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 07/13/2026 have been fully considered but they are not persuasive.
Regarding rejection on claim 1, applicant argued Cone’s “non-iterative” reconstruction is fundamentally different because it is mathematical, but not a learned mapping performed by a trained network. Applicant then argued Li fails to teach non-iteratively determining a complex-valued field as the output of a trained network based on a series of images for which a measurement parameter is varied. Applicant further argued combination of prior arts lack proper motivation.
However, examiner respectfully disagrees. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Although Coene does not teach using a trained network, utility of non-iteratively determining complex-valued field relating to a feature of a lithography target structure on a substrate is taught. As secondary reference, Li teach using a trained network (ANN) to output complex-valued field (paragraph 0076). Li furthers the trained ANN to comprises at least one of multilayer perceptron (MLP) model and a convolutional neural network (CNN) model (paragraphs 0008-0009). Output of either MLP or CNN is inherently direct (non-iterative) because both are feed-forward. So, Li does teach non-iteratively determining a complex-valued field as the output of a trained network. Despite applicant’s argument on Li using near field images (MI3D) from think mask images (MI2D), argued claim does not specifically limit using mask images as input to the trained network. The argued claim merely indicates that training data being relating to a series of holographic measurements of one or more training structures. One of ordinary skill in the art would have recognized both Coene and Li teach direct or non-iterative approach in determining complex-valued field relating to a feature of a lithography target structure on a substrate, wherein a trained network may provide more reliable result. Kaushal teach optimizing performance of a manufacturing tool by training self-learning and self-improving network from optical measurements (paragraphs 0060, 0092), which would have been obvious to one of ordinary skill in the art to recognize that Li’s training data for the trained network should be from holographic measurements because of reliability. So, one of ordinary skill in the art before the effective filing date of the claimed invention would have obviously recognized and incorporated teachings of Li and Kaushal into the system of Coene, in order to provide improved efficiency and reliability through a trained network for image analysis.
Thus, rejection is proper and maintained.
Claim Rejections - 35 USC § 103
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) 1-6, 8-16, 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Coene et al. (US2017/0261428) in view of Li et al. (US2018/0341173) and Kaushal et al. (US2010/0138026).
To claim 1, Coene teach in a lithography or metrology system, a method of determining a complex-valued field relating to a feature of a lithography target structure on a substrate, comprising:
obtaining image data relating to a series of images of the lithography target structure, for which at least one measurement parameter is varied over the series of images of the target structure (Fig. 4, paragraphs 0045-0050, 0070-0077);
non-iteratively determining the complex-valued field relating to the feature of the lithography target structure (paragraphs 0051, 0066-0067).
But, Coene do not expressly disclose obtaining a trained network operable to map the series of images of the lithography target structure to a corresponding complex-valued field; inputting the image data into the trained network; and determining the complex-valued field relating to the feature of the lithography target structure as the output of the trained network, wherein the trained network has been trained on training data relating to a series of holographic measurements of one or more training structures, for which at the least one measurement parameter is varied over the series of holographic measurements.
Li teach a method of using an artificial neural network (ANN) to determine near field images for optical lithography (abstract), comprising: obtaining a trained network operable to map the series of images of the lithography target structure to a corresponding complex-valued field; inputting the image data into the trained network (Figs. 3A-C; Fig. 4, paragraphs 0048-0065); and determining the complex-valued field relating to the feature of the lithography target structure as the output of the trained network (paragraphs 0033, 0068, 0076, processing through an ANN to obtain a complex-valued near field image, wherein MLP and CNN are feed-forward or direct output approach).
Corresponding to Li, Kaushal teach a self-learning and self-improving semiconductor manufacturing tool (abstract, Figs. 11A-B, 17-23), wherein the trained network has been trained on training data relating to a series of holographic measurements of one or more training structures (paragraphs 0058, 0096), for which at the least one measurement parameter is varied over the series of holographic measurements (optical measurements, paragraphs 0059-0060, 0092, 0114, 0121, 0145, holographic measurement as a form of optical measurement is well-known in the art, hence Official Notice is taken on holographic measurement utilization).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teachings of Li and Kaushal into the system of Coene, in order to provide with machine learning an efficient and reliable trained network for image analysis.
