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, see page 8, filed 9/16/25, with respect to the title have been fully considered and are persuasive. The objection has been withdrawn.
Applicant’s arguments, see page 8, filed 9/16/25, with respect to claim 16 have been fully considered and are persuasive. The 35 U.S.C. 112(a) rejection has been withdrawn.
Applicant's arguments filed 9/16/25 have been fully considered but they are not persuasive.
Regarding applicant’s argument for claim 23, on page 9, that Dziura does not disclose detection of electrons from one or more other substrates, examiner disagrees but also finds the argument moot. Current claim construction contains an “or” (NOTE: “and/or” is functionally the same scope as “or”) statement separating “signals from one or more electron beam inspection systems configured to detect electrons from one or more other substrates manufactured using the device manufacturing process” from “comprising data derived from the electron beam inspection system signals”, thereby requiring only one of the limitations to be disclosed. Paragraph 56 clearly discloses training data obtained from electron beam signals, however, even if the “or” statement was an “and” statement, figure 11 (paragraphs 60 and 61) clearly discloses obtaining SEM data from a plurality of reference structures as well as obtaining spectra data from a plurality of structures. Therefore the argument is overcome and the previous rejection remains.
Regarding applicant’s argument for claim 30, on page 10, that Shlain does not disclose a review based upon defect data, examiner disagrees. Paragraph 35 specifically discloses “analysis of defects that are re-detected by a review tool based on locations of suspected defects” and “a review system such as SEMVision” thereby disclosing a defect classifier (paragraph 28, analogous to machine learning defect output in Dziura) producing suspected defect locations that are then reviewed by electron beam inspection. Therefore the amended claim is disclosed by the previous combination of references.
Claim Rejections - 35 USC § 102
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)(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.
1) Claim(s) 23, 24, 27-31 and 33-36 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by U.S. patent application publication 2015/0046121 by Dziura et al.
2) Regarding claim 23, Dziura teaches a computer program product comprising a non-transitory computer-readable medium comprising instructions therein, the instructions, when executed by a computer system, configured to cause the computer system to at least: obtain, by a hardware computer system, a machine learning model (paragraph 54; machine learning can be utilized) configured to identify defects (paragraph 57; defects can be identified) and trained using a training set comprising (i) signals from one or more optical inspection systems configured to detect radiation redirected by one or more substrates manufactured using a device manufacturing process and/or comprising data derived from the optical inspection system signals (paragraphs 38 and 56; spectra of reflected light is part of the training set) and (ii) signals from one or more electron beam inspection systems configured to detect electrons from one or more other substrates manufactured using the device manufacturing process and/or comprising data derived from the electron beam inspection system signals (Paragraph 56 clearly discloses training data obtained from electron beam signals, however, even if the “or” statement was an “and” statement, figure 11 (paragraphs 60 and 61) clearly discloses obtaining SEM data from a plurality of reference structures as well as obtaining spectra data from a plurality of structures); and produce defect data by application of data acquired from a substrate by an optical inspection system to the machine learning model (paragraphs 74 and 87; defects are determined through modeling of the imaged training data).
3) Regarding claim 24, Dziura teaches the computer program product of claim 23, wherein the defect data comprises data regarding one or more selected from: necking, line pull back, line thinning, critical dimension, overlapping and/or bridging (paragraph 87; at least critical dimension is determined).
4) Regarding claim 27, Dziura teaches the computer program product of claim 23, wherein the training set further comprises values of a process parameter associated with a plurality of substrates processed by the device manufacturing process, the process parameter representing a setting or condition of a physical process, a material, an object or an apparatus that occurred during, or is occurring during, application of physical processing to the substrates (paragraph 36 and 94; grating structure and other structures can be analyzed as a parameter).
5) Regarding claim 28, Dziura teaches the computer program product of claim 27, wherein the process parameter is a characteristic of illumination by a lithographic apparatus, a characteristic of projection optics of a lithographic apparatus, dose, focus, a characteristic of resist, a characteristic of development of resist, a characteristic of post-exposure baking of resist, and/or a characteristic of etching (paragraphs 87 and 94; spectra of lithography, dose, focus, resist and etching are described as parameters).
6) Regarding claim 29, Dziura teaches an inspection system comprising: an optical or electron beam apparatus configured to detect radiation or electrons respectively; and the computer program product of claim 23 (paragraph 4; electron microscope disclosed).
7) Claim 30 is rejected in the same manner as described in the rejection of claim 23 above, with the exception of optical intensity (paragraph 56; spectra of reflected light is a measure of intensity).
8) Claims 31, 34 and 35 are taught in the same manner as described in the rejection of claims 23, 29 and 24 above, respectively.
9) Regarding claim 36, Dziura teaches the computer program product of claim 30, wherein the training set further comprises values of a process parameter associated with a plurality of substrates processed by the device manufacturing process, the process parameter representing a setting or condition of a physical process, a material, an object or an apparatus that occurred during, or is occurring during, application of physical processing to the substrates (paragraph 55; variation in anneal temperature is an example of a process parameter used in training).
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.
10) Claim(s) 16-22, 25, 26 and 32 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. patent application publication 2015/0046121 by Dziura et al., and further in view of U.S. patent application publication 2013/0279795 by Shlain et al.
11) Regarding claim 16, Dziura teaches a computer program product comprising a non-transitory computer- readable medium comprising instructions therein, the instructions, when executed by a computer system, configured to cause the computer system to at least: obtain, by a hardware computer system, a machine learning model (paragraph 54; machine learning can be utilized) configured to identify defects (paragraph 57; defects can be identified) and trained using a training set comprising signals from one or more optical inspection systems configured to detect radiation redirected by one or more substrates manufactured using a device manufacturing process and/or comprising data derived from the optical inspection system signals (paragraphs 38 and 56; spectra of reflected light is part of the training set); produce defect data by application of data acquired from a substrate by an optical inspection system to the machine learning model (paragraphs 74 and 87; defects are determined through modeling of the imaged training data);
Dziura does not specifically teach performing, based on the defect data, a review using an electron beam inspection of the substrate.
Schlain teaches performing, based on the defect data, a defect review using an electron beam inspection of the substrate (paragraph 35; “analysis of defects that are re-detected by a review tool based on locations of suspected defects” and “a review system such as SEMVision” is disclosed thereby teaching a defect classifier [paragraph 28, analogous to machine learning defect output in Dziura] producing suspected defect locations that are then reviewed by electron beam inspection).
Dziura and Schlain are combinable because they are both from the semiconductor defect classification field of endeavor.
It would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine Dziura and Schlain to add defect review. The motivation for doing so would have been to “reduce the load on human inspectors” (paragraph 3). Therefore it would have been obvious to combine Dziura and Schlain to obtain the invention of claim 16.
12) Claim 17 is taught in the same manner as described in the rejection of claim 23 above.
13) Regarding claim 18, Schlain (as combined with Dziura as disclosed in the rejection of claim 16 above) teaches the computer program product of claim 16, wherein the instructions are further configured to cause the computer system to calculate a probability of a defect for the substrate using the machine learning model (paragraphs 28 and 53; level of confidence [probability] of a defect class is disclosed).
14) Claims 19-22 are taught in the same manner as described in the rejections of claims 27, 28, 24 and 29 above, respectively.
15) Claims 25 and 32 are taught in the same manner as described in the rejection of claim 18 above.
16) Claim 26 is taught in the same manner as described in the rejection of claim 16 above.
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.
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BENJAMIN O. DULANEY
Primary Examiner
Art Unit 2676
/BENJAMIN O DULANEY/Primary Examiner, Art Unit 2682