CTNF 18/287,166 CTNF 87383 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. DETAILED ACTION Priority Acknowledgment is made of Applicant's claim for foreign priority based on various Patent Applications. It is noted that Applicant has filed certified copies of the applications. Examiner note: Claim 21 properly depends from Claim 16 but is a duplicate of Claim 19. Examiner respectfully submits Applicant’s intention may be Claim 21 depending from Claim 20. Claim Rejections - 35 USC § 103 07-20-aia AIA 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 . 07-23-aia AIA 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 non-obviousness. 07-21-aia AIA Claim s 1-2, 5-6, 8, 14, 16, 18-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over SOCHA et al (US Pub. No.: 2017-0177760) in view of CAMPBELL et al. (US Pub. No.: 2023-0222664) . As per Claim 1 SOCHA discloses A method of determining a stochastic metric relating to a structure, the method comprising (Figs. 1-11 [Abstract] [0088]) : obtaining a trained model, the model having been trained to correlate training optical metrology data to training stochastic metric data (Figs. 1-11 [0031] [0044-0049] structures 620 [0059-0061] correlative modeling/training [0080, 0082, 0088-0089]) , wherein the training optical metrology data comprises a plurality of measurement signals relating to a plurality of angularly resolved distributions of an intensity related parameter (Figs. 1-11 angular distributions [0031, 0044-0047] [0052-0054] [0061] [0064-0065] [0080, 0082, 0088, 0097]) across a zero or higher order of diffraction comprised within radiation scattered from a plurality of training structures on a substrate (Figs. 1-11 substrate W [0041-0042] zero order [0052-0054] [0061] [0064-0065, 0075]) , and the training stochastic metric data comprises stochastic metric values relating to the plurality of training structures (Figs. 1-11 structures 620. 625 [0059-0061] [0064-0065, 0075] [0080, 0082, 0088]) , wherein the plurality of training structures have been formed with a variation in one or more dimensions (Figs. 1-11 critical dimensions [0038] [0064-0065] [0069-0071]) ; obtaining optical metrology data comprising an angularly resolved distribution of the intensity related parameter across a zero or higher order of diffraction comprised within radiation scattered from a structure Figs. 1-11 angular distributions [0031, 0044-0047]; substrate W [0041-0042] zero order [0052-0054] [0061] [0064-0065] [0080, 0082, 0088, 0097]) ; and using the trained model to infer a value of the stochastic metric associated with the structure from the optical metrology data (Figs. 1-11 inferred values [0061] [0082, 0088, 0097]) SOCHA does not disclose but CAMPBELL discloses one or more dimensions on which the stochastic metric is dependent (Figs. 6A-H [0047-0054]) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include one or more dimensions on which the stochastic metric is dependent as taught by CAMPBELL into the system of SOCHA because of the benefit taught by CAMPBELL to assist with the training of stochastic data from captured spectral images by teaching advancements in image/model training and classification whereby SOCHA benefits from the assistance with stochastic data analysis from images captured for training purposes from related spectral analysis systems. As per Claim 2 SOCHA discloses The method as claimed in claim 1, wherein each of the measurement signals further comprises (See said analysis for Claim 1) parameter across a zero or higher order of diffraction comprised within radiation scattered from the plurality of training structures on the substrate (See said analysis for Claim 1) SOCHA does not disclose but CAMPBELL discloses spectrally resolved distributions of the intensity related parameter (Figs. 1-7 [0010, 0014-0015] [0049-0052]) (The motivation that applied in Claim 1 applies equally to Claim 2) As per Claim 5 SOCHA discloses The method as claimed in claim 1, wherein the model comprises a machine learning model (Figs. 1-11 [0052, 0085, 0089]) , neural network (either or) or convolutional neural network (either or) . As per Claim 6 SOCHA discloses The method as claimed in claim 1, wherein the variation in one or more dimensions is associated with a variation (Figs. 1-11 [0038]) in one or more process parameters of a lithographic process (Figs. 1-2 [Abstract] [0021-0023]) used in applying the training structures to the training substrate (Figs. 1-11 [0038] [0064-0065] [0069-0071]) . As per Claim 8 SOCHA discloses The method as claimed in claim 6, wherein the one or more process parameters are dose and/or focus (dose/focus [0087, 0091]) . As per Claim 14 SOCHA discloses A non-transitory computer-readable medium comprising a computer program therein, the computer program comprising program instructions operable to cause one or more processors to (Figs. 1-11 [0125] [0141]) SOCHA in view of CAMPBELL discloses perform at least the method of claim 1 (See said analysis for Claim 1) . As per Claim 16 SOCHA discloses A method of determining a stochastic metric relating to a structure, the method comprising (See said analysis for Claim 1) : obtaining a trained machine learning model (See said analysis for Claim 5) , the machine learning model having been trained to correlate training optical metrology data to training stochastic metric data (See said analysis for Claim 1) , wherein the training optical metrology data comprises a plurality of measurement signals relating to radiation scattered from a plurality of training structures (See said analysis for Claims 1 and 2) on a substrate and the training stochastic metric data comprises stochastic metric values relating to the plurality of training structures (See said analysis for Claims 1 and 2) , wherein the plurality of training structures have been formed with a variation in one or more dimensions (See said analysis for Claim 1) ; obtaining optical metrology data from a structure (See said analysis for Claim 1) ; and using the trained machine learning model to infer a value for the stochastic metric associated with the structure from the optical metrology data (See said analysis for Claim 1) SOCHA does not disclose but CAMPBELL discloses one or more dimensions on which the stochastic metric is dependent (See said analysis for Claim 1) As per Claim 18 SOCHA discloses The method of claim 16, wherein the measurement signal is a zero order pupil intensity distribution of radiation after being scattered by the training structure (Figs. 1-11 [0052] [0061] [0064-0065, 0075]) As per Claim 19 SOCHA discloses A non-transitory computer-readable medium comprising a computer program therein, the computer program comprising program instructions operable to cause one or more processors to (Figs. 1-11 [0125] [0141]) SOCHA in view of CAMPBELL discloses perform at least the method of claim 16 (See said analysis for Claim 16) . As per Claim 20 SOCHA discloses A method of determining a stochastic metric relating to a lithographic process, the method comprising (See said analysis for Claims 1 and 6) : obtaining a trained model, the model having been trained on training inspection image data and training stochastic metric data (Figs. 1-11 sensors 19, 23 [0031, 0050-0052] inspection imaging [0033-0034] [0044-0049] structures 620 [0059-0061] correlative modeling/training [0080, 0082, 0088-0089]) , wherein the training inspection image data comprises a plurality of inspection images (Figs. 1-11 sensors 19, 23 [0050-0056]) , each relating to reflected radiation having been reflected by a training structure of a plurality of training structures on a training substrate (Figs. 1-11 structures 620, 625 [0052-0056] [0059-0061] [0064-0065, 0075] [0080, 0082, 0088]) , and the training stochastic metric data comprises stochastic metric values relating to the training structures, wherein the plurality of training structures have been formed with a variation in one or more dimensions (See said analysis for Claim 1) ; obtaining inspection image data relating to a structure having been exposed in a lithographic process (Figs. 1-11 imaging data [0052-0056] [0059-0061] [0064-0065, 0075] [0080, 0082, 0088] - See said analysis for Claims 1 and 6) ; and using the trained model to infer a value for the stochastic metric associated with the structure from the inspection image data (Figs. 1-11 imaging data [0052-0056] [0059-0061] [0064-0065, 0075] [0080, 0082, 0088] - See said analysis for Claims 1 and 6) SOCHA does not disclose but CAMPBELL discloses one or more dimensions or process parameters on which the stochastic metric is dependent (See said analysis for Claim 1) As per Claim 21 SOCHA discloses A non-transitory computer-readable medium comprising a computer program therein, the computer program comprising program instructions operable to cause one or more processors to (Figs. 1-11 [0125] [0141]) SOCHA in view of CAMPBELL discloses perform at least the method of claim 16 (See said analysis for Claim 16) . 