This Office action is in response to amendment filed on 02/02/2026.
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
1. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/02/2026 has been entered.
Response to Amendment
2. Applicant’s amendments filed 02/02/2026 to the specification and claims are
accepted and entered.
Claims 1 and 20 have been amended.
Claim 10 has been canceled.
Claims 1-9 and 11-24 are examined.
Response to Argument
3. Applicant’s arguments filed 02/02/2026 regarding:
Note: Applicant does not need to amend the website/links on form PTO-892.
They are fine to list on PTO-892. However, the website/links should not be listed in the specification, i.e., listed in the form of “pdf”, “www”, or “https”. Please see MPEP 609.04(a) I. (under section 37 CFR. 1.98(b) last paragraph).
It is suggested to print out the PDFs (e.g., arxiv.org/pdf/1409.1556) and submit them in separate pages and input the title, author and publication date of the NPL on form PTO-892 under section Non-patent literature (NPL). This title, author, and date can be listed in the specification if Applicant refers to.
Please correct the specification, IDS, and form PTO-892 and resubmit them.
Applicant’s arguments regarding prior art have been fully considered, but they
are moot in view of new grounds of rejection as necessitated by applicant’s amendments.
Regarding claim 1, Applicant argues that “Izikson does not disclose or suggest a
neural network for transforming measurement data of at least one region of an element of a photolithography process and reference data associated with the measurement data into a quality measure of the element, in which the reference data and the reference data used for training purposes comprise at least one of: measured data of a defect-free region, simulated data of a defect-free region, design data, assessments of design data, or an aerial image of a defect-free region”. Izikson does not teach the "overlay error values" correspond to the "reference data" of claim 1.
In response, the examiner respectfully disagrees. Izikson discloses a photolithography tool used to form a structure, see [0028], layers between overlays to be measured, i.e., scanning electron microscope (SEM) used to measure overlay, SEM provides a form of measurement data such as SEM images, output values considered “quality measure data, see [0032], [0068]. In addition, each SEM image in the ROI 302 has a complementary (symmetric by design) image from the ROI 304, see [0034]. It is noted an SEM image with a complementary, symmetric-by-design (i.e., ROI 302) counterpart in another ROI (i.e., ROI 304) considered serving a defect-free reference image. In addition, Fig 8 shows if the error value below the threshold, “the threshold” is considered “reference data” or baseline used to compare, see [0061]. The overlay is measured on a specially designed target with built-in symmetry, see [0032], [0039]. Thus, Izikson does teach the reference data comprises measured data of a defect-free region.
Applicant further argues that blocks 808, 810, 812, and 814 in Fig 8 of lzikson are
not related to "measured data of a defect-free region, simulated data of a defect-free region, design data, assessments of design data, or an aerial image of a defect-free region."
In response, the examiner respectfully disagrees. Fig 8 of Izikson shows blocks 808-814 considered measured data, i.e., block 808: “obtain target quality metrics” considered “measured data”, see [0057]-[0059]. However, the measured data if measured on a specially designed target with built-in symmetry is considered measured data of a defect-free region, see [0032], [0039] as stated in the above response of (a).
Izikson does not disclose or suggest providing the SEM images, or analyses of
the SEM images, as input to the neural network. Therefore, in Izikson, the SEM images, or analyses of the SEM images, do not correspond to the "reference data" of claim 1.
In response, the examiner respectfully disagrees. As addressed in the previous office action, Izikson discloses a series of the SEM images are grabbed and analyzed from the consecutive junctions of inner and outer lines, see [0034]. This process is considered both an assessment of design data and a neural network input workflow.
Claim Objections
4. Claims 1-7, 11, 13 and 19-20 are objected to because of the following informalities:
Claim 1 lines 4-7, is suggested to add the comma “,” after “a model” and “including” before “reference data”, i.e., “a computer system comprising a model, that has been trained using a multiplicity of measurement data used for training purposes, including reference data associated with the measurement data used for training purposes and corresponding quality measures for training purposes”.
