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 .
Notice to Applicants
This action is in response to the Request for Continued Examination filed on 06/02/2026.
Claims 1-20 are pending.
Corrective Actions by Applicant
Claims 1 and 17 have been amended.
Request for Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/02/2026 has been entered.
Response to Arguments
The examiner has fully considered Applicant’s presented arguments.
On pages 7-9 of the remarks, Applicant argues that Toyoda fails to remedy the deficiencies of Sato. This is persuasive, based on Applicant’s argument regarding the new limitation of “and the raw image quality metric determined other than by reference to a target pattern corresponding to the one or more features”. Although primary reference Sato is argued by the examiner to disclose this limitation below, Toyoda discloses the opposite in that Toyoda’s methods require using the ground-truth target pattern for image evaluation, weather that be the quality of the image or of the substrate itself.
Thus, Toyoda is no longer combinable with Sato to read on the claims, and all previous 35 U.S.C. 103 rejections have been withdrawn. However, the amendments necessitate new rejections below in view of new reference Chen.
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.
Claims 1-3 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Sato et al. (U.S. Publ. US-20030111602-A1) in view of Chen et al. (U.S. Publ. US-2019/0139224-A1).
Regarding claim 1, Sato discloses a non-transitory computer-readable medium comprising instructions stored therein that, when executed by one or more processors, are configured to the one or more processors to at least (see paragraphs 0045-0046):
obtain a plurality of raw images of a patterned substrate (see figure 11 & paragraph 0148, where a plurality of images will be analyzed to remove abnormal images);
determine a raw image quality metric (see figure 11 & paragraph 0148, where quality of raw images is measured by evaluating displacement, contrast, or blur of the images in general);
and the raw image quality metric determined other than by reference to a target pattern corresponding to the one or more features (the quality evaluation embodiments in paragraphs 0148-0152 do not reference any kind of target pattern of the substrate used to evaluate image quality);
select, based on the raw image quality metric, a sub-set of raw images from the plurality of raw images (see figure 11 and paragraph 0148, where abnormal images are removed, leaving a subset of raw images);
and provide the sub-set of raw images for performing measurements associated with the one or more features within an image (paragraph 0151 specifies that this embodiment can be applied to embodiment 4, which paragraphs 0126-0128 specify is directed to measuring dimensions of wafer patterns).
Sato fails to disclose determine a raw image quality metric based on data associated with one or more gauges or one or more contours of one or more features within each image of the plurality of raw images, the raw image quality metric being indicative of a raw image quality (emphasis added via underline).
Pertaining to the same field of endeavor, Chen discloses determine a raw image quality metric based on data associated with one or more gauges or one or more contours of one or more features within each image of the plurality of raw images, the raw image quality metric being indicative of a raw image quality (first see paragraphs 0020, 0041, where the image quality of wafer images is classified based on identifed image artifacts; then see figure 4 & paragraphs 0044-0050, where the artifacts are identified by locations of high contrast via edge detection; figure 7 & paragraphs 0062-0065 illustrate this process; the examiner interprets this artifact/edge detection as an instance of detecting gauges/contours of image features);
and the raw image quality metric determined other than by reference to a target pattern corresponding to the one or more features (the process of figure 4 and paragraphs 0044-0050 determines artifacts & quality based solely on strong edge detection and comparing said edges across different image channels, rather than by comparing the features to a ground-truth target pattern).
Sato and Chen are considered analogous art, as they are both directed to evaluating quality of substrate images. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Chen into Sato by adding Chen’s artifact detection to Sato’s quality assessment because doing so improves the image quality assessment by distinguishing image artifacts from actual contaminants/defects (see Chen paragraphs 0020-0022).
Regarding claim 2, Sato fails to disclose the new limitations of claim 2.
Pertaining to the same field of endeavor, Chen discloses wherein the instructions configured to determine the raw image quality metric are further configured to cause the one or more processors to analyze, based on specified criteria, gauge data associated with gauges of each image of the plurality of raw images (see citations to claim 1 above; the artifact classification into different types reads on analyzing the gauges of the artifacts by specified criteria).
