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 Amendments
Claims 16, 18, and 32 have been amended.
Claims 16-35 remain pending in the application.
The amendment filed 02/27/2026 is sufficient to overcome the 35 U.S.C. 101 rejections of claims 16-35. The previous rejections have been withdrawn.
The amendment filed 02/27/2026 is sufficient to overcome the 35 U.S.C. 102(a)(1) rejections of claims 16 and 18 as being anticipated by Vezer. The previous rejections have been withdrawn.
The amendment filed 02/27/2026 is sufficient to overcome the 35 U.S.C. 103 rejections of claims 17 and 19 over Vezer in view of Rho. The previous rejections have been withdrawn.
Argument 1, regarding the 35 U.S.C. 101 rejections, applicant argues that the rejections should be withdrawn because the claims are directed towards an improvement of performance of lithographic exposure control during a manufacturing process. Examiner agrees and the 35 U.S.C. 101 rejections have been withdrawn.
Argument 2, regarding the prior art rejections, applicant argues that Vezer does not teach “obtain a first list identifying imputed and non-imputed data entries within the imputed data” in claim 16. Applicant argues that Vezer is directed towards determining what text is best to potentially insert into a text string. Examiner respectfully disagrees because Vezer recites “identifying a plurality of points in the sequence at which missing or erroneous data is potentially to be imputed;” (See Vezer P0062). Under the broadest reasonable interpretation, the limitation “obtain a first list identifying imputed and non-imputed data entries within the imputed data” includes identifying a plurality of datapoints within data to potentially be imputed, with the plurality of datapoints being interpreted as a list.
Applicant also argues that Vezer does not teach “input the imputed data to an analyzer model configured to discriminate between imputed and non-imputed data entries of the imputed data and output a second list identifying imputed and non-imputed data entries of the imputed data” in claim 16. Applicant argues that the cited portion of Vezer is directed towards determining what text to impute, and not determining whether or not the text should be imputed. Examiner respectfully disagrees because P0064-P0065 of Vezer recites an associated probability with replacing erroneous data, meaning there is a chance the data will be imputed and a chance the data will not be imputed. Under the broadest reasonable interpretation of the claim language, a probability of replacing data is considered to be determining whether or not data will be imputed. Applicant also argues that the portion of Vezer is not directed towards a list of any kind. Examiner respectfully disagrees because the broadest reasonable interpretation of “list” includes selected data.
Applicant also argues that Vezer does not teach “receive target data correlating to the imputed data” in claim 32. Applicant argues that Vezer is directed towards determining what text is best to potentially insert into a text string. Examiner respectfully disagrees because Vezer recites “identifying a plurality of points in the sequence at which missing or erroneous data is potentially to be imputed;” (See Vezer P0062). Under the broadest reasonable interpretation, the limitation “obtain a first list identifying imputed and non-imputed data entries within the imputed data” includes identifying a plurality of datapoints within data to potentially be imputed, with the plurality of datapoints being interpreted as a list.
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.
Claims 16, 18, and 20-35 are rejected under 35 U.S.C. 103 as being unpatentable over Vezer in view of David (Pub. No.: US 20170109646 A1), hereafter David.
Regarding claims 16 and 18, Vezer teaches A method and computer program product comprising a non-transitory computer-readable medium having computer-readable instructions therein, the instructions, when executed by a computer system, configured to cause the computer system to at least: (“there is provided a computer program product comprising code embodied on computer-readable storage and configured so as when run on a computing apparatus to perform any of the methods disclosed herein”, P0073, “ there is provided a computer implemented method comprising automatically:”, P0013) receive input data for an imputer model to obtain imputed data, wherein the imputed data comprises at least a subset of the input data (“The result which the neural networks are trained to optimize is to select an imputation (i.e. the selected candidate element or elements) which when substituted into the portion of input data (e.g. input sentence), will result in an updated version of the input data portion (e.g. sentence) most likely to be correct”, P0046); obtain a first list identifying imputed and non-imputed data entries within the imputed data (“identifying a plurality of points in the sequence at which missing or erroneous data is potentially to be imputed;”, P0062); input the imputed data to an analyzer model configured to discriminate between imputed and non-imputed data entries of the imputed data and output a second list identifying imputed and non-imputed data entries of the imputed data (A set of paths is generated for each point, where each path has a corresponding score with probability to replace erroneous data, P0064-P0065); and configure the imputer model based on a comparison between the first list and the second list (In the last step, a comparison is made between different paths based on their probability scores, P0068. Training the model includes comparing the actual observed output with the training output, P0006).