Further in lines 5, 8 recite “measurement data”, “reference data”,
lines 7, 9, 11, 13, 15, 20 recite “training purposes”, line 12 recites “training are…”,
line 14 recites “parameters”, line 17 recites “reference data”, “a quality measure”, and
lines 21-23 recite “a defect-free region”, “design data”,
the above limitations should read “the”, e.g., “the training purposes”, [[a]]the quality measure”, and so on.
Claim 2 recites “measurement data” should read “the measurement data”.
Claim 3 recites “measurement data”, “training purposes”, and “reference data” should read “the measurement data”, “the training purposes”, and “the reference data”,
Claim 4 recites “training purposes”, “measured data”, “simulated data”, “reference data”, and “design data” should read “the training purposes”, “the measured data”, “the simulated data”, “the reference data”, and “the design data”,
Claim 5 recites “training purposes”, “measured data”, “a defect-free region”, “measurement data of a region of the element” should read “the training purposes”, “the measured data”, “[[a]]the defect-free region”, “the measurement data of [[a]]the region of the element”.
Claim 6 recites “yes/no” should be whether “yes” or “no” or “yes” and “no”.
Claim 7 recites “design data” should read “the design data”.
Claim 11 recites “measurement data, “reference data” should read “the measurement data, “the reference data”.
Claim 13 recites “a quality measure of the element”, “the various regions” should read “[[a]]the quality measure of the element”, “[[the]] various regions”
Claim 19 recites “wherein the hyperparameter” should read “wherein the at least hyperparameter” for consistency.
Claim 20 is objected the same as stated above in claim 1. In addition, whether “the trained model” or “the model”, please be consistent.
Claims 1, 3, and 20 recite “corresponding quality measure” and “corresponding quality measures”, please be consistent the term “measure” as a singular or plural.
Claims 1 and 20 are missing the word “and” or “or” before the last “wherein clause”.
Appropriate correction is required.
Examiner note: It is noted “the model has been trained”, “the trained model”, and “the model after training” are the same. Please be consistent when reciting these terms.
Due to number of claim objections, the examiner has provided a number of examples of the claim deficiencies in the above objections, however, the list of deficiencies may not be all inclusion. Applicant should refer to theses as examples of deficiencies and should make all the necessary correction to eliminate the claim objections.
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 1-9 and 11-24 are rejected under 35 U.S.C. 101 as the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon,
or an abstract idea) without significantly more.
Regarding claims 1 and 20, the examiner submits that under Step 1 of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance) for evaluating claim for eligibility under 35 U.S.C. 101, the claims are apparatus and method which are the statutory categories of invention.
Regarding claim 1, continuing with the analysis, under Step 2A - Prong One of the test, the limitations (see Italic font below) of:
The limitation “An apparatus for transforming measurement data of at least one region of an element of a photolithography process and reference data associated with the measurement data into a quality measure of the element, the apparatus comprising: a computer system comprising a model that has been trained using a multiplicity of measurement data used for training purposes, reference data associated with the measurement data used for training purposes and corresponding quality measures for training purposes, wherein during each training cycle, measurement data and reference data used for training purposes are provided as input to the model in training, the model in training is used to generate a quality measure, the generated quality measure is compared with the corresponding quality measure for training purposes, and parameters of the model in training are adjusted based on a difference between the generated quality measure and the corresponding quality measure for training purposes, wherein the training is designed to determine parameters of the model to reduce the difference between the quality measure generated by the model and the corresponding quality measure for training purposes, wherein the model after training is configured to transform the measurement data and reference data associated with the measurement data into a quality measure of the element, said quality measure containing at least one information item about effects of the at least one region of the element when carrying out the photolithography process; wherein the reference data and the reference data used for training purposes comprise at least one of: measured data of a defect-free region, simulated data of a defect-free region, design data, assessments of design data, or an aerial image of a defect-free region” that fall into the grouping of mathematical concepts. Therefore, the claim recites a judicial exception under Step 2A - Prong One of the test.