07-21-aia AIA Claim 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over SOCHA et al (US Pub. No.: 2017-0177760) in view of CAMPBELL et al. (US Pub. No.: 2023-0222664), as applied in Claims 1-2, 5-6, 8, 14, 16, 18-21 , and further in view of TRIPODI et al. (US Pub. No.: 2019-0378012) . As per Claim 3 SOCHA discloses The method as claimed in claim 1, wherein SOCHA and CAMPBELL do not disclose but TRIPODI discloses the parameter is diffraction efficiency (Figs. 5a-d [0021] [0058-0061]) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the parameter is diffraction efficiency as taught by TRIPODI into the system of SOCHA and CAMPBELL because of the benefit taught by TRIPODI to introduce diffraction efficiency in combination with SOCHA and CAMPBELL that are both related to spectral light analysis for imaging and would benefit from the additional teachings for diffraction analysis . 07-21-aia AIA Claim 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over SOCHA et al (US Pub. No.: 2017-0177760) in view of CAMPBELL et al. (US Pub. No.: 2023-0222664), as applied in Claims 1-2, 5-6, 8, 14, 16, 18-21 , and further in view of SHCHEGROV et al. (US Pub. No. : 2016-0003609) As per Claim 13 SOCHA discloses The method as claimed in claim 1, wherein the stochastic metric comprises (See said analysis for Claim 1) SOCHA and CAMPBELL do not disclose but SHCHEGROV discloses one or more selected from defect rate (one of) or other defect metric (Figs. 1-5 [0109]) , line edge roughness (one of) , line width roughness (one of) , local critical dimension uniformity (one of) , circle edge roughness (one of) or edge placement error (one of) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include one or more selected from defect rate or other defect metric, line edge roughness, line width roughness, local critical dimension uniformity, circle edge roughness or edge placement error as taught by SHCHEGROV into the system of SOCHA and CAMPBELL because of the benefit taught by SHCHEGROV to provide areas of improvement and problems solved by incorporating trained image data to solve inspection issues which benefits the inspection systems of SOCHA and CAMPBELL . 07-21-aia AIA Claim 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over SOCHA et al (US Pub. No.: 2017-0177760) in view of CAMPBELL et al. (US Pub. No.: 2023-0222664), as applied in Claims 1-2, 5-6, 8, 14, 16, 18-21 , and further in view of BIAFORE et al. (US Pub. No.: 2018-0275523) . As per Claim 17 SOCHA discloses The method of claim 16, wherein the stochastic metric represents (See said analysis for Claim 16) SOCHA and CAMPBELL do not disclose but BIAFORE discloses a defect probability (probabilities of stochastic defects [0030]) or a CD variation at a spatial scale smaller than 1000 times the CD (either or) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a defect probability or a CD variation at a spatial scale smaller than 1000 times the CD as taught by BIAFORE into the system of SOCHA and CAMPBELL because of the benefit taught by BIAFORE to include teachings and problem-solving techniques for imaging inspection systems through advanced defect analysis which would benefit directly the imaging inspection systems of SOCHA and CAMPBELL. Allowable Subject Matter 07-43 Claims 4, 7, 9-12 is/are objected to as being dependent upon the 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 . Claims 4, 7, 9-12 is/are allowed. The following is an examiner’s statement of reasons for allowance: As per Claim 4 the prior art of record either alone or in reasonable combination fails to teach or suggest “ The method as claimed in claim 1, wherein the training optical metrology data further comprises nominal informative metrology data relating to one or both of: non-defect measurements and/or simulations; and/or specific defect measurements or simulations " These limitations in combination with the other limitations of the independent claim are thus deemed allowable. As per Claim 7 the prior art of record either alone or in reasonable combination fails to teach or suggest “ The method as claimed in claim 6, wherein the said training stochastic metric data describes an acceptable space or range of stochastic metric values or related dimensional metric values, and a corresponding acceptable space or range of values of the one or more process parameters " These limitations in combination with the other