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 .
DETAILED ACTION
2. This Office Action responds to the Application filed on 1/25/2023 and IDS filed on 1/25/2023.
Claims 1-20 are pending.
Claim Rejections - 35 USC § 102
3. 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.
4. Claim(s) 1-6, 8, 11, and 17-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chiang et al. (U.S. Pub. No. 2019/0287021 A1).
As per claim 1, Chiang discloses:
A non-transitory computer-readable medium having instructions that, when executed by a computer system, cause the computer system to at least:
obtain images of multiple patterns, wherein the multiple patterns correspond to target patterns to be printed on a substrate (See Figure 1 & Para [0027], i.e. hotspot or non-hotspot pattern …. Layout clip defined by a core, See Para [0029]-[0030], i.e. part of a layout is a hotspot…extracts only possible layout clips based on polygon, See Claim 1, i.e. a first set of training data including a plurality of failure-inducing patterns); and
select a set of patterns from the multiple patterns based on the images as training data for training a first model (See Para [0032], i.e. hot spot training data … non-hotspot training data…training data contains plurality of different patterns, See Para [0036], i.e. selects the centroid of the classified non-hotspot patterns associated with the cluster to represent the associated non-hotspot cluster. In some embodiments, the hotspot detection system discards the rest of the non-hotspot patterns within this cluster, See Para [0039]-[0042], See Para [0049]-[0058], See Para [0081]-[0084], See Claim 1, i.e. selecting a plurality of representative patterns from the plurality of patterns classified into the second plurality of clusters, each representative pattern represents a corresponding cluster in the second plurality; extracting a plurality of features from each cluster in the first plurality and from the representative patterns; and training a kernel of a plurality of kernels using the extracted features to identify the unique failure-inducing topology in a circuit).
As per claim 2, Chiang discloses all of the features of claim 1 as discloses above wherein Chiang also discloses wherein the instructions configured to select the set of patterns are further configured to cause the one or more processors to group the multiple patterns into main patterns and special patterns respectively by using different clustering algorithms (See Para [0039], i.e. extracts topological (geometry-related) and non-topological (lithography-process-related) critical features from the data items which comprise the hotspot clusters, See Para [0044], i.e. extracted from the hotspot clusters and the non-hotspot clusters (e.g. the polygon density of core) to quickly identify whether the extracted layout clip is important or not. The clip extraction step reads in the testing layout and extract layout clips, then the hotspot detection system uses some geometrical information extracted from the hotspot clusters and the non-hotspot clusters to quickly abandon useless clips, See Para [0049]-[0057], i.e. a two-level topological classification…density-based classification on the string classified intermediate clusters).
As per claim 3, Chiang discloses all of the features of claim 2 as discloses above wherein Chiang also discloses wherein the instructions configured to group the multiple patterns are further configured to cause the one or more processors to generate a group of special patterns and multiple groups of main patterns from the multiple patterns, wherein the generation of the group of special patterns includes grouping of the multiple patterns based on a distance between feature vectors of the multiple patterns, wherein a distance between two feature vectors of the feature vectors is indicative of a difference between two patterns in the corresponding two images (See Para [0049]-[0057], i.e. The distance…between two patterns).
As per claim 4, Chiang discloses all of the features of claim 3 as discloses above wherein Chiang also discloses wherein the instructions configured to generate the group of special patterns are further configured to cause the one or more processors to: cluster the feature vectors based on density-based spatial clustering to identify a set of feature vectors as outliers; and determine patterns in the images corresponding to the set of feature vectors as the group of special patterns (See Para [0039], i.e. extracts topological (geometry-related) and non-topological (lithography-process-related) critical features from the data items which comprise the hotspot clusters, See Para [0044], i.e. extracted from the hotspot clusters and the non-hotspot clusters (e.g. the polygon density of core) to quickly identify whether the extracted layout clip is important or not. The clip extraction step reads in the testing layout and extract layout clips, then the hotspot detection system uses some geometrical information extracted from the hotspot clusters and the non-hotspot clusters to quickly abandon useless clips, See Para [0049]-[0057], i.e. a two-level topological classification…density-based classification on the string classified intermediate clusters).
