CTNF 18/518,025 CTNF 82213 DETAILED ACTION 08-25-01 AIA Applicant’s election without traverse of Group 1, claims 1-14 and 18 in the reply filed on 03/30/2026 is acknowledged. Claims 15-16 and 19-20 have been cancelled. New claims 21-23 have been added. Claims 1-14, 17-18 and 21-23 are now pending in the present application. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15-aia AIA Claim(s) 1-14, 17-18 and 21-23 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Lu (US 2019/0287230 A1) . Regarding claims 1 and 18, Lu discloses a system comprising one or more processing circuitries configured to: (paragraphs 11, 77, 86-89 and figure 10; Lu discloses a wafer inspection tool and a processor in electronic communication with the wafer inspection tool and figure 10 shows system 200 including computer subsystem 202, processor 208, electronic storage 209 and machine learning module 210.) [claim 18: A non-transitory computer readable medium comprising instructions that, when executed by at least one or more processing circuitries, cause the at least one or more processing circuitries to perform:] obtain a first inspection image informative of a first area of a semiconductor specimen acquired by an examination tool, (paragraphs 49, 77-83; Lu discloses that an image of a wafer may be received at the processor, that the image may be an SEM image, and that the image may be obtained using a wafer inspection tool, such as an SEM.) feed at least the first inspection image to a machine learning algorithm (paragraphs 45, 50, 88-89 and figure 1; Lu discloses applying the model at step 103 using a processor to find one or more anomalies in images patches and figure 1 shows applying the model to generate reconstruction errors and determine anomalies.) configured to determine, for each given pixel of a plurality of pixels of the first inspection image, or for each given group of pixels of a plurality of groups of pixels of the first inspection image, one or more given parameters of a given model informative of pixel intensity distribution, (paragraphs 45, 51, 54-56, 74; figures 2-4’ Lu discloses image patches, a variational autoencoder that aims to model the distributions rather than the real values, an approximate posterior and that f(x) outputs parameters of the approximate posterior. Lu further states that reconstruction probability is models as a Gaussian distribution. Therefore, Lu’s SEM image patches are groups of pixels and Lu’s variational autoencoder probability model determines parameters of a distribution model informative of nominal SEM image-pixel content.) for said each given pixel, or said each given group of pixels, use: at least one of the one or more given parameters, or at least part of the given model associated with the one or more given parameters, (paragraphs 45, 58, 72-74; figure 6; Lu discloses that the model generates reconstruction errors and/or probabilities, predicts whether a patch is abnormal using reconstruction error/probabilities and identifies anomaly regions by thresholding pixel-level reconstruction probabilities.) and measured pixel intensity of the given pixel or of the given group of pixels, to determine whether a defect is present in the given pixel or in the given group of pixels. (paragraphs 48, 52-54, 71-74; figures 5-6; Lu defines reconstruction error as the difference between original input vector x and reconstruction PNG media_image1.png 22 14 media_image1.png Greyscale and states that the difference between reconstructed and original SEM images may be calculated to located anomaly patterns and defects.) Regarding claim 2, Lu discloses the claimed invention wherein for said each given pixel or for said each given group of pixels, use at least some of the one or more given parameters, or at least part of the given model associated with the one or more given parameters, to determine a probability that a defect is present in the given pixel or in the given group of pixels. (paragraphs 45, 48 and 72-74) Regarding claim 3, Lu discloses the claimed invention wherein, for said each given pixel or for said each given group of pixels, at least part of the given model associated with the one or more given parameters is informative of a pixel intensity probability distribution usable to determine a probability that the measured pixel intensity of the given pixel or of the given group of pixels corresponds to a defect. (paragraphs 51, 54-56 and 74) Regarding claim 4, Lu discloses the claimed invention wherein, for said each given pixel or for said each given group of pixels, at least one of the one or more given parameters, or at least part of the given model associated with the one or more given parameters, is usable to determine an expected pixel intensity in an absence of defects in the given pixel or in the given group of pixels. (paragraphs 42, 44, 68 and 71) Regarding claim 5, Lu discloses the claimed invention wherein, for said each given pixel or for said each given group of pixels, at least one of the one or more given parameters, or at least part of the given model associated with the one or more given parameters, is usable to determine:- an expected pixel intensity in an absence of defects in the given pixel or in the given group of pixels, and- a probability that a deviation from the expected pixel intensity corresponds to a defect. (paragraphs 45, 48, 68 and 71-74) Regarding claim 6, Lu discloses the claimed invention wherein, configured to use the given model to detect, on average, in different regions with a different level of noise, a maximal number of defects below a same threshold for said different regions. (paragraphs 64, 67 and 72-75) Regarding claim 7, Lu discloses the claimed invention wherein, configured to, for said each given pixel or for said each given group of pixels, use at least some of the one or more given parameters, or at least part of the given model associated with the one or more given parameters, to differentiate between presence of a defect and presence of noise in the given pixel or in the given group of pixels. (paragraphs 64 and 71-74) Regarding claim 8, Lu discloses the claimed invention wherein the one or more given parameters are determined by the machine learning algorithm specifically for said each given pixel or said each given group of pixels, wherein the one or more given parameters, or data derived thereof, comprise:- data informative of an expected pixel intensity in the given pixel, or in the given group of pixels, in an absence of defects in the given pixel, or in the given group of pixels;- at least one of: o data informative of noise present in the given pixel, or in the given