Furthermore, under Step 2A - Prong Two of the test, this judicial exception is not integrated into a practical application. In particular, the additional elements recited in the claim (see above limitation in non-Italic font under Prong-One is pasted below):
“An apparatus for transforming measurement data of at least one region of an
element of a photolithography process and reference data associated with the measurement data into a quality measure of the element, the apparatus comprising: a computer system comprising a model that has been trained using a multiplicity of measurement data used for training purposes, reference data associated with the measurement data used for training purposes and corresponding quality measures for training purposes, wherein during each training cycle, measurement data and reference data used for training purposes are provided as input to the model in training” generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), add extra-solution activities (mere data gathering, i.e., measurement data) using elements recited at a high level of generality (see MPEP 2106.05(g)) and use a computer as a tool to perform an abstract idea (i.e., using computer/machine learning for transforming measurement data, see MPEP 2106.05(f)).
“said quality measure containing at least one information item about effects of the
at least one region of the element when carrying out the photolithography process; wherein the reference data and the reference data used for training purposes comprise at least one of: measured data of a defect-free region, simulated data of a defect-free region, design data, assessments of design data, or an aerial image of a defect-free region”, where the quality measure containing information item is mere data gathering in the photolithography process used for training purposes that is insignificantly extra-solution activity.
Accordingly, these additional elements, when considered individually and in combination, do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considering the claim as a whole. The claim is directed to a judicial exception under Step 2A of the test.
Additionally, under Step 2B of the test, claim 1 does not include additional elements that, when considered individually and in combination, is sufficient to amount to significantly more than the judicial exception because the additional element:
recite extra-solution activity (i.e., mere data gathering), adding insignificant extra-
solution activity to the judicial exception, see MPEP 2106.05(d).
generally linking the use of the judicial exception to a particular technological
environment or field of use, see MPEP 2106.05(h), i.e., an apparatus for transforming measurement data of at least one region of an element of a photolithography process and reference data associated with the measurement data into a quality measure of the element, i.e., using computer/machine learning for transforming measurement data, see MPEP 2106.05(f).
The claim, when considered as a whole, does not provide significantly more
under Step 2B of the test. Based on the analysis, the claim is not patent eligible.
Similarly, independent claim 20 is directed to a judicial exception (abstract idea) without significantly more as explained above with regards to claim 1.
Regarding the dependent claims 2-9, 11-19 and 21-24, they are also directed to the non-statutory subject matter because:
they just extend the abstract idea of the independent claims by additional
limitations (claims 5-9, 11-17), that under the broadest interpretation in light of the specification, cover performance of the limitations using mathematical concepts, and
the additional elements recited in the dependent claims, when considered
individually and in combination, refers to extra-solution activity and at a high level of generality, i.e., storing/recorded data (claims 2, 4, 21-24), and used machine learning to facilitate the application of the abstract idea (claims 3-5, 8, 11, 13, 17-19), which as indicated in the Office's guidance does not integrate the judicial exception into a practical application (Step 2A -Prong Two) and/or does not provide significantly more (Step 2B).
Claim Rejections - 35 USC § 112
7. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
8. Claims 1-9 and 11-24 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
(a) Claim 1 line 9 recites “the model in training” lacks antecedent basis. It is unclear whether it means “the model has been trained as recited in line 4? Further, in:
(b) line 13 recites “the training is designed” lacks antecedent basis. It is unclear whether it refers to “the training purposes” or “the training for the model”. For purpose of the examination, it is interpreted “the training for the model”.
(c) lines 14 and 15 recite “the model” lacks antecedent basis. It is unclear whether it means “the model has been trained” in line 4, or “the model in training” in line 9? It is interpreted “the model in training”.
(d) line 16 recites “wherein the model after training is configured to transform the measurement data” is indefinite. It is unclear the limitation because “the model for transforming measurement data is considered part of the ongoing process” rather than just a static “model after training”.
(e) line 18 recites “said quality measure” lacks antecedent basis. It is unclear whether it means “the quality measure of the element”, or “the generated quality measure”, or “the corresponding quality measure” in lines 12-13? It is interpreted “the quality model of the element”.