Vezer does not appear to explicitly teach “automatically update, by a lithography control unit of a lithographic apparatus, at least one exposure control parameter for a subsequent lithographic exposure based on output generated by the configured imputer model”.
David teaches automatically update, by a lithography control unit of a lithographic apparatus, at least one exposure control parameter for a subsequent lithographic exposure based on output generated by the configured imputer model (lithographic exposure errors may be processed by a machine learning model in order to provide updates to parameters of a lithography apparatus in order to correct the errors, P0065-P0067).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Vezer and David before them, to include David’s specific teaching of inputting imputed data to a prediction model to obtain predicted data in Vezer’s system of imputation using a neural network. One would have been motivated to make such a combination of inputting imputed data to a prediction model to obtain predicted data (see David P0169-P0172, P0178) and using a binary classifier to predict classes of an input sequence, whether or not the data at that position should be imputed or not (see Vezer P0040-P0042) to improve process control techniques for lithography, yield prediction, and other aspects of semiconductor manufacturing processes (see David P0002).
Regarding claim 32, Vezer teaches A computer program product comprising a non-transitory computer- readable medium having computer-readable instructions therein, the instructions, when executed by a computer system, configured to cause the computer system to at least: (“there is provided a computer program product comprising code embodied on computer-readable storage and configured so as when run on a computing apparatus to perform any of the methods disclosed herein”, P0073) receive input data for an imputer model, the imputer model being a machine learning model and configured to provide imputed data (“The result which the neural networks are trained to optimize is to select an imputation (i.e. the selected candidate element or elements) which when substituted into the portion of input data (e.g. input sentence), will result in an updated version of the input data portion (e.g. sentence) most likely to be correct”, P0046); receive target data correlating to the imputed data (“identifying a plurality of points in the sequence at which missing or erroneous data is potentially to be imputed;”, P0062);… and train the imputer model based on a comparison between the predicted data and the target data (In the last step, a comparison is made between different paths based on their probability scores, P0068. Training the model includes comparing the actual observed output with the training output, P0006).
Vezer does not appear to explicitly teach “input the imputed data to a prediction model to obtain predicted data… automatically update, by a lithography control unit of a lithographic apparatus, at least one exposure control parameter for a subsequent lithographic exposure based on output generated by the configured imputer model”.
David teaches input the imputed data to a prediction model to obtain predicted data (Imputed data may be entered to a prediction model to obtain predicted data, P0169-P0172, P0178)… automatically update, by a lithography control unit of a lithographic apparatus, at least one exposure control parameter for a subsequent lithographic exposure based on output generated by the configured imputer model (lithographic exposure errors may be processed by a machine learning model in order to provide updates to parameters of a lithography apparatus in order to correct the errors, P0065-P0067).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Vezer and David before them, to include David’s specific teaching of inputting imputed data to a prediction model to obtain predicted data in Vezer’s system of imputation using a neural network. One would have been motivated to make such a combination of inputting imputed data to a prediction model to obtain predicted data (see David P0169-P0172, P0178) and using a binary classifier to predict classes of an input sequence, whether or not the data at that position should be imputed or not (see Vezer P0040-P0042) to improve process control techniques for lithography, yield prediction, and other aspects of semiconductor manufacturing processes (see David P0002).
Regarding claim 33, Vezer in view of David teaches the limitations of claim 32 as outlined above. David further teaches wherein the instructions are further configured to cause the computer system to configure the prediction model based on the comparison (Prediction model may be trained based on a comparison between the actual vs predicted overlay measurements, P0127).
Regarding claim 34, Vezer in view of David teaches the limitations of claim 33 as outlined above. David further teaches wherein the instructions configured to cause the computer system to configure the prediction model are configured to provide training of the prediction model based on the comparison (Prediction model may be trained based on a comparison between the actual vs predicted overlay measurements, P0127).
Regarding claims 26 and 20, Vezer teaches the limitations of claims 16 and 18 as outlined above. Vezer does not appear to explicitly teach “input the input data and/or target data correlating to the imputed data”.
David teaches input the input data and/or target data correlating to the imputed data (“Data from prior production runs can be used to create a model for a target parameter, and data from a current production run can be input to the model to generate a prediction for the target parameter, and to correlate the prediction with the actual data”, P0029).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Vezer and David before them, to include David’s specific teaching of inputting data to a model to generate a prediction for a target parameter and to correlate the prediction with actual data in Vezer’s system of imputation using a neural network. One would have been motivated to make such a combination of inputting data to a model to generate a prediction for a target parameter and to correlate the prediction with actual data (see David P0029) and using a binary classifier to predict classes of an input sequence, whether or not the data at that position should be imputed or not (see Vezer P0040-P0042).
