Prosecution Insights
Last updated: October 02, 2026
Application No. 18/557,578

HIERARCHICAL CLUSTERING OF FOURIER TRANSFORM BASED LAYOUT PATTERNS

Non-Final OA §103
Filed
Oct 26, 2023
Priority
Apr 29, 2021 — provisional 63/181,902 +1 more
Examiner
GOEBEL, EMMA ROSE
Art Unit
2662
Tech Center
2600 — Communications
Assignee
ASML Holding N.V.
OA Round
3 (Non-Final)
52%
Grant Probability
Moderate
3-4
OA Rounds
1m
Est. Remaining
86%
With Interview

Examiner Intelligence

Grants 52% of resolved cases
52%
Career Allowance Rate
36 granted / 69 resolved
-9.8% vs TC avg
Strong +34% interview lift
Without
With
+33.5%
Interview Lift
resolved cases with interview
Typical timeline
3y 0m
Avg Prosecution
26 currently pending
Career history
92
Total Applications
across all art units

Statute-Specific Performance

§101
17.4%
-22.6% vs TC avg
§103
61.5%
+21.5% vs TC avg
§102
10.6%
-29.4% vs TC avg
§112
8.2%
-31.8% vs TC avg
Black line = Tech Center average estimate • Based on career data from 69 resolved cases