Sato and Chen are considered analogous art, as they are both directed to evaluating quality of substrate images. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Chen into Sato by adding Chen’s artifact detection to Sato’s quality assessment because doing so improves the image quality assessment by distinguishing image artifacts from actual contaminants/defects (see Chen paragraphs 0020-0022).
Regarding claim 3, Sato fails to disclose the new limitations of claim 3.
Pertaining to the same field of endeavor, Chen discloses wherein the instructions configured to analyze gauge data are further configured to cause the one or more processors to: determine whether the gauge data associated with the gauges exists for a given raw image of the plurality of images (see paragraph 0022, where each analyzed image can have discovered artifacts and gauges thereof or lack any artifacts);
responsive to the gauge data not existing, assign a first value to the raw image quality metric (see paragraph 0022, where the image quality can be classified as "good" if no artifacts, and thus no gauges, are found);
and responsive to the gauge data existing, assign a second value to the raw image quality metric different than the first value (see paragraph 0022, where the image quality can be classified as different types of "bad" if any artifacts and gauges thereof are found).
Sato and Chen are considered analogous art, as they are both directed to evaluating quality of substrate images. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Chen into Sato because using Chen's classification system incorporates false positive and actual defect classes (see Chen paragraph 0022).
Regarding claim 17, Sato in view of Chen discloses claim 17 as applied to claim 1 above.
Regarding claim 18, Sato in view of Chen discloses claim 18 as applied to claim 2 above.
Regarding claim 19, Sato fails to disclose the new limitations of claim 19.
Pertaining to the same field of endeavor, Chen discloses determining whether the gauge data associated with the gauges exists for a given raw image of the plurality of images (see citations to same limitation in claim 3 above);
and responsive to the gauge data not existing, assigning a first value to the raw image quality metric, the first value being lower than a selection threshold (see paragraph 0022, where the image quality can be classified as "good" if no artifacts, and thus no gauges, are found; this reads on the level of image artifacting being below a threshold such that it is assigned a "good" class),
or responsive to the gauge data existing, assigning a second value to the raw image quality metric, the second value being relatively higher than the selection threshold (see paragraph 0022, where the image quality can be classified as different types of "bad" if any artifacts and gauges thereof are found; this reads on the level of artifacting being above a selection threshold such that the image is assigned a "bad" class; see Chen claim 9, which specifies that this process can include quality ratings and thresholds; note that the present claim does not specify whether a higher or lower metric value represents higher quality).
Sato and Chen are considered analogous art, as they are both directed to evaluating quality of substrate images. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Chen into Sato because using Chen's classification system incorporates false positive and actual defect classes (see Chen paragraph 0022).
Claims 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Sato et al. (U.S. Publ. US-20030111602-A1) in view of Chen et al. (U.S. Publ. US-2019/0139224-A1), and further in view of Grodt et al. (U.S. Publ. US-2019/0128664-A1).
Regarding claim 9, Sato in view of Chen fails to disclose the new limitations of claim 9.
Pertaining to the same field of endeavor, Grodt discloses wherein the instructions configured to determine the raw image quality metric are further configured to cause the one or more processors to perform statistical analysis on gauge data of the gauges associated with each raw image to generate the raw image quality metric (first see figure 1C & paragraph 0025, where an edge profile of a target is first obtained by obtaining light gradients across the inter to outer diameter of the target at different points along the contour; then see figure 4 & paragraph 0030, where the average/median contrast for each point is used as an edge profile symmetry metric, which is an example of statistical analysis).
Sato and Grodt are considered analogous art, as they are both directed to evaluating quality of manufacturing images. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Grodt into Sato and Chen because Grodt's edge profile calculation accurately determines edge symmetry and dimensional deviations based on contrast (see Grodt paragraph 0015).
Regarding claim 10, Sato in view of Chen discloses wherein the raw image quality metric indicates a contrast (see Sato figure 11 & paragraph 0148, where quality of raw images is measured by evaluating displacement, contrast, or blur of the images in general).
Sato in view of Chen fails to disclose wherein the raw image quality metric indicates a contrast at the gauges associated with each raw image (emphasis added via underline). In other words, although they do disclose evaluating contrast at the gauges, they fail to do so using statistical analysis, as required by parent claim 9 above.