As per claim 5, Chiang discloses all of the features of claim 3 as discloses above wherein Chiang also discloses wherein the instructions configured to generate the multiple groups of main patterns are further configured to cause the one or more processors to: determine those of the feature vectors not in the group of special patterns as a set of feature vectors corresponding to main patterns; and cluster the set of feature vectors into the multiple groups of main patterns (See Para [0032], i.e. hot spot training data … non-hotspot training data…training data contains plurality of different patterns, See Para [0036], i.e. selects the centroid of the classified non-hotspot patterns associated with the cluster to represent the associated non-hotspot cluster. In some embodiments, the hotspot detection system discards the rest of the non-hotspot patterns within this cluster, See Para [0039]-[0042], See Para [0049]-[0058], See Para [0081]-[0084], See Claim 1).
As per claim 6, Chiang discloses all of the features of claim 3 as discloses above wherein Chiang also discloses wherein the instructions configured to select the set of patterns from the multiple groups are further configured to cause the one or more processors to: select the group of special patterns and a representative main pattern from each group of main patterns, wherein selection of the representative main pattern from each group of main patterns includes: determination, for each group of main patterns, a centroid of the corresponding group of main patterns; and determination, for each group of main patterns, a feature vector that is closest to the centroid as a representative main pattern of the corresponding group of main patterns (See Para [0032], i.e. hot spot training data … non-hotspot training data…training data contains plurality of different patterns, See Para [0036], i.e. selects the centroid of the classified non-hotspot patterns associated with the cluster to represent the associated non-hotspot cluster. In some embodiments, the hotspot detection system discards the rest of the non-hotspot patterns within this cluster, See Para [0039]-[0042], See Para [0049]-[0058], See Para [0081]-[0084], See Claim 1).
As per claim 8, Chiang discloses all of the features of claim 2 as discloses above wherein Chiang also discloses wherein the instructions are further configured to cause the one or more processors to train the first model using the images corresponding to the group of special patterns and the main patterns to generate a simulated pattern to be printed on the substrate or to generate a mask pattern (See Para [0032], i.e. hot spot training data … non-hotspot training data…training data contains plurality of different patterns, See Para [0036], i.e. selects the centroid of the classified non-hotspot patterns associated with the cluster to represent the associated non-hotspot cluster. In some embodiments, the hotspot detection system discards the rest of the non-hotspot patterns within this cluster, See Para [0039]-[0042], See Para [0049]-[0058], See Para [0081]-[0084], See Claim 1, i.e. selecting a plurality of representative patterns from the plurality of patterns classified into the second plurality of clusters, each representative pattern represents a corresponding cluster in the second plurality; extracting a plurality of features from each cluster in the first plurality and from the representative patterns; and training a kernel of a plurality of kernels using the extracted features to identify the unique failure-inducing topology in a circuit).
As per claim 11, Chiang discloses all of the features of claim 1 as discloses above wherein Chiang also discloses wherein the instructions configured to output the set of patterns are further configured to cause the one or more processors to: determine a minimum distance of each pattern from any other patterns; and classify the patterns into one or more categories based on a distribution of the minimum distances of the patterns (See Para [0049]-[0057], i.e. The distance…between two patterns).
As per claim 17, Chiang discloses all of the features of claim 1 as discloses above wherein Chiang also discloses wherein the first model is a machine learning model, a non-machine learning model, or a combination thereof (See Para [0032], i.e. hot spot training data … non-hotspot training data…training data contains plurality of different patterns, See Para [0036], i.e. selects the centroid of the classified non-hotspot patterns associated with the cluster to represent the associated non-hotspot cluster. In some embodiments, the hotspot detection system discards the rest of the non-hotspot patterns within this cluster, See Para [0039]-[0042], See Para [0049]-[0058], See Para [0081]-[0084], See Claim 1).
