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 .
Claim Rejections - 35 USC § 112
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.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], 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.
Claim 20 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, 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.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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)(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.
Claims 18 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Riley et al. (US 2019/0294923).
Regarding claim 18, Riley disclose:
preparing a sample wafer that has undergone at least one unit process of a semiconductor manufacturing process (see para [5], [28], and [56], obtaining a wafer (i.e., “specimen”) during its manufacturing process);
capturing, using an electronic microscope, a plurality of primary images of different locations of the sample wafer (see [6], [57]-[58], and fig 2, capturing, using a scanning election microscope (SEM), a plurality of images of the wafer as an initial training set; and see [88], the initial training set includes defect images and non-defect images of the wafer);
obtaining a plurality of secondary images by changing at least one of a brightness, a color contrast, a pixel size, and a shape of the plurality of primary images (see [72], [82], [83], [85], and [88], obtaining synthetic defect images based on the initial training set, and including the synthetic defect images into the initial training set to generate a modified training set; and see [66], [68], and [70], the synthetic defect images are obtained by augmenting the defect images and non-defect images in the initial training set via varying brightness, blurring, scaling, adding known defects, rotation, etc.).
detecting a plurality of defect images by performing data processing on the plurality of primary images and the plurality of secondary images (see [83] and [94], re-training a classifier configured to detect/classify defects using the modified training set; and see para [89] and [94], wherein re-training to detect/classify defects includes applying the detection/classification the modified training set and comparing to ground-truth for parameter adjustment);
classifying and labeling at least one of the plurality of defect images as defect data (see [58] and [61], the defect images and synthetic defect images are classified and labeled as including a defect); and
generating an automatic defect classification model by performing machine learning based on the defect data (see [26], improving automatic defect classification by using the trained classifier).
Regarding claim 20, Riley further discloses: wherein the obtaining of the plurality of secondary images comprises generating the plurality of secondary images by performing data augmentation comprising the changing of the at least one of the brightness, the color contrast, the pixel size, and the shape on the plurality of primary images (see rejection of claim 18, augmenting via varying brightness, blurring, scaling, adding known defects, rotation, etc.).
Allowable Subject Matter
Claims 1 and 8-12 are allowed. The following is an examiner’s statement of reasons for allowance:
Regarding claim 1, Riley discloses:
preparing a sample wafer that has undergone at least one unit process of a semiconductor manufacturing process (see para [5], [28], and [56], obtaining a wafer (i.e., “specimen”) during its manufacturing process);
capturing, using an electronic device, a plurality of primary images of different locations of the sample wafer (see [6], [57]-[58], and fig 2, capturing, using a scanning election microscope (SEM), a plurality of images of the wafer as an initial training set; and see [88], the initial training set includes defect images and non-defect images of the wafer);
obtaining a plurality of secondary images based on the capturing of the plurality of primary images (see [72], [82], [83], [85], and [88], obtaining synthetic defect images based on the initial training set, and including the synthetic defect images into the initial training set to generate a modified training set),
wherein the plurality of secondary images are generated by performing data augmentation on the plurality of primary images, the data augmentation comprising changing at least one of a brightness, a color contrast, and a spatial resolution of the plurality of primary images (see [66], [68], and [70], the synthetic defect images are obtained by augmenting the defect images and non-defect images in the initial training set via varying brightness, blurring, scaling, adding known defects, rotation, etc.).
detecting a plurality of defect images comprising a defect from among the plurality of primary images and the plurality of secondary images (see [83], re-training a classifier configured to detect/classify defects using the modified training set);
classifying and labeling at least one of the plurality of defect images as defect data (see [58] and [61], the defect images and synthetic defect images are classified and labeled as including a defect during the re-training); and
generating an automatic defect classification model based on the defect data (see [26], improving automatic defect classification by using the trained classifier).
However, Riley does not disclose: comparing the plurality of primary images with the plurality of secondary images.
*Limitations “at least one unit process of a semiconductor manufacturing process”, “data augmentation”, “data processing”, “data processing” throughout the claims are interpreted as equivalent to the definitions given to these phrases by the applicant’s arguments on 09/16/2026. For example, “at least one unit process of a semiconductor manufacturing process” is interpreted as any positive step taken towards semiconductor manufacturing, for purposes of examining.
Response to Arguments
Regarding 112(b) rejection
In view of the claim amendments and the applicant’s arguments, the 112(b) rejection has been withdrawn.
Regarding claims 1 and 8-12 and prior art
In view of the claim amendments and the applicant’s arguments, the 102 rejection of these claims has been withdrawn.
Regarding claims 18 and 20 and prior art
Applicant's arguments filed 09/16/2026 have been fully considered but they are not persuasive. The applicant argues that Riley fails to disclose the subject matter of claim 18, specifically because Riley does not disclose the limitation “detecting a plurality of defect images by performing data processing on the plurality of primary images and the plurality of secondary images”, which requires processing that compares the primary and secondary images to detect defects.
The examiner respectfully disagrees. Claim 18 does not recite any comparison of primary and secondary images. Riley clearly discloses that the synthetic defect images (i.e., reads on “secondary images”) generated are to be added to a modified training set, which is used for re-training an algorithm that detects/classifies defects. In this case, a first re-training reads on the claimed “performing data processing”, as the applicant does not define the “data processing” sufficiently enough to be distinguished from Riley’s re-training. The following second re-training (i.e., Riley discloses continuous re-training in para [96]) reads on the claimed “classifying and labeling”, as the re-training performs said detection/classification for parameter adjustment.
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 SJ PARK whose telephone number is (571)270-3569. The examiner can normally be reached M-F 8:00 AM - 5:00 PM.
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, EMILY TERRELL can be reached at 571-270-3717. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SJ Park/Primary Examiner, Art Unit 2675
