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 .
Allowable Subject Matter
Claims 11 – 14 and 20 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.
Response to Amendment
Applicant’s arguments with respect to claim(s) 1 – 10, 15 – 19 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4 – 10, 15 – 19 are rejected under 35 U.S.C. 103 as being unpatentable over MOS et al. US 2019/0011842 (hereinafter MOS) in view of LEE et al. US 2010/0324878 (hereinafter LEE).
Regarding claim 1, MOS teaches: A method of determining a correction strategy for a semiconductor manufacturing process (Fig. 3), the method comprising:
obtaining functional indicator data relating to one or more functional indicators associated with one or more process parameters of each of a plurality of different control regimes of the semiconductor manufacturing process and/or a tool associated with the semiconductor manufacturing process ([0048], [0053] - - determine quality metric; Fig. 1 - - a plurality of different control regimes of semiconductor manufacturing process);
using the functional indicator data as an input to a model to determine for which of the control regimes should a correction be determined so as to improve performance of the semiconductor manufacturing process according to at least one quality metric being representative of a quality of the semiconductor manufacturing process (Fig. 3, [005]-[0059] - - the measured quality value is used by a model to generate fingerprint); and
calculating the correction for the determined control regime(s) ([0059] - - calculate corrections using the fingerprint).
But MOS does not explicitly teach: a trained model.
However, LEE teaches: a trained model ([0091] - - train artificial intelligence module for determination of correction candidates).
MOS and LEE are analogous art because they are from the same field of endeavor. They all relate to control system.
Therefore before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by MOS, and incorporating a trained model, as taught by LEE.
One of ordinary skill in the art would have been motivated to do this modification in order to evaluate effectiveness of the correction, as suggested by LEE (Abstract).
Claim 15 is substantially similar to claim 1 and is rejected for the same reasons and rationale as above.
Regarding claim 4, the combination of MOS and LEE teaches all the limitations of the base claims as outlined above.
MOS further teaches: determining candidate correction strategies based on the functional indicators, wherein each candidate correction strategy relates to a different control regime or combination thereof; and using the trained model to select a preferred correction strategy from the candidate correction strategies ([0091] - - using the trained artificial intelligence module for determination of one or more correction candidates).
Regarding claim 5, the combination of MOS and LEE teaches all the limitations of the base claims as outlined above.
LEE further teaches: the preferred correction strategy is one determined by the trained model to have the highest probability of improving the at least one quality metric ([0072] - - rank the correction candidates based on the likelihood of success in improving a metric; [0070] - - using a model).
MOS and LEE are combinable for the same rationale as set forth.
Regarding claim 6, the combination of MOS and LEE teaches all the limitations of the base claims as outlined above.
LEE further teaches: the trained model is operable to rank the candidate correction strategies in terms of their respective probabilities of improving the at least one quality metric ([0072] - - rank the correction candidates based on the likelihood of success in improving a metric; [0070] - - using a model).
MOS and LEE are combinable for the same rationale as set forth.
Regarding claim 7, the combination of MOS and LEE teaches all the limitations of the base claims as outlined above.
LEE further teaches: the trained model comprises an output function operable to rank the candidate correction strategies into a probability distribution ([0072] - - rank the correction candidates based on the likelihood of success in improving a metric; [0070] - - using a model).
MOS and LEE are combinable for the same rationale as set forth.
Regarding claim 8, the combination of MOS and LEE teaches all the limitations of the base claims as outlined above.
MOS further teaches: performing a pre-processing step to select a model for making the prediction ([0087] - - selecting a distortion parameter model from a number of distortion parameter models).
LEE further teaches: grouping the candidate correction strategies into sets based on patterns in the functional indicator data, each set relating to a different trained model having been separately trained; ([0112] - - grouping a plurality of correction candidates; [0043] - - various models).
MOS and LEE are combinable for the same rationale as set forth.