(f) line 20 recites “wherein (1) the reference data and (2) the reference data used for training purposes”, where (1) lacks antecedent basis and indefinite. It is unclear whether (1) means referring to (2)? For purpose of the examination, it is interpreted deleting (1).
Claims 8 and 17, recite “wherein the model for transforming” lacks antecedent basis. It is unclear whether Applicant refers to “the model has been trained”, or “the model in training” as recited in claim 1. In addition, claim 1 recites (1) “the model after training is configured to transform the measurement data”, and claims 8 and 17, recite (2) “the model for transforming the measurement data”, both (1) and (2) are not the same model because (1) represents predictor or transformer, and (2) represents the learner.
Claim 20 line 9 recites “said quality measure”, is rejected for the same above 8(f). Further, in:
lines 17 and 19-20 recite “the model in training”, are rejected for the same above 8(a).
line 21 recites “the training is designed”, is rejected for the same above 8(b), and
lines 22-23 recites “the model”, are rejected for the same above 8(c).
Dependent claims are rejected for the same reason as respective parent claim.
Examiner note: It is noted, claims 1 and 20 recite two models: (1) “a model in training” meaning on-going process, and (2) “a model has been trained, a trained model, and the model after training”, which represent a completed action, also represents “a static model” (not for continuous change).
It is suggested Applicant should recite clearly what steps are for the model in training (i.e., the model continues to learn and update to predict process evolution) and steps are for the model has been trained/ trained model (i.e., a static model) which is trained on measurement data/ fixed historical dataset/ and “freezes” meaning the data cannot be changed.
9. The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers
10. Claim 12 is rejected under 35 U.S.C. 112(d) as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 12 is improper dependent form because claim 12 recites “the apparatus of claim 10”, however, claim 10 is canceled. For purpose of examination, claim 12 depends on claim 11.
Applicant may cancel the claims, amend the claims to place the claims in proper dependent form, rewrite the claims in independent form, or present a sufficient showing that the dependent claims, complies with the statutory requirements.
Examiner note: Please correct antecedent basis based on its parent claim.
Claim Rejections - 35 USC § 102
11. 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 person shall be entitled to a patent unless -
(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.
12. Claims 1-9, 13-17, 20-21 and 24 are rejected under AIA 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated over US 2009/0063378 of Izikson (IDS of record).
As per Claim 1, Izikson teaches an apparatus for transforming measurement data of at least one region of an element of a photolithography process and reference data associated with the measurement data into a quality measure of the element ( photolithography tool used to form structure [0028], layers between overlays to be measured, i.e., scanning electron microscope “SEM” used to measure overlay, SEM provides a form of measurement data such as SEM images, is output values considered “quality measure data, see [0032], [0068] ), the apparatus comprising:
a computer system comprising a model that has been trained using a multiplicity of measurement data used for training purposes, reference data associated with the measurement data used for training purposes and corresponding quality measures for training purposes ( as stated above, in addition, Fig 8 shows measure overlay error values 806 considered “measurement data” and target quality metrics 808 considered “reference data”, and “alignment metric from lithography tool 810 considered “quality measure of the element, and 808-814 are considered “multiplicity of measurement data” which are inputted into neural network considered used for training purposes, see [0057]-[0060], i.e., a series of the SEM images are grabbed and analyzed from the consecutive junctions of inner and outer lines, see [0034]. This process is considered both an assessment of design data and a neural network input workflow ),
wherein during each training cycle, measurement data and reference data used for training purposes are provided as input to the model in training, the model in training is used to generate a quality measure, the generated quality measure is compared with the corresponding quality measure for training purposes, and parameters of the model in training are adjusted based on a difference between the generated quality measure and the corresponding quality measure for training purposes ( Fig 8 shows neural network “NN” 832 complete a full training cycle in steps 816-828, i.e., through many iterations and adjustments, NN 832 learns how to improve the accuracy of its prediction, see [0063], [0060]-[0062] ),
wherein the training is designed to determine parameters of the model to reduce the difference between the quality measure generated by the model and the corresponding quality measure for training purposes ( detecting the edges, comparing between the edge “quality measure” from the ROI 302 “corresponding quality measure”, and reducing the error to the measurement, see [0034]-[0036], [0066] ),
wherein the model after training is configured to transform the measurement data and reference data associated with the measurement data into a quality measure of the element, said quality measure containing at least one information item about effects of the at least one region of the element when carrying out the photolithography process (the NN predicts a set of inputs from a set of outputs and improves the accuracy of its
predictions through a process of iterative self-learning, is considered transforming the measurement data into a quality measure, see [0051], [0063] ),
wherein the reference data and the reference data used for training purposes comprise at least one of: measured data of a defect-free region, simulated data of a defect-free region, design data, assessments of design data, or an aerial image of a defect-free region (each SEM image in the ROI 302 has a complementary, symmetric by design image from the ROI 304, see [0034], It is noted an SEM image with a complementary, symmetric-by-design (i.e., ROI 302) counterpart in another ROI (i.e., ROI 304) considered serving a defect-free reference image. Fig 8 shows if the error value below “the threshold” which is considered “reference data” or baseline used to compare, see [0061]. The overlay is measured on a specially designed target with built-in symmetry, see [0032], [0039]. Thus, Izikson does teach the reference data comprises measured data of a defect-free region).