As per claim 18, Chiang discloses:
A method of pattern selection, the method comprising:
obtaining images of multiple patterns, wherein the multiple patterns correspond to target patterns to be printed on a substrate (See Figure 1 & Para [0027], i.e. hotspot or non-hotspot pattern …. Layout clip defined by a core, See Para [0029]-[0030], i.e. part of a layout is a hotspot…extracts only possible layout clips based on polygon, See Claim 1, i.e. a first set of training data including a plurality of failure-inducing patterns); and
selecting, by a hardware computer system, a set of patterns from the multiple patterns based on the images as training data for training a model to predict patterns to be printed on a substrate (See Para [0032], i.e. hot spot training data … non-hotspot training data…training data contains plurality of different patterns, See Para [0036], i.e. selects the centroid of the classified non-hotspot patterns associated with the cluster to represent the associated non-hotspot cluster. In some embodiments, the hotspot detection system discards the rest of the non-hotspot patterns within this cluster, See Para [0039]-[0042], See Para [0049]-[0058], See Para [0081]-[0084], See Claim 1, i.e. selecting a plurality of representative patterns from the plurality of patterns classified into the second plurality of clusters, each representative pattern represents a corresponding cluster in the second plurality; extracting a plurality of features from each cluster in the first plurality and from the representative patterns; and training a kernel of a plurality of kernels using the extracted features to identify the unique failure-inducing topology in a circuit).
As per claim 19, Chiang discloses:
A non-transitory computer readable medium having instructions that, when executed by a computer system, cause the computer system to at least (See Figure 9, i.e. processor 902…memory 920):
obtain images of multiple patterns, wherein the multiple patterns correspond to target patterns to be printed on a substrate (See Figure 1 & Para [0027], i.e. hotspot or non-hotspot pattern …. Layout clip defined by a core, See Para [0029]-[0030], i.e. part of a layout is a hotspot…extracts only possible layout clips based on polygon, See Claim 1, i.e. a first set of training data including a plurality of failure-inducing patterns);
group the images into a group of special patterns and multiple groups of main patterns (See Para [0032], i.e. hot spot training data … non-hotspot training data…training data contains plurality of different patterns, See Para [0036], i.e. selects the centroid of the classified non-hotspot patterns associated with the cluster to represent the associated non-hotspot cluster. In some embodiments, the hotspot detection system discards the rest of the non-hotspot patterns within this cluster, See Para [0039]-[0042], See Para [0049]-[0058], See Para [0081]-[0084], See Claim 1, i.e. selecting a plurality of representative patterns from the plurality of patterns classified into the second plurality of clusters, each representative pattern represents a corresponding cluster in the second plurality; extracting a plurality of features from each cluster in the first plurality and from the representative patterns; and training a kernel of a plurality of kernels using the extracted features to identify the unique failure-inducing topology in a circuit –[prior art group non-hotspot patterns and hotspot patterns correspond to the group of special patterns and multiple groups of main patterns as cited above); and
output a set of patterns based on the images as training data for training a model configured to predict patterns to be printed on a substrate, wherein the set of patterns includes the group of special patterns and a representative main pattern from each group of main patterns (See Para [0032], i.e. hot spot training data … non-hotspot training data…training data contains plurality of different patterns, See Para [0036], i.e. selects the centroid of the classified non-hotspot patterns associated with the cluster to represent the associated non-hotspot cluster. In some embodiments, the hotspot detection system discards the rest of the non-hotspot patterns within this cluster, See Para [0039]-[0042], See Para [0049]-[0058], See Para [0081]-[0084], See Claim 1, i.e. selecting a plurality of representative patterns from the plurality of patterns classified into the second plurality of clusters, each representative pattern represents a corresponding cluster in the second plurality; extracting a plurality of features from each cluster in the first plurality and from the representative patterns; and training a kernel of a plurality of kernels using the extracted features to identify the unique failure-inducing topology in a circuit).
