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 May 20, 2026 have been fully considered but they are not persuasive. Applicant argues on pages 5-6 that Pandev does not disclose or teach a DPU configured to “perform analysis on the data belonging to the second data type using local processing resources” and “provide results of the analysis performed on the data belonging to the second data type to the metrology equipment or adjusting the fabrication process”. The Examiner respectfully disagrees. First, Pandev (Fig. 3 and associated text) discloses a metrology system (i.e., PANDEV ,Fig. 3, 300)…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) (within item 300, ¶[0013]). Second, the 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. Examiner notes that almost anything can be configured (e.g. a chair can be configured to be a barricade by leaning the back of the chair up against a door just under the door knob). The Applicant’s DPU alone, does not “using the remote communication link, send data belonging to the first 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; perform analysis on the 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”. The Applicant is relying on “configured to” language rather than structure. Examiner notes that the Applicant’s DPU is not structurally configured, but rather “configured to” by providing an AI/machine learning algorithm that has instructions usually stored on a transitory or non-transitory storage medium. Examiner notes that DPU of Pandev can be “configured to” to perform the same functions claimed by the Applicant if provided the same AI algorithm. If the Applicant disagrees, the Examiner invites the Applicant to explain structurally why the DPU of Pandev could not do so. Lastly, does the Applicant have ownership of this AI/machine learning algorithm?
The Examiner takes the position the rejection is proper.
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.
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) (within item 300, ¶[0013]) 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 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 data that does not require local analysis and a second data type that comprises 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 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 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 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 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
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.
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 June 18, 2026
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