DETAILED ACTION
This Office Action is sent in response to Applicant’s Communication received 5/5/2025 for application number 17/281,123.
Claims 1-2, 4-21 are pending.
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 4 recites the limitation "The method of claim 3." There is insufficient antecedent basis for this limitation in the claim. Claim 3 has been cancelled, and it is unclear which claim this was meant to depend on. For the purposes of prior art, the Examiner assumes “claim 2,” was intended.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 2, 4-8, and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
For claims 2 and 16, representative claim 2 recites:
A method comprising: obtaining a machine learning model comprising (i) a generator model configured to generate a characteristic pattern to be printed on a substrate subjected to a patterning process, and (ii) a discriminator model configured to distinguish the characteristic pattern from a training pattern; and training, by a computer hardware system, the generator model and the discriminator model in a cooperative manner based on a training set comprising the training pattern, such that the generator model generates the characteristic pattern that matches the training pattern and the discriminator model identifies the characteristic pattern as the training pattern, wherein the characteristic pattern and the training pattern comprises a hotspot pattern; applying a design rule check to a pattern that is based on the characteristic pattern generated by the trained generator model;
wherein the training is an iterative process, an iteration comprises: generating the characteristic pattern, via simulation using the generator model with an input vector; evaluating a first cost function related to the generator model; distinguishing, via the discriminator model, the characteristic pattern from the training pattern; evaluating a second cost function related to the discriminator model; and adjusting one or more parameters of the generator model to improve the first cost function, and one or more parameters of the discriminator model to improve the second cost function.
(2A, prong 1) The underlined portions of the claim recite an abstract idea, specifically a mathematical calculation. Evaluating a cost function is a calculation of a mathematical function. (Applicant’s specification states that the first and second cost functions may be:
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(2A, prong 2) This judicial exception is not integrated into a practical application. The claims recite the additional limitations of [a] obtaining a generator and discriminator; [b] training the generator and discriminator so the generator creates hotspot patterns matching training data and the discriminator distinguishes if the generated patterns match the training data; [c] applying a design rule check; [d] iteratively training by the generator generating a hotspot pattern with an input vector, the discriminator distinguishing between the fake, generated hotspot pattern and real training data, and adjusting parameters of the generator and discriminator to improve the cost functions. These limitations are mere instructions to apply the exception. MPEP 2106.05(f) explains that in evaluating if a limitation is a mere instruction to apply, examiners may consider (1) if the claim only recites the idea of a solution or outcome and (2) if the claims invoke computers as a tool to perform an existing process. Here, limitations [b]-[d] only recite the idea of an outcome, without how the outcome is accomplished. Specifically, the claim does not explain how the generator creates hotspot patterns from an input vector, how the discriminator is able to distinguish between the generated patterns and training data, how the parameters are adjusted based on the cost functions, or what applying a design rule entails. In other words, these limitations merely state that the generator makes hotspot patterns and the discriminator distinguishes between generated and real patterns, without a mechanism for how the task is accomplished. Furthermore, limitation [a] invokes computers in their ordinary capacity of receiving, storing, and transmitting data. Obtaining the generator and discriminator models is using a computer in its normal capacity of receiving and storing data. Even when all of the additional elements are considered together with the abstract idea, they only add mere instructions to apply to the abstract idea, so the additional elements do not integrate the abstract idea into a practical application.
(2B) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above, additional elements [a]-[d] are mere instructions to apply the exception. Furthermore, even when all of the additional elements are considered in combination with the abstract idea, they only add mere instructions to apply to the abstract idea, so the additional elements do not amount to significantly more than the judicial exception itself. In combination, the additional elements merely use computers in their ordinary capacity for obtaining generator and discriminator models, and then recite the outcome of training the generator and discriminator to create hotspot images using the mathematical calculations of first and second cost functions, without explaining how they accomplish that outcome.