Office Action

§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 . Continued Examination Under 37 CFR 1.114 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 July 1, 2026 has been entered. Priority Acknowledgement is made of Applicant’s claim of priority from U.S. Provisional Application No. 63/181,902, filed April 29, 2021 and PCT Application No. PCT/EP2022/061319, filed April 28, 2022. Status of Claims Claims 1-20 are pending. Response to Arguments With regards to the 35 USC § 103 rejection of claims 1-20, Applicant argues the claims as amended are not taught by the immediate prior art and the claims should not be rejected under 35 USC § 103. Applicant’s remarks and amendments have been fully considered and are found convincing, however, upon further search and consideration, the newly discovered Chae reference, as described in the updated rejection below, teaches the limitations as claimed. Please see below for full rejection. Accordingly, applicant’s amendments have necessitated the new grounds of rejection set forth, and rejection of the claims under 35 USC § 103 is maintained. 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. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. Claims 1, 8-9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 2013/0288403 A1) in view of Kim et al. (US 2018/0307792 A1) further in view of Yeongnam Chae (US 2019/0304096 A1). Regarding claim 1, Chen teaches a system for grouping a plurality of geometric patterns extracted from image data (Chen, Para. [0025], system for automatically classifying and clustering failure patterns that provides accurate failure pattern recognition), the system comprising: a controller including circuitry configured to cause the system to perform (Chen, Para. [0059], processes and logic flows described can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output): receiving the image data including the plurality of geometric patterns that represent features to be formed on a portion of a wafer (Chen, Para. [0042], a respective pattern for each of the respective selected wafer maps is classified using the respective determined feature and a plurality of stored pattern information. Stored pattern information can include pattern definitions and pattern templates. Pattern definitions can include feature information, object index information and/or wafer map index information, attribute information, object information and image information on the wafer map (e.g. pixel information, angle information) for respective patterns (i.e., image data including patterns)); separating the plurality of geometric patterns (Chen, Para. [0042], a respective pattern for each of the respective selected wafer maps is classified using the respective determined feature and a plurality of stored pattern information. A k-nearest neighbor algorithm is used to classify the respective pattern for each of the respective selected wafer maps); and performing, on a respective set of geometric patterns, a hierarchical clustering to obtain a plurality of subsets of geometric patterns by recursively evaluating features related to similarity between geometric patterns within the respective set of geometric patterns (Chen, Para. [0046], the plurality of respective classified patterns can be ranked using a clustering algorithm with stored patterns of substantially similar classification (e.g. localized, sector, center, scratch, edge, ring, radiation, donut or top/bottom.) In various embodiments, a hierarchical clustering method can be used to form a respective wafer fingerprint for each of the respective selected wafer maps. The distance of the closest points of the two clusters can be compared. If the computed cluster distance in either algorithm is below a certain predetermined threshold, then the two clusters are completely merged. Para. [0056], a plurality of the respective classified patterns are grouped into two or more pattern clusters. In various embodiments, a hierarchical clustering method or agglomerative clustering method can be used). Although Chen teaches separating the plurality of patterns (i.e., classifying respective patterns) (Chen, Para. [0042]), Chen does not explicitly teach separating the plurality of geometric patterns “after Fourier Transform”. However, in an analogous field of endeavor, Kim teaches the images of the portions of the design layout (i.e., plurality of patterns) may be processed by an image processing algorithm, using a Fourier transform to generating a Fourier transform representation of the image. The Fourier transform representations of the images may then be input into a machine learning algorithm for analyzing the design layout. The machine learning algorithm may extract parameters from each of the images to provide relevant characteristics of each portion of the design layout. The parameters extracted from each image may then be input to a clustering method which performs unsupervised machine learning on the dataset provided by the parameters from each image in order to find clusters in the dataset. The clustering may use a number of different ways of dividing the parameters into clusters, such as principal component analysis (PCA) (Kim, Paras. [0070]-[0071]). Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chen with the teachings of Kim by including performing the pattern classification (i.e., separating the plurality of patterns) after performing a Fourier transform on the images. One having ordinary skill in the art would have been motivated to combine these references because doing so would improve analysis of designs of integrated circuits, as recognized by Kim. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date. Although Chen in view of Kim teaches a set of geometric patterns (Chen, Para. [0046]), they do not explicitly teach “performing a cohesion test for evaluating the feature, the cohesion test comprising evaluating a cohesion degree of the respective set of geometric patterns to obtain an evaluation result, wherein the cohesion degree is a ratio between a number of datapoints within a test region and a total number of datapoints in the respective set of geometric patterns”. However, in an analogous field of endeavor, Chae teaches calculating the ratio of the number of pixels of a partial region (i.e., number of datapoints within a test region) to the total number of pixels contained in the template (i.e., total number of datapoints in the respective set of geometric patterns) (Chae, Para. [0071]). Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chen in view of Kim with the teachings of Chae by including determining a ratio between a number of datapoints (i.e., pixels) in a partial region (i.e., test region) and the number of datapoints (i.e., pixels) in the template (i.e., respective set of geometric patterns as described in Chen). One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for calculating a score based on a ratio of datapoints in regions of an image, as recognized by Chen. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date. Regarding claim 8, Chen in view of Kim further in view of Chae teaches the system of claim 1, wherein the image data is in Graphic Database System (GDS) format, Graphic Database System II (GDS II) format, Open Artwork System Interchange Standard (OASIS) format, or Caltech Intermediate Format (CIF) (Kim, Para. [0068], the design layout (i.e., image data) may be provided as a Graphic Design System (GDS) file, an Open Artwork System Interchange Standard (OASIS) file, or any other type of file that may carry a design layout). The proposed combination as well as the motivation for combining the Chen, Kim and Chae references presented in the rejection of Claim 1, apply to Claim 8 and are incorporated herein by reference. Thus, the system recited in Claim 8 is met by Chen in view of Kim further in view of Chae. Claim 9 recites a computer-readable storage medium storing a program with instructions corresponding to the elements recited in Claim 1. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding system claim. Additionally, the rationale and motivation to combine the Chen, Kim and Chae references, presented in rejection of Claim 1, apply to this claim. Finally, the combination of the Chen, Kim and Chae references discloses a computer readable storage medium (Chen, Para. [0060], one or more modules of computer program instructions encoded on a tangible machine readable storage medium for execution by, or to control the operation of, data processing apparatus. The tangible storage medium can be a computer readable medium). Claim 16 recites a method with steps corresponding to the elements of the system recited in Claims 1. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding system claim. Additionally, the rationale and motivation to combine the Chen, Kim and Chae references, presented in rejection of Claim 1, apply to this claim. Claims 2-4, 10-12 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 2013/0288403 A1) in view of Kim et al. (US 2018/0307792 A1) further in view of Yeongnam Chae (US 2019/0304096 A1), as applied to claims 1, 8-9 and 16 above, and further in view of Agarwal et al. (US 2015/0112649 A1). Regarding claim 2, Chen in view of Kim further in view of Chae teaches the system of claim 1, wherein the circuitry is further configured to cause the system to perform: performing Fourier Transform on the plurality of geometric patterns to obtain, respectively, a plurality of Fourier Transform based images in a frequency domain (Kim, Para. [0091], the Fourier transform representation is a two-dimensional representation in frequency domain of an image). The proposed combination as well as the motivation for combining the Chen, Kim and Chae references presented in the rejection of Claim 1, apply to Claim 2 and are incorporated herein by reference. Although Chen in view of Kim further in view of Chae teaches performing Fourier transform to obtain images in a frequency domain (Kim, Para. [0091]), they do not explicitly teach “obtaining a plurality of vectors based on the plurality of Fourier Transform based images respectively”. However, in an analogous field of endeavor, Agarwal teaches the frequency domain feature extraction mechanism extracts a feature vector from the center of the diffraction pattern (Agarwal, Para. [0046]). Each sample is an m-dimensional vector derived from the frequency domain representation of pattern j (Agarwal, Para. [0061]). Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the system of Chen in view of Kim further in view of Chae with the teachings of Agarwal by including extracting vectors from the Fourier transform based images (i.e., frequency domain representation of pattern). One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for clustering frequency domain features, as recognized by Agarwal. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date. Regarding claim 3, Chen in view of Kim further in view of Chae and Agarwal teaches the system of claim 2, and further teaches wherein the circuitry is further configured to cause the system to perform: evaluating similarity of the plurality of geometric patterns based on distance features of the plurality of vectors (Agarwal, Para. [0063], the mechanism computes a Fast Fourier Transform (FFT) for each clip and applies a distance metric to the resulting feature vector and the centroids of known hotspot clusters. If the distance is low (high similarity) to a cluster, than the clip represents a potential hotspot). The proposed combination as well as the motivation for combining the Chen, Kim, Chae and Agarwal references presented in the rejection of Claim 2, apply to Claim 3 and are incorporated herein by reference. Thus, the system recited in Claim 3 is met by Chen in view of Kim further in view of Chae and Agarwal. Regarding claim 4, Chen in view of Kim further in view of Chae teaches the system of claim 1, as described above. Although Chen in view of Kim further in view of Chae teaches k-means clustering (Kim, Para. [0099]), they do not explicitly teach “wherein the plurality of geometric patterns after Fourier Transform are separated into multiple sets of geometric patterns using a k-means algorithm based on the distance features”. However, in an analogous field of endeavor, Agarwal teaches the clustering algorithm uses the feature vectors to cluster hotspot clips. In one example embodiment, the clustering algorithm is a k-means clustering algorithm, which is a method of vector quantization originally from signal processing that is popular for cluster analysis in data mining. The k-means clustering algorithm aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster (Agarwal, Para. [0048]). K-means clustering aims to partition the n observations into k sets so as to minimize the within-cluster sum of squares (WCSS) (Agarwal, Para. [0049]). Assign each observation to the cluster whose mean yields the least within-cluster sum of squares. Since the sum of squares is the squared Euclidean distance, this is intuitively the “nearest” mean (Agarwal, Para. [0052]). The proposed combination as well as the motivation for combining the Chen, Kim, Chae and Agarwal references presented in the rejection of Claim 2, apply to Claim 4 and are incorporated herein by reference. Thus, the system recited in Claim 4 is met by Chen in view of Kim further in view of Chae and Agarwal. Claims 10-12 recite computer-readable storage mediums storing programs with instructions corresponding to the elements recited in Claims 2-4, respectively. Therefore, the recited programming instructions of these claims are mapped to the proposed combination in the same manner as the corresponding elements in their corresponding system claims. Additionally, the rationale and motivation to combine the Chen, Kim, Chae and Agarwal references, presented in rejection of Claim 2, apply to these claims. Finally, the combination of the Chen, Kim, Chae and Agarwal references discloses a computer readable storage medium (Chen, Para. [0060], one or more modules of computer program instructions encoded on a tangible machine readable storage medium for execution by, or to control the operation of, data processing apparatus. The tangible storage medium can be a computer readable medium). Claims 17-19 recite methods with steps corresponding to the elements of the systems recited in Claims 2-4, respectively. Therefore, the recited steps of these claims are mapped to the proposed combination in the same manner as the corresponding elements in their corresponding system claims. Additionally, the rationale and motivation to combine the Chen, Kim, Chae and Agarwal references, presented in rejection of Claim 2, apply to