As per Claim 2, Izikson teaches the apparatus of claim 1, the measurement data of the at least one region comprise at least one of: the measurement data of the at least one region recorded with at least one measuring apparatus (see [0032]. It is noted scanning electron microscope “SEM” records data ), the measurement data of the at least one region stored in a non-volatile memory device (Fig 10, computer 1028, [0070], memory modules configured to store data, [0081], [0067]. It is noted a computer hard drive “HDD”, i.e., ROM, is a non-volatile memory), or measurement data of the at least one region obtained via a data connection.
As per Claim 3, Izikson teaches the apparatus of claim 1, wherein the corresponding quality measures are produced based on measurement data used for training purposes and reference data associated with the measurement data used for training purposes (comparing predicted output data with “desired output values” for training, [0060], [0062]).
As per Claim 4, Izikson teaches the apparatus of claim 2, wherein the measurement data used for training purposes comprise at least one of: measured data recorded with the at least one measuring apparatus or simulated data (see [0032]. It is noted scanning electron microscope “SEM” can record data), and/or wherein reference data associated with the measurement data used for training purposes comprise at least one of: measured reference data recorded with the at least one measuring apparatus (see [0062], [0081]), simulated reference data, or design data.
As per Claim 5, Izikson teaches the apparatus of claim 1, wherein the measurement data used for training purposes comprise at least one of: measurement data of a defect-free region of the element, measurement data of a region of the element with at least one repaired defect, measurement data of a region of the element with at least one incompletely repaired defect, or measurement data of a region having at least one defect (the target 650 is defective because of the asymmetry, the defect is referred to as a systematic error, [0044]).
As per Claim 6, Izikson teaches the apparatus of claim 1, wherein the quality measure comprises at least of: a difference image between an image of the region of the element and a reference image of the region of the element (Fig 3A shows region of interest “ROI” 302 considered “a reference image of the region” and ROI 304 considered “the image of region of element”, ROIs 302 and 304 are different images as shown by arrows, see [0034]-[0036]), a qualified defect map of the region of the element, which contains at least one information item about effects of the defects specified in a defect map when carrying out the photolithography process, or a yes/no statement whether the region of the element can be used in the photolithography process.
As per Claim 7, Izikson teaches the apparatus of claim 1, further comprising a predictor (NN 832 being trained to predict considered “predictor”, [0060] ) operable to at least one of: to generate assessments of design data or to decide whether at least one defect is present in the at least one region of the element based on the assessments of the design data and the measurement data of the at least one region (NN 832 uses a variety of overlay measurements techniques to generate the overlay error values, see [0060], a series of SEM images are analyzed from the consecutive junctions of inner and outer lines considered “assessment of design data”, see [0034]-[0035], Fig 1 shows analyzing wafer generating, i.e., “target quality metrics” as specific measurements used to assess the quality of wafer, for training and decide the outcomes, see also Figs 8-9).