Regarding claim 9, the combination of MOS and LEE teaches all the limitations of the base claims as outlined above.
However, LEE teaches: using a constraint solver to determine whether the candidate correction strategies and/or the selected correction strategy violate any design and/or actuation constraint or rule, and rejecting a candidate correction strategy if it does ([0067] - - design constraints aware; rejecting the implementation of a change if such a change violate a constraint).
MOS and LEE are combinable for the same rationale as set forth.
Regarding claim 10, the combination of MOS and LEE teaches all the limitations of the base claims as outlined above.
LEE further teaches: training the trained model to learn mapping between the candidate correction strategies and the at least one quality metric and/or one or more related metrics based on historic and/or simulated process parameter data ([0091] - - train the artificial neural network to find correlations between the metrics and the correction candidate; [0087] - - expert knowledge and experience utilized by the adaptive learning module is historic data).
MOS and LEE are combinable for the same rationale as set forth.
Regarding claim 16, the combination of MOS and LEE teaches all the limitations of the base claims as outlined above.
MOS further teaches: a lithographic apparatus comprising:
a support structure configured to support a patterning device, the patterning device configured to pattern a beam of radiation according to a desired pattern ([0003] - - lithographic apparatus; [0026] - - stage is a support structure);
a substrate table configured to hold a substrate ([0026] - - stage);
a projection system configured the project the patterned beam onto a target portion of the substrate ([0023] - - projection system); and
the computer program product of claim 15 ([0003] - - lithographic apparatus).
Regarding claim 17, the combination of MOS and LEE teaches all the limitations of the base claims as outlined above.
MOS further teaches: the at least one quality metric comprises a categorical indicator ([0053] - - the quality metric is overlay offset, critical dimension, focus or dose; each of these is a category, thus the quality metric comprises a categorical indicator).
Regarding claim 18, the combination of MOS and LEE teaches all the limitations of the base claims as outlined above.
MOS further teaches: the at least one quality metric comprises or relates to overlay and/or focus used in the semiconductor manufacturing process ([0041] - - overlay, focus).
Regarding claim 19, the combination of MOS and LEE teaches all the limitations of the base claims as outlined above.
LEE further teaches: the trained model is a regression type model or a neural network ([0090] - - neural network).
MOS and LEE are combinable for the same rationale as set forth.
Claims 2, 3 are rejected under 35 U.S.C. 103 as being unpatentable over MOS et al. US 2019/0011842 (hereinafter MOS) in view of LEE et al. US 2010/0324878 (hereinafter LEE) and further in view of WEIJDEN et al. WO 2019/185233 (hereinafter WEIJDEN).
Regarding claim 2, the combination of MOS and LEE teaches all the limitations of the base claims as outlined above.
But the combination of MOS and LEE does not explicitly teach: using a functional model to determine the functional indicator data based on process parameter data related to the one or more process parameters.
However, WEIJDEN teaches: using a functional model to determine the functional indicator data based on process parameter data related to the one or more process parameters ([0052] - - using a neural network model to relate process data to quality metric data).
MOS, LEE and WEIJDEN are analogous art because they are from the same field of endeavor. They all relate to control system.
Therefore before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of MOS and LEE, and incorporating using a functional model, as taught by WEIJDEN.
One of ordinary skill in the art would have been motivated to do this modification in order to improving controlling a manufacturing process, as suggested by WEIJDEN (Abstract).
Regarding claim 3, the combination of MOS, LEE and WEIJDEN teaches all the limitations of the base claims as outlined above.
WEIJDEN further teaches: the process parameter data comprises data relating to earlier exposures of more than one preceding substrate ([0033] - - historic data).
MOS, LEE and WEIJDEN are combinable for the same rationale as set forth.
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 YUHUI R PAN whose telephone number is (571)272-9872. The examiner can normally be reached Monday-Friday 8AM-5PM EST.
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/YUHUI R PAN/Primary Examiner, Art Unit 2116