To claim 11, Coene, Li and Kaushal teach a metrology apparatus configured to determine a characteristic of a feature of a lithography target structure manufactured on a substrate (as explained in response to claim 1 above).
To claims 2 and 12, Coene, Li and Kaushal teach claims 1 and 11.
Coene, Li and Kaushal teach wherein the image data has been obtained from an unreferenced optical measurement (Kaushal, paragraphs 0058, 0096, unlabeled data set for training).
To claims 3 and 13, Coene, Li and Kaushal teach claims 2 and 12.
Coene, Li and Kaushal teach wherein the unreferenced optical measurement was performed using a holographic apparatus for which a reference branch was disabled (obvious due to unreferenced data being obtained).
To claims 4 and 14, Coene, Li and Kaushal teach claims 1 and 11.
Coene, Li and Kaushal teach further comprising performing one or more optical measurements to obtain the image data (Kaushal, paragraph 0060).
To claims 5 and 15, Coene, Li and Kaushal teach claims 1 and 11.
Coene, Li and Kaushal teach wherein the trained network is a neural network or an auto-encoder/decoder network (Li, paragraphs 0007-0009).
To claims 6 and 16, Coene, Li and Kaushal teach claims 1 and 11.
Though Coene, Li and Kaushal do not expressly disclose wherein an apparatus used to obtain the image data and an apparatus used to obtain the training data is a similar or the same holographic apparatus, comprising a reference branch for providing reference radiation; and wherein: the image data is obtained from unreferenced optical measurements performed with the reference branch disabled, and the training data is obtained from referenced optical measurements performed with the reference branch enabled, above claimed feature is well-known in the art in setting/calibrating optical measurement, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate by design preference, hence Official Notice is taken.
To claims 8 and 18, Coene, Li and Kaushal teach claims 1 and 11.
Coene, Li and Kaushal teach wherein the training data exclusively or partially comprises simulated holographic measurements (Kaushal, paragraphs 0057, 0062,
simulation can be employed as training data, which makes simulating holographic measurements obviously applicable).
To claims 9 and 19, Coene, Li and Kaushal teach claims 1 and 11.
Coene, Li and Kaushal teach comprising the step of performing and/or simulating the holographic measurements to obtain the training data (Kaushal, paragraphs 0057, 0062,
simulation can be employed as training data, which makes simulating holographic measurements obviously applicable).
To claims 10 and 20, Coene, Li and Kaushal teach claims 1 and 11.
Coene, Li and Kaushal teach wherein at least one measurement parameter comprises focus (despite lack of disclosure, focus would obviously be one of parameters on optical/holographic measurement to one of ordinary skill in the art, hence Official Notice is taken).
Claim(s) 7, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Coene et al. (US2017/0261428) in view of Li et al. (US2018/0341173), Kaushal et al. (US2010/0138026) and Foneska et al. (US2007/0092018).
To claims 7 and 17, Coene, Li and Kaushal teach claims 1 and 11.
Coene, Li and Kaushal teach comprising a training step to train an untrained network to obtain the trained network (as explained in response to claim 1 above), but Coene, Li and Kaushal do not expressly disclose extracting sideband data and central band data from the training data; determining complex-valued field data from the sideband data; and using central band data and corresponding complex-valued field data to train the untrained network to directly map the central band data to the complex-valued field, wherein, optionally, the training step comprises an initial correction for optical aberration in the training data or sideband data prior to the step of determining the complex-valued field.
However, complex-valued field using sideband data and central band data would have been obvious.
Foneska teach extracting sideband data and central band data from the training data; determining complex-valued field data from the sideband data; and using central band data and corresponding complex-valued field data to train the untrained network to directly map the central band data to the complex-valued field (paragraph 0294), which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the system and apparatus of Coene, Li and Kaushal, in order to obtain complex-valued field data.
Conclusion
THIS ACTION IS MADE FINAL. 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 ZHIYU LU whose telephone number is (571)272-2837. The examiner can normally be reached Weekdays: 8:30AM - 5:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen R Koziol can be reached at (408) 918-7630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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ZHIYU . LU
Primary Examiner
Art Unit 2669
/ZHIYU LU/Primary Examiner, Art Unit 2665 October 6, 2026
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