Pertaining to the same field of endeavor, Grodt discloses wherein the raw image quality metric indicates a contrast at the gauges associated with each raw image (see citations to claim 9 above).
Sato and Grodt are considered analogous art, as they are both directed to evaluating quality of manufacturing images. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Grodt into Sato and Chen because Grodt's edge profile calculation accurately determines edge symmetry and dimensional deviations based on contrast (see Grodt paragraph 0015).
Regarding claim 11, Sato in view of Chen fails to disclose the new limitations of claim 11.
Pertaining to the same field of endeavor, Grodt discloses wherein the raw image quality metric indicates an average of slopes determined at the gauges associated with each raw image (see citations to claim 9 above).
Sato and Grodt are considered analogous art, as they are both directed to evaluating quality of manufacturing images. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Grodt into Sato and Chen because Grodt's edge profile calculation accurately determines edge symmetry and dimensional deviations based on contrast (see Grodt paragraph 0015).
Claims 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Sato et al. (U.S. Publ. US-20030111602-A1) in view of Chen et al. (U.S. Publ. US-2019/0139224-A1), and further in view of Nakagaki et al. (U.S. Publ. US-2011/0261190-A1).
Regarding claim 12, Sato fails to disclose the new limitations of claim 12.
Pertaining to the same field of endeavor, Chen discloses wherein the instructions configured to determine the raw image quality metric are further configured to cause the one or more processors to: obtain a first contour of a feature within an average image of the plurality of raw images associated with a particular pattern (see figure 4 & paragraph 0046, where the edges/contours of an artifact are first obtained, but not from an average image);
obtain a second contour of the feature from each of the raw images associated with the particular pattern (see paragraph 0046, where the edges/contours of said artifact can then be analyzed from the vertical direction of the image);
and determine a distance between the first contour with the second contour (see paragraph 0047, where the relative dimensions of the horizontal and vertical edges/contours are then compared, which implicitly reads on comparing distances therebetween).
Sato and Chen are considered analogous art, as they are both directed to evaluating quality of substrate images. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Chen into Sato because comparing the horizontal & vertical contours differentiates between image artifacts and actual defects (see Chen paragraph 0047).
Chen fails to further disclose obtain a first contour of a feature within an average image of the plurality of raw images associated with a particular pattern (emphasis added via underline). In other words, Chen merely fails to disclose obtaining the first contour from an averaged image, as opposed to a single image.
Pertaining to the same field of endeavor, Nakagaki discloses obtain a first contour of a feature within an average image of the plurality of raw images associated with a particular pattern (see paragraphs 0008, 0015, where averaging of the same portion of different images is commonly performed for defect detection in wafer images).
Sato and Nakagaki are considered analogous art, as they are both directed to evaluating quality of substrate images. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Nakagaki into Sato and Nakagaki by using an averaged image for Chen’s artifact detection because doing so creates a high-quality image with a high signal-to-noise ratio (see Nakagaki paragraph 0015).
Regarding claim 13, Sato in view of Chen fails to disclose the new limitations of claim 13.
Pertaining to the same field of endeavor, Nakagaki discloses wherein the average image is obtained by: clustering of the raw images based on a characteristic of the feature; and averaging a cluster of raw images within a specified cluster region (see paragraphs 0008, 0015, where the image averaging is performed on a plurality of images of the same portion/cluster region of a feature area).
Sato and Nakagaki are considered analogous art, as they are both directed to evaluating quality of substrate images. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Nakagaki into Sato and Nakagaki by using an averaged image for Chen’s artifact detection because doing so creates a high-quality image with a high signal-to-noise ratio (see Nakagaki paragraph 0015).
Regarding claim 14, Sato in view of Nakagaki fails to disclose the new limitations of claim 14.
