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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 04/29/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-7, 9-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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) 1-3, 9, 11-18, 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cao et al. (WO2019/162346) in view of Wang (US2018/0321596).
To claim 1, Cao teach a non-transitory computer-readable medium comprising instructions stored therein that, when executed by one or more processors, cause the one or more processors to at least:
obtain an image having a plurality of patterns (paragraph 0104, SEM image);
determine, based on pixel intensities within the image (paragraph 0104, image pixel intensity), a metric indicative of a level of informativeness contained in one or more portions of the image (paragraph 0104, a cost function that describes the difference between a predicted resist image and an experimentally measured resist image SEM image. The cost function can be based on image pixel intensity difference, contour to contour difference, or CD difference, etc.) without simulation of one or more of the plurality of patterns using a process model associated with a patterning process, or without application, using one or more of the plurality of patterns, of a machine learning model associated with the patterning process (paragraph 0104, obviously without simulation, which satisfies one of claimed alternative limitations);
select, based on the metric, a sub-set of the plurality of patterns from the one or more portions of the image having values of the metric within a specified range (Figs. 4-5, 6, 10A-C, paragraphs 0081-0083, 0097, further training and/or fine-tuning the trained process models based on a first training data set, e.g., printed patterns, and a first cost function, e.g., difference between printed patterns and predicted patterns; which means pattern mask image as output of one machine learning model based on the cost function would be input as training data to another machine learning model, wherein said output or pattern mask image is only a portion of a design layout); and
provide the sub-set of patterns as training data for training a model associated with a patterning process (paragraph 0104, the training process may involve reducing, in an embodiment, minimize, a cost function; paragraph 0109, training a process model of a patterning process to predict a pattern on a substrate).
In further strengthening said obviousness, Wang teach a relationship between one or more directly measureable processing parameters and a not directly measureable processing parameter may be retrieved from a database or established by an experiment (Fig. 2; paragraph 0090, wherein simulation is not necessary while machine learning is not available).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Wang into the apparatus of Cao, in order to implement processing by design preference.
To claim 16, Cao and Wang teach a method for generating training data for training a model (as explained in response to claim 1 above).
To claims 2 and 17, Cao and Wang teach claims 1 and 16.
Cao teach wherein the level of informativeness corresponds to non-homogeneity of each of the plurality of patterns, an uncertainty associated with a model prediction, or an error associated with a model prediction (paragraph 0104).
To claims 3 and 18, Cao and Wang teach claims 1 and 16.
Cao teach wherein the instructions configured to determine the metric are further configured to cause the one or more processors to generate information content data by applying the metric to one or more pixels of the image (paragraph 0104).
To claim 9, Cao and Wang teach claim 1.
Cao teach wherein the instructions configured to select the sub-set of patterns are further configured to cause the one or more processors to: compare values of the metric across the image; identify portions of the image corresponding to values of the metric within the specified range; and select selecting the sub-set of patterns within the identified portions (paragraphs 0097, 0104, wherein processes of comparing, identifying, and selecting are embedded).
To claim 11, Cao and Wang teach claim 1.
Cao teach wherein the sub-set of patterns comprises at least a portion of a pattern of the sub-set of patterns (paragraph 0097).
To claim 12, Cao and Wang teach claim 1.
Cao teach wherein the image is at least one selected from: a design layout comprising patterns to be printed on a substrate; or a SEM image of a patterned substrate acquired via a scanning electron microscope (SEM) (paragraph 0104).
To claim 13, Cao and Wang teach claim 1.
Cao teach wherein the image is at least one selected from: a binary image, a grey scale image; or
a n-channel image, wherein n refers to number of colors used in the image (paragraph 0081).
To claim 14, Cao and Wang teach claim 1.
Cao teach wherein the instructions are further configured to cause the one or more processors to train, using the sub-set of patterns as training data, a model associated with the patterning process (paragraphs 0097, 0124, CTM-CNN training).
To claim 15, Cao and Wang teach claim 14.
Cao teach wherein the instructions configured to train the model are further configured to cause the one or more processors to train a model configured to generate optical proximity correction structures associated with the plurality of patterns of a design layout, wherein the optical proximity correction structures comprises one or more selected from: main features corresponding to the plurality of patterns of the design layout; or assist features surrounding the plurality of patterns of the design layout (paragraph 0097, using the trained process models to train another machine learning model e.g., 8002 configured to predict mask pattern e.g., including OPC).
To claim 21, Cao and Wang teach a non-transitory computer-readable medium comprising instructions stored therein that, when executed by one or more processors, cause the one or more processors to at least: obtain an image having a plurality of patterns; determine, based on pixel intensities within the image, a metric indicative of a level of informativeness contained in one or more portions of the image, wherein the level of informativeness corresponds to non-homogeneity of each of the plurality of patterns, an uncertainty associated with a model prediction, or an error associated with a model prediction; select, based on the metric, a sub-set of the plurality of patterns from the one or more portions of the image having values of the metric within a specified range; and provide the sub-set of patterns as training data for training a model associated with a patterning process (as explained in responses to claims 1-2 above).
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) 4, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cao et al. (WO2019/162346) in view of Wang (US2018/0321596) and Aparna et al. (“Application of Image Intensity Local Variance Measure for Analysis of Distorted Images”).
To claims 4 and 19, Cao and Wang teach claims 3 and 18.
But, Cao and Wang do not expressly disclose wherein the instructions configured to generate the information content data are further configured to cause the one or more processors to: slide a window of specified shape and/or size through the image; and compute, for each sliding position, a value of the metric applied within the window.
Aparna teach instructions configured to generate the information content data are further configured to cause the one or more processors to: slide a window of specified shape and/or size through the image; and compute, for each sliding position, a value of the metric applied within the window (page 383, III), 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 apparatus of Cao and Wang, in order to further detail in information generation.
Claim(s) 5-7, 10, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cao et al. (WO2019/162346) in view of Wang (US2018/0321596) and Yin et al. (“Unsupervised Hierarchical Image Segmentation Through Fuzzy Entropy Maximization”).
To claims 5 and 20, Cao and Wang teach claims 1 and 16.
But, Cao and Wang do not expressly disclose wherein the metric is at least one selected from: an information entropy, Renyi entropy, or differential entropy.
Yin teach the metric is at least one selected from: an information entropy, Renyi entropy, or differential entropy (pages 245-246), 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 apparatus of Cao and Wang, in order to further detail in information generation.
To claim 6, Cao, Wang and Yin teach claim 5.
Cao, Wang and Yin teach wherein the metric comprises an information entropy and the information entropy comprises a sum of products of a probability of an outcome of a plurality of possible outcomes associated with the image and a logarithmic function of the probability of the outcome (Yin, page 248, section 4).
To claim 7, Cao, Wang and Yin teach claim 6.
Cao, Wang and Yin teach wherein the possible outcomes comprises at least one selected from: a binary value assigned to a pixel of the image, a first value being indicative of presence of a pattern within the image and a second value being indicative of absence a pattern within the image; or a grey scale value assigned to a pixel of the image (Yin, page 249, section 5.1).
To claim 10, Cao, Wang and Yin teach claim 5.
Cao, Wang and Yin teach wherein the instructions configured to select the sub-set of patterns are further configured to cause the one or more processors to: identify portions of the image corresponding to relatively low entropy values compared to other portions; and select the sub-set of patterns within the identified portion (low entropy means an image with high predictability and low complexity, obvious in paragraphs 0090, 0097, 0154 of Cao).
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.
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ZHIYU . LU
Primary Examiner
Art Unit 2669
/ZHIYU LU/Primary Examiner, Art Unit 2665 May 16, 2026
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