As per claim 20, Chiang discloses all of the features of claim 19 as discloses above wherein Chiang also discloses wherein the instructions configured to cause the computer system to group the images are further configured to cause the computer system to use different clustering algorithms to identify the group of special patterns and the multiple groups of main patterns See Para [0039], i.e. extracts topological (geometry-related) and non-topological (lithography-process-related) critical features from the data items which comprise the hotspot clusters, See Para [0044], i.e. extracted from the hotspot clusters and the non-hotspot clusters (e.g. the polygon density of core) to quickly identify whether the extracted layout clip is important or not. The clip extraction step reads in the testing layout and extract layout clips, then the hotspot detection system uses some geometrical information extracted from the hotspot clusters and the non-hotspot clusters to quickly abandon useless clips, See Para [0049]-[0057], i.e. a two-level topological classification…density-based classification on the string classified intermediate clusters).
Claim Rejections - 35 USC § 103
5. 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.
6. Claim(s) 7, 10, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chiang et al. (U.S. Pub. No. 2019/0287021 A1) in view of Song et al. (U.S. Pat. No. 9,646,127 B2).
As per claim 7, Chiang discloses all of the features of claim 1 as discloses above.
Chiang does not teach the limitations: wherein the images are simulated images comprising at least one selected from: resist images, mask images, aerial images and/or etch images.
However, Song teach the limitations: wherein the images are simulated images comprising at least one selected from: resist images, mask images, aerial images and/or etch images (See Col 1; Lines 30-57, See Col 4; Line 47 to Col 9; Line 33).
Therefore, it would have been obvious to a person of ordinary skill in the
art at the effective filing date of the invention to incorporate the teaching of Song into the
teaching of Chiang because it would improve accuracy and the efficiency of a process
model in lithography (See Col 1; Lines 18-27).
As per claim 10, Chiang discloses all of the features of claim 1 as discloses above.
Chiang does not teach the limitations: wherein the obtained images are resist images, and wherein the instructions configured to obtain the images of the multiple patterns are further configured to cause the one or more processors to: generate, using the pattern data, aerial images of the multiple patterns from a source model that is representative of optical properties of a lithographic apparatus; and generate, using the pattern data, resist images of the multiple patterns from aerial images using a resist model.
However, Song teach the limitations: wherein the obtained images are resist images, and wherein the instructions configured to obtain the images of the multiple patterns are further configured to cause the one or more processors to: generate, using the pattern data, aerial images of the multiple patterns from a source model that is representative of optical properties of a lithographic apparatus; and generate, using the pattern data, resist images of the multiple patterns from aerial images using a resist model (See Col 1; Lines 30-57, See Col 4; Line 47 to Col 9; Line 33).
Therefore, it would have been obvious to a person of ordinary skill in the
art at the effective filing date of the invention to incorporate the teaching of Song into the
teaching of Chiang because it would improve accuracy and the efficiency of a process
model in lithography (See Col 1; Lines 18-27).
As per claim 16, Chiang discloses all of the features of claim 1 as discloses above.
Chiang does not teach the limitations: wherein the first model is a resist model, an etch model or a combination thereof.
However, Song teach the limitations: wherein the first model is a resist model, an etch model or a combination thereof. (See Col 1; Lines 30-57, See Col 4; Line 47 to Col 9; Line 33).
Therefore, it would have been obvious to a person of ordinary skill in the
art at the effective filing date of the invention to incorporate the teaching of Song into the
teaching of Chiang because it would improve accuracy and the efficiency of a process
model in lithography (See Col 1; Lines 18-27).
Allowable Subject Matter
7. Claims 9 and 12-15 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.
8. The following is a statement of reasons for the indication of allowable subject matter: The prior art does not teach the limitations of claims 9, 12, 13, and/or 14 – wherein claim 15 depend on claim 12.
Conclusion
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to NHA T NGUYEN whose telephone number is (571)270-1405. The examiner can normally be reached M-F 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, Jack Chiang can be reached at 571-272-7483. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NHA T NGUYEN/Primary Examiner, Art Unit 2851
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