limitations of the independent claim are thus deemed allowable . As per Claim 9 the prior art of record either alone or in reasonable combination fails to teach or suggest “ The method as claimed in claim 1, further comprising the initial steps of: obtaining the training optical metrology data and stochastic metric data; and training the trained model on the training optical metrology data and stochastic metric data " These limitations in combination with the other limitations of the independent claim are thus deemed allowable . As per Claim 10 the prior art of record either alone or in reasonable combination fails to teach or suggest “ The method as claimed in claim 9, comprising: obtaining high-resolution metrology data; and determining the stochastic metric data from the high-resolution metrology data " These limitations in combination with the other limitations of the independent claim are thus deemed allowable . As per Claim 11 the prior art of record either alone or in reasonable combination fails to teach or suggest “ The method as claimed in claim 10, wherein the high-resolution metrology data is obtained from scanning electron microscope metrology " These limitations in combination with the other limitations of the independent claim are thus deemed allowable . As per Claim 12 the prior art of record either alone or in reasonable combination fails to teach or suggest “ The method as claimed in claim 1, further comprising using the inferred value for the stochastic metric to decide where and/or when to perform further high-resolution metrology " These limitations in combination with the other limitations of the independent claim are thus deemed allowable . The closest prior art of record SOCHA et al (US Pub. No.: 2017-0177760) for Claims 4, 7, 9-12 does not teach all the elements in combination with the other limitations of the independent claim. SOCHA only discloses obtaining a trained model, the model having been trained to correlate training optical metrology data to training stochastic metric data, wherein the training optical metrology data comprises a plurality of measurement signals relating to a plurality of angularly resolved distributions of an intensity related parameter across a zero or higher order of diffraction comprised within radiation scattered from a plurality of training structures on a substrate. The prior art also teaches training stochastic metric data by stochastic metric values relating to the plurality of training structures, wherein the plurality of training structures formed with a variation in one or more dimensions. Finally, the prior art teaches obtaining optical metrology data comprising an angularly resolved distribution of the intensity related parameter across a zero or higher order of diffraction comprised within radiation scattered from a structure and using the trained model to infer a value of the stochastic metric associated with the structure from the optical metrology data. 13-03 Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.” Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to EILEEN M ADAMS whose telephone number is 571-270-3688. The examiner can normally be reached on Monday-Friday from 8:30am-5:00pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, William Vaughn can be reached on (571) 272-3922. The fax phone number for the organization where this application or proceeding is assigned is 571-270-4688. 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 http://pair-direct.uspto.gov. Should you have any 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. /EILEEN M ADAMS/Primary Examiner, Art Unit 2481 Application/Control Number: 18/287,166 Page 2 Art Unit: 2481 Application/Control Number: 18/287,166 Page 3 Art Unit: 2481 Application/Control Number: 18/287,166 Page 4 Art Unit: 2481 Application/Control Number: 18/287,166 Page 5 Art Unit: 2481 Application/Control Number: 18/287,166 Page 6 Art Unit: 2481 Application/Control Number: 18/287,166 Page 7 Art Unit: 2481 Application/Control Number: 18/287,166 Page 8 Art Unit: 2481 Application/Control Number: 18/287,166 Page 9 Art Unit: 2481 Application/Control Number: 18/287,166 Page 10 Art Unit: 2481 Application/Control Number: 18/287,166 Page 11 Art Unit: 2481 Application/Control Number: 18/287,166 Page 12 Art Unit: 2481 Application/Control Number: 18/287,166 Page 13 Art Unit: 2481 Application/Control Number: 18/287,166 Page 14 Art Unit: 2481
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