Dependent claims 4 and 17 recite:
4. The method of claim 3, wherein the input vector is a seed hotspot image and the seed hotspot image is obtained from simulation of a lithographic process with a design layout as an input.
17. The computer product of claim 16, wherein the input vector is a random vector and/or a seed hotspot image
(2A, prong 2) These additional limitations do not integrate the abstract idea into a practical application because they are field of use limitations. These limitations generally link the use of the mathematical calculation to the field of IC lithography. Also, these limitations do not alter or affect the rest of the process (in other words, the cost functions and GAN are not affected whether the input vector is limited to lithographic images or photos of animals; these limitations are just a token addition to the end of the claim to confine the field of use to IC lithography). Even when all of the additional elements are considered together with the abstract idea, they only add mere instructions to apply and field of use limitations to the abstract idea, so the additional elements do not integrate the abstract idea into a practical application.
(2B) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above, additional elements [a]-[d] are mere instructions to apply the exception. The limitations from claims 4 and 17 are field of use limitations that generally link the use of the mathematical calculation to the field of IC lithography, as explained above. Furthermore, even when all of the additional elements are considered in combination with the abstract idea, they only add mere instructions to apply and field of use limitations to the abstract idea, so the additional elements do not amount to significantly more than the judicial exception itself.
Dependent claims 5 and 20 recite:
5. The method of claim 2, wherein the distinguishing comprises: determining a probability that the characteristic pattern is the training pattern; and responsive to the probability, assigning a label to the characteristic pattern, the label indicating whether the characteristic pattern is a real pattern or a fake pattern.
20. The computer product of claim 16, wherein the distinguishing comprises: determination of a probability that the characteristic pattern is the training pattern; and responsive to the probability, assignment of a label to the characteristic pattern, the label indicating whether the characteristic pattern is a real pattern or a fake pattern.
(2A, prong 2) These claims recite the additional elements of the discriminator determining a probability of an input image being real, and in response, labeling the image as real or fake. These limitations are mere instructions to apply the exception because they do not explain how the discriminator determines a probability that the input image is fake, and labeling uses computers in their ordinary capacity to store data, i.e. store a label with the image. Even when all of the additional elements are considered together with the abstract idea, they only add mere instructions to apply to the abstract idea, so the additional elements do not integrate the abstract idea into a practical application.
(2B) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above, all of additional elements are mere instructions to apply the exception. Furthermore, even when all of the additional elements are considered in combination with the abstract idea, they only add mere instructions to apply and field of use limitations to the abstract idea, so the additional elements do not amount to significantly more than the judicial exception itself.
Dependent claims 6-8 and 18-19 recite:
6. The method of claim 2, wherein the first cost function comprises a log-likelihood term that determines a probability that the characteristic pattern is a fake given the input vector.
7. The method of claim 6, wherein the adjusting of one or more parameters of the generator model is such that the first log-likelihood term is minimized.
8. The method of claim 2, wherein the second cost function includes a log-likelihood term that determines a probability that the characteristic pattern is real given the training pattern.
18. (Previously Presented) The computer product of claim 16, wherein the first cost function comprises a log-likelihood term that determines a probability that the characteristic pattern is a fake given the input vector.
19. (Previously Presented) The computer product of claim 16, wherein the second cost function includes a log-likelihood term that determines a probability that the characteristic pattern is real given the training pattern.
(2A, prong 1) These claims add additional mathematical calculations to the abstract idea. The cost function comprising minimizing a log-likelihood term is a mathematical calculation.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1, 9-15, 17, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sha et al. (US 10,539,881 B1) in view of Sha et al. (US 2019/0377849 A1) hereinafter Sha II.
