Prosecution Insights
Last updated: April 19, 2026
Application No. 18/178,528

DEEP LEARNING MODEL-BASED ALIGNMENT FOR SEMICONDUCTOR APPLICATIONS

Non-Final OA §102§103
Filed
Mar 05, 2023
Examiner
LU, ZHIYU
Art Unit
2665
Tech Center
2600 — Communications
Assignee
Kla Corporation
OA Round
1 (Non-Final)
49%
Grant Probability
Moderate
1-2
OA Rounds
3y 8m
To Grant
63%
With Interview

Examiner Intelligence

Grants 49% of resolved cases
49%
Career Allow Rate
374 granted / 759 resolved
-12.7% vs TC avg
Moderate +14% lift
Without
With
+13.9%
Interview Lift
resolved cases with interview
Typical timeline
3y 8m
Avg Prosecution
57 currently pending
Career history
816
Total Applications
across all art units

Statute-Specific Performance

§101
2.9%
-37.1% vs TC avg
§103
66.6%
+26.6% vs TC avg
§102
11.8%
-28.2% vs TC avg
§112
17.0%
-23.0% vs TC avg
Black line = Tech Center average estimate • Based on career data from 759 resolved cases

Office Action

§102 §103
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 . Election/Restrictions Applicant’s election without traverse of claims 1, 11-17, 20-21 in the reply filed on 10/09/2025 is acknowledged. 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)(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. Claim(s) 1, 17, 20-21 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Zhang et al. (US2022/0404712). To claim 1, Zhang teach a system configured to determine an offset for use in a process performed on a specimen, comprising: one or more computer subsystems; and one or more components executed by the one or more computer subsystems, wherein the one or more components comprise a deep learning model (paragraph 0169, deep CNN); and wherein the one or more computer subsystems are configured for: transforming design information for an alignment target on a specimen to a predicted image of the alignment target by inputting the design information into the deep learning model (925 of Fig. 9, paragraphs 0156-0157, image generator/machine learning model for predicting a measured image from design pattern); aligning the predicted image to an image of the alignment target on the specimen generated by an imaging subsystem (320 of Fig. 3, paragraph 0125, printed pattern can be imaged by an image capture device to generate measured image); determining an offset between the predicted image and the image generated by the imaging subsystem based on results of said aligning (1010 of Fig. 10, paragraphs 0159-0160); and storing the determined offset as an align-to-design offset for use in a process performed on the specimen with the imaging subsystem (730 of Fig. 7, paragraphs 0140-0143, calibrating the process model to reduce a difference, computed based on determined offset, between simulated contour and measured contour). To claim 20, Zhang teach a non-transitory computer-readable medium, storing program instructions executable on one or more computer systems for performing a computer-implemented method for determining an offset for use in a process performed on a specimen (as explained in response to claim 1 above). To claim 21, Zhang teach a computer-implemented method for determining an offset for use in a process performed on a specimen (as explained in response to claim 1 above). To claim 17, Zhang teach claim 1. Zhang teach wherein the process is an inspection process (paragraph 0125). 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. Claim(s) 11-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US2022/0404712). To claim 11, Zhang teach claim 1. Zhang teach wherein the one or more computer subsystems are further configured for performing the inputting, aligning, determining, and storing during runtime of the process performed on the specimen and during runtime of the process performed on one or more other specimens (obvious in paragraphs 0202-0206, storing temporary variables or other intermediate information during execution of instructions to be executed). To claim 12, Zhang teach claim 1. Zhang teach wherein the one or more computer subsystems are further configured for performing the process on the specimen with the imaging subsystem, and wherein the process comprises aligning a target image of an inspection area on the specimen to a design for the specimen based on the align-to-design offset (as explained in response to claim 1 above), transforming design information for the inspection area to a predicted target image of the inspection area by inputting the design information for the inspection area into the deep learning model (paragraphs 0032, 0147-0160, 0184, 0276-0277), subtracting the predicted target image from the aligned target image, and applying a defect detection method to results of the subtracting (despite lack of disclosure, applying defect detection method to results of subtracting is well-known technique in the art, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate, hence Official Notice is taken). To claim 13, Zhang teach claim 1. Zhang teach wherein the one or more computer subsystems are further configured for training the deep learning model and setting up the process, and wherein the training and setting up do not comprise storing a setup predicted image of the alignment target for use in the process performed on the specimen with the imaging subsystem (obvious in scenario or implementation by design preference). To claim 14, Zhang teach claim 13. Zhang teach wherein the one or more computer subsystems are further configured for performing the process on the specimen with the imaging subsystem and performing the inputting, aligning, determining, and storing during the process, and wherein the offset is a runtime-to-design offset (obvious in implementation, despite lack of disclosure, it is well-known in the art for implementation, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate, hence Official Notice is taken). To claim 15, Zhang teach claim 14. Zhang teach wherein the process further comprises accurately placing care areas on inspection images generated by the imaging subsystem during the process performed on the specimen based on the runtime-to-design offset (paragraphs 0130-0138, 0262, with explanation in response to claim 14 above). Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US2022/0404712) in view of Chen (US2021/0242059). To claim 16, Zhang teach claim 1. Zhang teach wherein the alignment target is one of multiple alignment targets located in a swath of images generated by the imaging subsystem, and wherein the one or more computer subsystems are further configured for performing the transforming, aligning, determining, and storing steps for the multiple alignment targets (paragraph 0274, 0279, 0285, 0288, multiple target images), but Zhang do not expressly disclose clustering the offsets determined for the multiple alignment targets to generate a clustered offset, and replacing one or more of the offsets determined for the multiple alignment targets with the clustered offset. Chen teach clustering the offsets determined for the multiple alignment targets to generate a clustered offset, and replacing one or more of the offsets determined for the multiple alignment targets with the clustered offset (paragraphs 0084-0093), which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate into the system of Zhang, in order to apply coarse alignment offset. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHIYU LU whose telephone number is (571)272-2837. The examiner can normally be reached Weekdays: 8:30AM - 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 R 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. ZHIYU . LU Primary Examiner Art Unit 2669 /ZHIYU LU/Primary Examiner, Art Unit 2665 March 14, 2026
Read full office action

Prosecution Timeline

Mar 05, 2023
Application Filed
Oct 09, 2025
Response after Non-Final Action
Mar 14, 2026
Non-Final Rejection — §102, §103 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
49%
Grant Probability
63%
With Interview (+13.9%)
3y 8m
Median Time to Grant
Low
PTA Risk
Based on 759 resolved cases by this examiner. Grant probability derived from career allow rate.

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