As per Claim 8, Izikson teaches the apparatus of claim 1, wherein the model for transforming the measurement data is fitted to the quality measure (Fig 1: NN 102 transforms the measurement data, i.e., target quality metrics 106 into output values, i.e., predicted output values 114, see [0058], [0064]-[0067], Fig 7 shows a transformation of inputs to outputs within NN 700, [0052]-[0056]).
As per Claim 9, Izikson teaches the apparatus of claim 2, wherein the apparatus comprises the at least one measuring apparatus (overlay measurement system, metrology tool 1020, [0068]), and/or wherein the at least one measuring apparatus comprises at least one of: “a scanning particle microscope” (refers to a scanning electron microscope “SEM”, [0068]), a scanning probe microscope, or an interferometer.
As per Claim 13, Izikson teaches the apparatus of claim 1, further operable to transform the quality measure of the at least one region into a quality measure of the element by considering the quality measures of the various regions of the element (the regions of interest “ROIs” 202 and 204 are identified around the center of symmetry “COS” which is a symmetry element considered “quality measure of the element”, [0033]-[0034]).
As per Claim 14, Izikson teaches the apparatus of claim 1, wherein the apparatus comprises a scanning electron microscope, which is embodied to scan the element of the photolithography process, and which is further embodied to repair the at least one defect of the element of the photolithography process (the target is correctly positioned in the X,Y,Z directions, i.e., to place the target in focus and to position the target as closes as possible to the center of the field of view of the metrology tool, [0073]).
As per Claim 15, Izikson teaches the apparatus of claim 1, wherein the element of the photolithography process comprises at least one of: a photolithographic mask (photolithographic process, such as process metrics. It is noted photolithography mask, also called a photomask or “reticle”, see [0049]-[0050]) or a template for a nano-imprint technology.
As per Claim 16, Izikson teaches the apparatus of claim 1, wherein the region of the element comprises at least one of: a region with at least one defect (the target 650 is defective because of the asymmetry, [0044], [0034]), a region with at least one repaired defect, or a region with at least one incompletely repaired defect.
As per Claim 17, Izikson teaches the apparatus of claim 1, wherein the model for transforming the measurement data comprises a machine learning model (Neural network is a machine learning, see [0014]).
Claim 20 is rejected for the same rational as in claim 1.
As per Claim 21, Izikson teaches the method of claim 20, further comprising recording measurement data of the at least one region with at least one measuring apparatus for checking the at least one region (It is noted SEM can record data. Fig 2 shows aerial image of region of interest “ROI”, [0032], the ROIs 202 and 204 are identified, [0033]-[0034]).
As per Claim 24, Izikson teaches a computer program stored on a non-volatile memory and having instructions to cause a computer system to perform the method steps of claim 20 (a computer comprises processor and memory device [0081]. It is noted a computer hard drive “HDD”, i.e., ROM, is a non-volatile memory).
Claim Rejections - 35 USC § 103
13. 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.
14. Claims 11-12 and 22 are rejected under AIA 35 U.S.C. 103 as being obvious over Izikson in view of Budach et al, “Budach” (US 2014/0165236- ids of record).
As per Claim 11, Izikson teaches the apparatus of claim 1, Izikson does not explicitly teach the apparatus is operable to transform measurement data in an aerial image and reference data in a reference aerial image. Budach teaches transforming measurement data in an aerial image and reference data in a reference aerial image (EUV lithography system images considered “aerial images”. The simulation data of Fig 11, i.e., repairing diameter correct defect 1030 considered “transform the measured data”, [0151]-[0154], each defect stored in library associated with repairing form considered “reference aerial image”, [0165]-[0166]). It would have been obvious to one ordinary skill in the art at the time before the effective filing date of claimed invention to modify the teaching of Izikson to repair or correct defect data in an aerial image as taught by Budach that would compensate defects which significantly depart from a circular shape (Budach, [0163]).