Regarding claims 27, 21, and 35, Vezer in view of David teaches the limitations of claims 26, 20, and 32 as outlined above. David further teaches wherein the input data, imputed data, and target data are related to a lithographic patterning process (Input data, target data, and data to be imputed is related to a lithographic process, P0067-P0069, P0174, P0178).
Regarding claims 28 and 22, Vezer in view of David teaches the limitations of claims 26 and 20 as outlined above. David further teaches input the target data correlating to the imputed data and wherein the target data comprises yield data (“using these techniques to predict yield at any step of the process…Data from prior production runs can be used to.. correlate the prediction with the actual data.”, P0029).
Regarding claims 29 and 23, Vezer teaches the limitations of claims 16 and 18 as outlined above. Vezer does not appear to explicitly teach “wherein the input data comprises at least one selected from: levelling data, alignment data, and/or overlay data”.
David teaches wherein the input data comprises at least one selected from: levelling data, alignment data, and/or overlay data (“incorporating data analysis to make corrections on the lithographic apparatus for overlay error and critical dimension (CD) variation”, P0037).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Vezer and David before them, to include David’s specific teaching of imputing overlay error and critical dimension data in Vezer’s system of imputation using a neural network. One would have been motivated to make such a combination of imputing overlay error and critical dimension data (see David P0037) and using a binary classifier to predict classes of an input sequence, whether or not the data at that position should be imputed or not (see Vezer P0040-P0042).
Regarding claims 30 and 24, Vezer teaches the limitations of claims 16 and 18 as outlined above. Vezer does not appear to explicitly teach “wherein the imputed data comprises one or more selected from: overlay, critical dimension, and/or edge placement error”.
David teaches wherein the imputed data comprises one or more selected from: overlay, critical dimension, and/or edge placement error (“incorporating data analysis to make corrections on the lithographic apparatus for overlay error and critical dimension (CD) variation”, P0037).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Vezer and David before them, to include David’s specific teaching of imputing overlay error and critical dimension data in Vezer’s system of imputation using a neural network. One would have been motivated to make such a combination of imputing overlay error and critical dimension data (see David P0037) and using a binary classifier to predict classes of an input sequence, whether or not the data at that position should be imputed or not (see Vezer P0040-P0042).
Regarding claims 31 and 25, Vezer teaches the limitations of claims 16 and 18 as outlined above. Vezer does not appear to explicitly teach “wherein the imputer model is configured to provide input for a model configured to predict yield data”.
David teaches wherein the imputer model is configured to provide input for a model configured to predict yield data (“This disclosure describes new techniques for measuring and/or compensating for process variations in production runs of a semiconductor manufacturing processes, for using these techniques to predict yield at any step of the process, and for optimizing testing and burn-in procedures. For example, machine learning algorithms can be used to create new approaches to data analysis by incorporating new types of input data”, P0029…“machine learning algorithms can be used to predict yield”, P0045).
Claims 17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Vezer in view of David and further in view of Rho et al (Pub. No.: US 20220207300 A1), hereafter Rho.
Regarding claims 17 and 19, Vezer in view of David teaches the limitations of claims 16 and 18 as outlined above. Vezer does not appear to explicitly teach “wherein the imputer model and analyzer model are configured as a Generative Adversarial Network (GAN)”.
Rho teaches wherein the imputer model and analyzer model are configured as a Generative Adversarial Network (GAN) (“there is provided a generative adversarial network-based classification method using a classification system based on a generative adversarial network (GAN) configured of a generator, a discriminator, an actor, and a weighted function unit, the method comprising the steps of: a) generating a missing imputation value for a missing part among states from a labeled dataset, by the generator; b) predicting an action through a policy with the missing imputation value generated by the generator, by the actor; c) generating a weight value of a reward on the basis of a state replaced with the missing imputation value, the predicted action, and a label of the labeled dataset, by the weighted function unit…”, P0026).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Vezer, David, and Rho before them, to include Rho’s specific teaching of configuring an imputer and analyzer model as a GAN in Vezer’s system of imputation using a neural network. One would have been motivated to make such a combination of configuring an imputer and analyzer model as a GAN (see Rho P0026) and using a binary classifier to predict classes of an input sequence, whether or not the data at that position should be imputed or not (see Vezer P0040-P0042) to improve overall quality of data (see Rho P0046).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20170068169 A1 (Hung et al) recites a model configured to update parameters of a lithography tool.
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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/I.M./Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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