In reference to claim 1, Sha discloses a method (col. 1, line 20) comprising: obtaining a machine learning model comprising: (i) a generator model configured to generate a characteristic pattern to be printed on a substrate subjected to a patterning process (generator network is configured to create design layout patterns for printing, col. 6, line 55-58 col. 4, lines 10-26), and (ii) a discriminator model configured to distinguish the characteristic pattern from a training pattern (discriminator trained to distinguish fake / synthetic layouts from real layouts, col. 6, lines 59-61); and training, by a computer hardware system, the generator model and the discriminator model in a cooperative manner based on a training set comprising the training pattern, such that the generator model generates the characteristic pattern that matches the training pattern and the discriminator model identifies the characteristic pattern as the training pattern (the generator and discriminator are trained together so the generator is optimized to generate real-looking layout patterns and the discriminator is optimized to identify fake patterns from the discriminator, col. 6, line 52 – col. 7, line 3), wherein the characteristic pattern and the training pattern comprises a hotspot pattern (the generated patterns and training data are hotspot patterns, col. 5, lines 29-49; col. 6, lines 20-40; col. 7, lines 44-48; col. 12, lines 50-60).
However, Sha does not explicitly teach applying a design rule check to a pattern that is based on the characteristic pattern generated by the trained generator model.
Sha II teaches applying a design rule check to a pattern that is based on the characteristic pattern generated by the trained generator model (design rule check is applied to generated patterns as post-processing, para. 0024, 0046).
It would have been obvious to one of ordinary skill in art, having the teachings of Sha and Sha II before the earliest effective filing date, to modify the GAN as disclosed by Sha to include the design rule as taught by Sha II.
One of ordinary skill in the art would have been motivated to modify the GAN of Sha to include the design rule of Sha II because it can help ensure generated patterns conform with rules for particular layout patterns that may not be captured by training (Sha II, para. 0046).
In reference to claim 9, Sha teaches the method of claim 1, wherein the training pattern includes a hotspot pattern (training data includes layouts with hotspots, col. 6, lines 20-30).
In reference to claim 10, Sha does not explicitly teach the method of claim 1, wherein the training pattern is obtained from simulation using of a process model of the patterning process, from metrology data of a printed substrate, and/or from a database storing printed patterns.
Sha II teaches the method of claim 1, wherein the training pattern is obtained from simulation using of a process model of the patterning process, from metrology data of a printed substrate, and/or from a database storing printed patterns (training data is measurements of printed substrate, or metrology data, para. 0042, 58).
It would have been obvious to one of ordinary skill in art, having the teachings of Sha and Sha II before the earliest effective filing date, to modify the training data as disclosed by Sha to include the metrology data as taught by Sha II.
One of ordinary skill in the art would have been motivated to modify the training data of Sha to include the metrology data of Sha II because it would allow the GAN of Sha to be trained with more types of data.
In reference to claim 11, Sha teaches the method of claim 1, wherein the characteristic pattern includes features resembling the training pattern (both real and fake patterns have lithographic pattern features, col. 6, line 52 – col. 7, line 3).
In reference to claim 12, Sha does not explicitly teach the method of claim 1, wherein the characteristic pattern and the training pattern further comprises a non-hotspot pattern, and/or a user-defined pattern.
Sha II teaches the method of claim 1, wherein the characteristic pattern and the training pattern further comprises a non-hotspot pattern, and/or a user-defined pattern (training patterns can be non-hotspot, para. 0042, 58; generated patterns can also be non-hotspot, para. 0034).
It would have been obvious to one of ordinary skill in art, having the teachings of Sha and Sha II before the earliest effective filing date, to modify the patterns as disclosed by Sha to include the non-hotspot pattern as taught by Sha II.
One of ordinary skill in the art would have been motivated to modify the patterns of Sha to include the non-hotspot pattern of Sha II because it would allow the GAN of Sha to also generate valid patterns.
In reference to claim 13, Sha teaches the method of claim 1, further comprising generating, via simulation using the trained generator model, a design pattern including a hotspot pattern and/or a user-defined pattern (after training, generator can then generate hotspot design patterns, col. 12, line 50-col. 13, line 5).
