skilled in the art before the effectively filing date to incorporate optical inspection wafer maps to the classification in the method portion of PANDEV’s system, since SAH teaches that a random sampling would not detect killer defects, however defect distribution is caught with the wafer mapping because the metrology data would focus/shape wafer review sampling plan to the edge of the wafer, rather than the center, where defects are more likely to be caught (SAH, ¶[0061).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TELLY D GREEN whose telephone number is (571)270-3204. The examiner can normally be reached M-F 8am-5pm.
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TELLY D. GREEN
Examiner
Art Unit 2898
/TELLY D GREEN/Primary Examiner, Art Unit 2898 February 20, 2026