As per Claim 12, Izikson teaches the apparatus of claim 10, Izikson does not explicitly teach the aerial image comprises at least one of: a measured aerial image, a simulated aerial image, or an aerial image focus stack, and/or wherein the reference aerial image comprises at least one of: a measured reference aerial image, a simulated reference aerial image, or a reference aerial image focus stack. Budach teaches a measured aerial image, a simulated aerial image, or an aerial image focus stack (simulating an aerial image 1540 of Fig 15, [0064], [0082]), and/or wherein the reference aerial image comprises at least one of: a measured reference aerial image, a simulated reference aerial image, or a reference aerial image focus stack (a measured aerial image ([0144], [0165]-[0166], [0064], [0082]). It would have been obvious to one ordinary skill in the art at the time before the effective filing date of claimed invention to modify the teaching of Izikson having simulated aerial images as taught by Budach that would compensate defects which significantly depart from a circular shape (Budach, [0163]).
As per Claim 22, Izikson teaches the method of claim 20, Izikson does not further teach comprising repairing the at least one defect using a repair tool. Budach teaches repairing the at least one defect using a repair tool ([0094], [0111], [0143] - Merit™ system of Zeiss is considered a repairing tool on semiconductor wafers. It would have been obvious to one ordinary skill in the art at the time before the effective filing date of claimed invention to modify the teaching of Izikson having repairing tool of Zeiss as taught by Budach that would compensate defects which significantly depart from a circular shape (Budach, [0163]).
15. Claims 18-19 are rejected under AIA 35 U.S.C. 103 as being obvious over Izikson in view of Liu (US 2018/0314163 – of record).
As per Claim 18, Izikson teaches the apparatus of claim 17, Izikson does not explicitly teach the machine learning model comprises at least one hyperparameter. Liu teaches the machine learning model comprises at least one hyperparameter (see [0072] It would have been obvious to one ordinary skill in the art at the time before the effective filing date of claimed invention to modify the teaching of Izikson having the machine learning model includes a hyperparameter as taught by Liu that would provide design variables of a lithographic projection apparatus and minimize the cost function (Liu, [0031], [0044]).
As per Claim 19, Izikson in view of Liu teaches the apparatus of claim 18, Liu further teaches wherein the hyperparameter comprises at least one of: a mask type, see [0092], an exposure wavelength (i.e., wavelength of 365 nm considered exposure wavelength, [0036]), a numerical aperture (NA) of a scanner objective ([0023], [0043]), or an exposure setting of the scanner (exposure tool, [0032], i.e., using deep UV for exposure, [0078]. It is noted exposure tool refers to the mechanism or setting that controls “adjusts” the amount of light to expose to). It would have been obvious to one ordinary skill in the art at the time before the effective filing date of claimed invention to modify the teaching of Izikson having exposure wavelength or an exposure tool as taught by Liu that would provide design variables of a lithographic projection apparatus and minimize the cost function (Liu, [0032], [0044]).
16. Claim 23 is rejected under AIA 35 U.S.C. 103 as being obvious over Izikson in view of Bode et al “Bode” (US patent 6737208 – of record).
As per Claim 23, Izikson teaches the method of claim 20, Izikson does not teach further comprising releasing or rejecting the element for the photolithography process for operation based on the quality measure. Bode teaches rejecting the element for the photolithography process for operation based on the quality measure (col 8 lines 25-51). It would have been obvious to one ordinary skill in the art at the time before the effective filing date of claimed invention to modify the teaching of Izikson to reject the element for the photolithography process as taught by Bode that would provide the selection of the outlier rejection boundary helps ensure that only the
points that are significantly outside the normal operating conditions of the process are rejected (Bode, col 8 lines 48-51).
Conclusion
17. Any inquiry concerning this communication or earlier communications from the
examiner should be directed to LYNDA DINH whose telephone number is (571) 270-
7150. The examiner can normally be reached on M-F 10 AM - 6 PM ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Arleen M Vazquez can be reached on 571-272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the
Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only.
For more information about the PAIR system, see https://ppairmy.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LYNDA DINH/Examiner, Art Unit 2857
/LINA CORDERO/Primary Examiner, Art Unit 2857
Prosecution Insights