In reference to claim 14, Sha teaches the method of claim 1, wherein the generator model and the discriminator model are convolution neural networks (col. 7, lines 36-38).
In reference to claim 15, this claim is directed to a computer-readable medium associated with the method claimed in claim 1 and is therefore rejected under a similar rationale.
In reference to claim 17, Sha teaches the computer product of claim 16, wherein the input vector is a random vector and/or a seed hotspot image (input to GAN is a seed layout of a lithographic process, col. 6, lines 41 – col. 7, line 48).
In reference to claim 21, this claim is directed to a computer-readable medium associated with the method claimed in claim 1 and is therefore rejected under a similar rationale.
Claim(s) 2, 4-8, 16, 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sha et al. (US 10,539,881 B1) in view of Sha et al. (US 2019/0377849 A1) hereinafter Sha II as applied to claims 1 and 15 above, and in further view of Yang et al., GAN-OPC: Mask Optimization with Lithography-guided Generative Adversarial Nets (see NPL attached to Non-Final action of 7/3/2024).
In reference to claim 2, Sha and Sha II do not explicitly teach the method of claim 1, wherein the training is an iterative process, an iteration comprises: generating the characteristic pattern, via simulation using of the generator model with an input vector; evaluating a first cost function related to the generator model; distinguishing, via the discriminator model, the characteristic pattern from the training pattern; evaluating a second cost function related to the discriminator model; and adjusting one or more parameters of the generator model to improve the first cost function, and one or more parameters of the discriminator model to improve the second cost function.
Yang teaches the method of claim 1, wherein the training is an iterative process (training is performed iteratively with mini-batches, page 3, heading 3.3), an iteration comprises: generating the characteristic pattern, via simulation using of the generator model with an input vector (generator creates layout with random input vector, page 2, heading 3); evaluating a first cost function related to the generator mode (loss function of generator is calculated, page 2, heading 3.1 and equation 4); distinguishing, via the discriminator model, the characteristic pattern from the training pattern; evaluating a second cost function related to the discriminator model (discriminator distinguishes between valid and invalid generated layout and evaluates loss function, page 3, headings 3.2 and 3.3 and equation 5); and adjusting one or more parameters of the generator model to improve the first cost function, and one or more parameters of the discriminator model to improve the second cost function (losses are used to adjust generator and discriminator parameters, page 3, heading 3.3).
It would have been obvious to one of ordinary skill in art, having the teachings of Sha, Sha II, and Yang before the earliest effective filing date, to modify the GAN as disclosed by Sha to include the iterative training as taught by Yang.
One of ordinary skill in the art would have been motivated to modify the GAN of Sha to include the iterative training of Yang because Sha does not explicitly disclose how to training the GAN, and Yang discloses a training process for a GAN that produces high-quality masks for target circuit patterns (Yang, page 1, abstract).
In reference to claim 4, Sha teaches the method of claim 3 (see 112(b) rejection above – examiner is assuming this claim is meant to depend on claim 2), wherein the input vector is a seed hotspot image and the seed hotspot image is obtained from simulation of a lithographic process with a design layout as an input (input to GAN is a seed layout of a lithographic process, col. 6, lines 41 – col. 7, line 48).
In reference to claim 5, Sha and Sha II do not explicitly teach the method of claim 2, wherein the distinguishing comprises: determining a probability that the characteristic pattern is the training pattern; and responsive to the probability, assigning a label to the characteristic pattern, the label indicating whether the characteristic pattern is a real pattern or a fake pattern.
Yang further teaches the method of claim 2, wherein the distinguishing comprises: determining a probability that the characteristic pattern is the training pattern; and responsive to the probability, assigning a label to the characteristic pattern, the label indicating whether the characteristic pattern is a real pattern or a fake pattern (based on log-likelihood, the discriminator labels patterns as real or fake, page 3, heading 3.2).
It would have been obvious to one of ordinary skill in art, having the teachings of Sha, Sha II, and Yang before the earliest effective filing date, to modify the GAN as disclosed by Sha to include the probability and labeling as taught by Yang.