this claim. Claims 5-6, 13-14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 2013/0288403 A1) in view of Kim et al. (US 2018/0307792 A1) further in view of Yeongnam Chae (US 2019/0304096 A1), as applied to claims 1, 8-9 and 16 above, and further in view of Bergman et al. (US 2011/0013837 A1). Regarding claim 5, Chen in view of Kim further in view of Chae teaches the system of claim 1, as described above. Although Chen in view of Kim further in view of Chae teaches hierarchical clustering by recursively evaluating features (Chen, Para. [0046]), they do not explicitly teach “performing recursive partitions on the respective set of geometric patterns based on results of the recursively evaluating the feature at respective hierarchical levels”. However, in an analogous field of endeavor, Bergman teaches hierarchical recursive partitioning of an image (Bergman, Para. [0031]). After the graph has been created, the partitioning module recursively partitions the vertices into two subsets at each step. The recursive partitioning process can be terminated at any point, with the recursion varying in depth in different parts of the recursion tree (Bergman, Para. [0033]). Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chen in view of Kim further in view of Chae with the teachings of Bergman by including performing recursive partitions on the image (i.e., set of patterns) based on evaluating the feature at respective hierarchical levels. One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for automatic image segmentation in ways that require minimal manual intervention, as recognized by Bergman. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date. Regarding claim 6, Chen in view of Kim further in view of Chae and Bergman teaches the system of claim 5, and further teaches wherein the circuitry is further configured to cause the system to perform: performing the cohesion test includes determining whether to suspend the recursive partitions according to the evaluation result (Bergman, Para. [0092], FIG. 8 shows an embodiment of a method of terminating the hierarchical recursive partitioning in which the termination decision is based primarily on the difference between the two cut regions in the CIELAB color space or in color entropy). The proposed combination as well as the motivation for combining the Chen, Kim, Chae and Bergman references presented in the rejection of Claim 5, apply to Claim 6 and are incorporated herein by reference. Thus, the system recited in Claim 6 is met by Chen in view of Kim further in view of Chae and Bergman. Claims 13-14 recite computer-readable storage mediums storing programs with instructions corresponding to the elements recited in Claims 5-6, respectively. Therefore, the recited programming instructions of these claims are mapped to the proposed combination in the same manner as the corresponding elements in their corresponding system claims. Additionally, the rationale and motivation to combine the Chen, Kim, Chae and Bergman references, presented in rejection of Claim 5, apply to these claims. Finally, the combination of the Chen, Kim, Chae and Bergman references discloses a computer readable storage medium (Chen, Para. [0060], one or more modules of computer program instructions encoded on a tangible machine readable storage medium for execution by, or to control the operation of, data processing apparatus. The tangible storage medium can be a computer readable medium). Claim 20 recites a method with steps corresponding to the elements of the system recited in Claims 5. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding system claim. Additionally, the rationale and motivation to combine the Chen, Kim, Chae and Bergman references, presented in rejection of Claim 5, apply to this claim. Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 2013/0288403 A1) in view of Kim et al. (US 2018/0307792 A1) further in view of Yeongnam Chae (US 2019/0304096 A1) and Bergman et al. (US 2011/0013837 A1), as applied to claims 5-6, 13-14 and 20 above, and further in view of Niklas Karlsson (US 2017/0249652 A1). Regarding claim 7, Chen in view of Kim further in view of Chae and Bergman teaches the system of claim 6, as described above. Although Chen in view of Kim further in view of Chae and Bergman teaches a termination decision of recursive partition is based on an evaluation result (Bergman, Para. [0092]), they do not explicitly teach “receiving a user input indicating a parameter associated with evaluating the cohesion degree”. However, in an analogous field of endeavor, Karlsson teaches configuration parameters that can act as termination criteria for recursively partitioning P/V information to produce a representation of a P/V curve. It will be appreciated that the configuration parameters discussed above can be user defined parameters (e.g., through user input) (Karlsson, Para. [0041]). Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chen in view of Kim further in view of Chae and Bergman with the teachings of Karlsson by including that the parameter associated with evaluating the cohesion degree (i.e., the parameter for termination decision) is a user defined parameter (i.e., a user input indicating a parameter). One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for user-defined termination criteria for terminating a recursive partitioning, as recognized by Karlsson. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date. Claim 15 recites a computer-readable storage medium storing a program with instructions corresponding to the elements recited in Claim 7. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding system claim. Additionally, the rationale and motivation to combine the Chen, Kim, Chae, Bergman and Karlsson references, presented in rejection of Claim 7, apply to this claim. Finally, the combination of the Chen, Kim, Chae, Bergman and Karlsson references discloses a computer readable storage medium (Chen, Para. [0060], one or more modules of computer program instructions encoded on a tangible machine readable storage medium for execution by, or to control the operation of, data processing apparatus. The tangible storage medium can be a computer readable medium). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Emma Rose Goebel whose telephone number is (703)756-5582. The examiner can normally be reached Monday - Friday 7:30-5. 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, Amandeep Saini can be reached at (571) 272-3382. 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. /Emma Rose Goebel/Examiner, Art Unit 2662 /AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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Prosecution Timeline

Show 2 earlier events
Feb 20, 2026
Response Filed
Apr 03, 2026
Final Rejection mailed — §103
May 14, 2026
Response after Non-Final Action
Jul 01, 2026
Request for Continued Examination
Jul 02, 2026
Interview Requested
Jul 06, 2026
Response after Non-Final Action
Jul 14, 2026
Applicant Interview (Telephonic)
Aug 17, 2026
Non-Final Rejection mailed — §103 (current)

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Prosecution Projections

3-4
Expected OA Rounds
52%
Grant Probability
86%
With Interview (+33.5%)
3y 0m (~1m remaining)
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High
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