Pertaining to the same field of endeavor, Chen discloses wherein the instructions configured to obtain the second contour are further configured to cause the one or more processors to: determine an image property at contour locations associated with the feature within a given raw image; determine whether the image property breaches a threshold (see figure 4 & paragraph 0046, where the edges/contours of an artifact are first obtained, and the contrast property is measured and a threshold for high contrast is applied);
and responsive to the image property breaching the threshold, extract the second contour of the feature from the given raw image (see paragraph 0046, where the edges/contours of said artifact can then be analyzed from the vertical direction of the image, if high contrast was previously detected).
Sato and Chen are considered analogous art, as they are both directed to evaluating quality of substrate images. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Chen into Sato and Nakagaki because comparing the horizontal & vertical contours differentiates between image artifacts and actual defects (see Chen paragraph 0047).
Regarding claim 15, Sato in view of Nakagaki fails to disclose the new limitations of claim 15.
Pertaining to the same field of endeavor, Chen discloses wherein the image property is a local edge sharpness or contrast value at a location associated with the feature, or intensity at a contour of the feature (see citations to claims 12 and 14 above, where the image property is local contrast at the artifact edges).
Sato and Chen are considered analogous art, as they are both directed to evaluating quality of substrate images. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Chen into Sato and Nakagaki because comparing the horizontal & vertical contours differentiates between image artifacts and actual defects (see Chen paragraph 0047).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Sato et al. (U.S. Publ. US-20030111602-A1) in view of Chen et al. (U.S. Publ. US-2019/0139224-A1), and further in view of Kusnadi et al. (U.S. Publ. US-2009/0100389-A1).
Regarding claim 16, Sato in view of Chen fails to disclose the new limitations of claim 16.
Pertaining to the same field of endeavor, Kusnadi discloses wherein the instructions configured to determine the raw image quality metric are further configured to cause the one or more processors to: obtain contours of a feature within each raw image of the plurality of raw images associated with a particular pattern; and determine a matrix of a distance between a contour of each raw image of the plurality of raw images with a contour of each another raw image of the plurality of raw images (see figures 8-9 and paragraph 0037, where the distance between corresponding points on a contour are mapped from a printed feature image and a simulated feature image).
Sato and Kusnadi are considered analogous art, as they are both directed to evaluating quality of semiconductor images. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have integrated the teachings of Kusnadi into Sato and Chen because comparing cost functions, such as those of Kusnadi’s paragraph 0037, is useful for evaluating the accuracy of photolithographic models (see Kusnadi paragraph 0036).
Allowable Subject Matter
Claims 4-8 and 20 are objected to as being dependent upon a 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.
Regarding claims 4 and 6, the prior art fails to disclose or reasonably suggest using either of critical dimension/CD gauges (in the case of claim 4) or edge placement/EP gauges (in the case of claim 6) in the context of image quality measurement, as opposed to using these gauges for measuring the quality of semiconductors that are depicted in said images.
Pnueli et al. (“Image quality monitoring for enhanced precision and tool matching of CD measuring tools”, SPIE Proceedings paper, 24 May 2004) discloses a process of evaluating the quality of images to be used for measuring CD of wafers. The the quality of the images themselves, however, is only measured using signal-to-noise ratio, contrast-to-noise ratio, and resolution (see section 1, “Introduction”); image quality is not measured using the CD values themselves.
Banerjee et al.(“ICCAD-2013 CAD Contest in Mask Optimization and Benchmark Suite”, IEEE/ACM ICCAD paper, 2013) does disclose a method of measuring image quality using edge placement error/EPE (see page 272, left column), but this process notably depends on comparing the target layout values to the actual measured values (see section II.A, “Problem Statement” and figure 3). This is contrary to the amended independent claims now reciting that “the raw image quality metric [is] determined other than by reference to a target pattern corresponding to the one or more features”. Thus, Banerjee teaches away from the requirements of the independent claims before being combined with Sato and Chen to teach any dependent claims.
Regarding claims 5 and 7-8, these claims depend upon and further narrow claims 4 or 6 above.
Regarding claim 20, this dependent claim would be allowable for similar reasons to claim 4 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS JOHN HELCO whose telephone number is (703)756-5539. The examiner can normally be reached on Monday-Friday from 9:00 AM to 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella, can be reached at telephone number 571-272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NICHOLAS JOHN HELCO/Examiner, Art Unit 2667 /MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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