One of ordinary skill in the art would have been motivated to modify the GAN of Sha to include the probability and labeling of Yang because Sha does not explicitly disclose how to training the GAN, and Yang discloses a training process for a GAN that produces high-quality masks for target circuit patterns (Yang, page 1, abstract).
In reference to claim 6, Sha and Sha II do not explicitly teach the method of claim 2, wherein the first cost function comprises a first log-likelihood term that determines a probability that the characteristic pattern is a fake given the input vector.
Yang teaches the method of claim 2, wherein the first cost function comprises a first log-likelihood term that determines a probability that the characteristic pattern is a fake given the input vector (page 3, headings 3.2 and 3.3, and equation 5).
It would have been obvious to one of ordinary skill in art, having the teachings of Sha, Sha II, and Yang before the earliest effective filing date, to modify the GAN as disclosed by Sha to include the log-likelihood as taught by Yang.
One of ordinary skill in the art would have been motivated to modify the GAN of Sha to include the log-likelihood of Yang because Sha does not explicitly disclose how to training the GAN, and Yang discloses a training process for a GAN that produces high-quality masks for target circuit patterns (Yang, page 1, abstract).
In reference to claim 7, Sha and Sha II do not explicitly teach the method of claim 6, wherein the adjusting of one or more parameters of the generator model is such that the first log-likelihood term is minimized.
Yang teaches the method of claim 6, wherein the adjusting of one or more parameters of the generator model is such that the first log-likelihood term is minimized (parameters of generator are adjusted to minimize log loss, page 3, heading 3.3).
It would have been obvious to one of ordinary skill in art, having the teachings of Sha, Sha II, and Yang before the earliest effective filing date, to modify the GAN as disclosed by Sha to include the log-likelihood as taught by Yang.
One of ordinary skill in the art would have been motivated to modify the GAN of Sha to include the log-likelihood of Yang because Sha does not explicitly disclose how to training the GAN, and Yang discloses a training process for a GAN that produces high-quality masks for target circuit patterns (Yang, page 1, abstract).
In reference to claim 8, Sha and Sha II do not explicitly teach the method of claim 2, wherein the second cost function includes a second log-likelihood term that determines a probability that the characteristic pattern is real given the training pattern.
Yang teaches the method of claim 2, wherein the second cost function includes a second log-likelihood term that determines a probability that the characteristic pattern is real given the training pattern (discriminator also determines log-likelihood of being real, para. 0074-76).
It would have been obvious to one of ordinary skill in art, having the teachings of Sha, Sha II, and Yang before the earliest effective filing date, to modify the GAN as disclosed by Sha to include the log-likelihood as taught by Yang.
One of ordinary skill in the art would have been motivated to modify the GAN of Sha to include the log-likelihood of Yang because Sha does not explicitly disclose how to training the GAN, and Yang discloses a training process for a GAN that produces high-quality masks for target circuit patterns (Yang, page 1, abstract).
In reference to claim 16, this claim is directed to a computer-readable medium associated with the method claimed in claim 2 and is therefore rejected under a similar rationale.
In reference to claim 18, this claim is directed to a computer-readable medium associated with the method claimed in claim 7 and is therefore rejected under a similar rationale.
In reference to claim 19, this claim is directed to a computer-readable medium associated with the method claimed in claim 8 and is therefore rejected under a similar rationale.
In reference to claim 20, this claim is directed to a computer-readable medium associated with the method claimed in claim 5 and is therefore rejected under a similar rationale.
Response to Arguments
Applicant’s arguments with respect to the rejection(s) under Tien have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Sha et al. (US 2019/0377849 A1).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew T. Chiusano whose telephone number is (571)272-5231. The examiner can normally be reached M-F, 10am-6pm.
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/ANDREW T CHIUSANO/Primary Examiner, Art Unit 2144
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144