group of pixels; o data informative of a confidence associated with the data informative of expected pixel intensity in the given pixel or in the given group of pixels; o data enabling normalization of noise present in the given pixel or in the given group of pixels; or o data enabling differentiating between defects and noise in the given pixel or in the given group of pixels. (paragraphs 45, 51, 54-56, 64 and 71-74) Regarding claim 9, Lu discloses the claimed invention wherein, configured to feed, to the machine learning algorithm, in addition to the first inspection image, one or more reference images informative of one or more other areas of the semiconductor specimen, or of another semiconductor specimen. (paragraph 62) Regarding claim 10, Lu discloses the claimed invention wherein the machine learning algorithm has been trained with at least one training image and a loss function comprising, for each given pixel of a plurality of pixels of the training image, or for each group of pixels of a plurality of group of pixels of the training image, one or more parameters of a model modelling pixel intensity associated with the given pixel or with the given group of pixels. (paragraphs 41-44, 54 and 73) Regarding claim 11, Lu discloses the claimed invention wherein at least one of (i) or (ii) is met:(i) the machine learning algorithm is configured to determine, for each given pixel of a plurality of pixels of the first inspection image, or for each given group of pixels of a plurality of groups of pixels of the first inspection image, at the same time: data informative of expected pixel intensity in the given pixel or in the given group of pixels, in an absence of defects in the given pixel or in the given group of pixels, and data informative of noise present in the given pixel or in the given group of pixels;(ii) the same machine learning algorithm is configured to determine, for each given pixel of a plurality of pixels of the first inspection image, or for each given group of pixels of a plurality of groups of pixels of the first inspection image: data informative of expected pixel intensity in the given pixel or in the given group of pixels, in an absence of defects in the given pixel or in the given group of pixels, and data informative of noise present in the given pixel or in the given group of pixels. (paragraphs 54-56, 68 and 71-74) Regarding claim 12, Lu discloses the claimed invention wherein obtain a single inspection image informative of an area of a semiconductor specimen acquired by the examination tool, determine one or more defects in the area based on the single inspection image, said determination comprising: feeding the single inspection image to the machine learning algorithm configured to determine, for each given pixel of a plurality of pixels of the single inspection image, or for each given group of pixels of a plurality of groups of pixels of the first inspection image, one or more given parameters of a given model informative of pixel intensity distribution, and for said each given pixel, or said each given group of pixels, using at least one of the one or more given parameters, or at least part of the given model associated with the one or more given parameters, and measured pixel intensity of the given pixel or of the given group of pixels, to determine whether a defect is present in the given pixel or in the given group of pixels. (paragraphs 14-15, 45, 48-49 and 102) Regarding claim 13, Lu discloses the claimed invention wherein one or more training images used to train the machine learning algorithm have at least one of a smaller height or a smaller width than the first inspection image. (paragraphs 45, 64 and 75-76) Regarding claim 14, Lu discloses the claimed invention wherein configured to use at least one of the one or more given parameters, or at least part of the given model associated with the one or more given parameters, to generate a new image. (paragraphs 46, 68, 71 and 75-76) Regarding claim 17, Lu discloses the claimed invention wherein one or more training images used to train the machine learning algorithm correspond to one or more inspection images of a semiconductor specimen, to which one or more artificial defects have been added. (paragraphs 39-44 and 67-70) Regarding claim 21, Lu discloses the claimed invention wherein at least one of (i) or (ii) is met: (i) the new image is noise-free or contains less noise than the first inspection image; (ii) the new image is defect-free or contains less defects than the first inspection image. (paragraphs 62-63) Regarding claim 22, Lu discloses the claimed invention wherein for said each given pixel, or said each given group of pixels, use at least one of the one or more given parameters, or at least part of the given model associated with the one or more given parameters, to determine a new given pixel intensity value, thereby obtaining a set of new given pixel intensity values, and use the set of new given pixel intensity values to generate a new image. (paragraphs 48, 52-54, 71-74; figures 5-6) Conclusion 23. (New) The non-transitory computer readable medium of claim 22, wherein: each new given pixel intensity value corresponds to a given expected pixel intensity in an absence of defects in the given pixel or in the given group of pixels, and the set of new given pixel intensity values corresponds to a set of given expected pixel intensity values, wherein at least one of (i) or (ii) is met:(i) the new image is noise-free or contains less noise that the first inspection image;(ii) the new image is defect-free or contains less defects than the first inspection image. (paragraphs 62-63) Any inquiry concerning this communication or earlier communications from the examiner should be directed to BOBBAK SAFAIPOUR whose telephone number is (571)270-1092. The examiner can normally be reached Monday - Friday, 8:00am - 5:00pm. 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, Stephen 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. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /BOBBAK SAFAIPOUR/Primary Examiner, Art Unit 2665 Application/Control Number: 18/518,025 Page 2 Art Unit: 2665 Application/Control Number: 18/518,025 Page 3 Art Unit: 2665 Application/Control Number: 18/518,025 Page 4 Art Unit: 2665 Application/Control Number: 18/518,025 Page 5 Art Unit: 2665 Application/Control Number: 18/518,025 Page 6 Art Unit: 2665 Application/Control Number: 18/518,025 Page 7 Art Unit: 2665 Application/Control Number: 18/518,025 Page 8 Art Unit: 2665 Application/Control Number: 18/518,025 Page 9 Art Unit: 2665
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