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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on October 20, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Status of Claims
Claims 1 – 20 remain pending.
Claims 1 and 20 are Amended
Response to Arguments
Applicant’s arguments, see “Remarks”, filed December 03, 2025, with respect to the rejection(s) of claim(s) claims 1 – 20 under 35 USC § 102/103 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 Therefore, the rejection has been withdrawn.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 WO-2019005170-A1 (hereinafter Britson).
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.
Claims 1, 2, 7 – 15, 17 – 20 are rejected under 35 U.S.C 103 as being unpatentable over Ma et al. Patent Application Publication No. WO-2019190566-A1 (hereinafter Ma) in view of Britson Patent Application Publication No. WO-2019005170-A1 (hereinafter Britson).
Regarding claim 1, Ma discloses a non-transitory computer-readable medium having instructions that, when executed by a computer system, are configured to cause the computer system to at least (Ma in [0088] discloses, “An apparatus comprising a machine-readable medium having instructions which when executed by a machine cause the machine to perform operations”): provide an input that is representative of images of (a) a target pattern to be printed on a substrate (Ma in [0033] discloses, “the final model is re-applied via OPC correction to modify the physical mask such that features and structures are positioned in the correct location so that when a next silicon wafer is fabricated through the manufacturing process”) and (b) a reference layer pattern associated with the target pattern, to a machine learning model (Ma in [0015] discloses, “Disclosed herein is a multilayer optical proximity correction (OPC) tool and model for OPC ... that incorporates patterning effects from one or more neighboring reference layers of an IC design layout into full OPC correction of a current layer”. Furthermore, Ma in [0023] discloses, “current layer” will refer to the layer receiving OPC correction, and “reference layer(s)” will refer to the layer or layers whose design information aids the OPC correction of the current layer”. And [0031] of Ma discloses about machine learning model); the post-OPC result is for use in generating a post-OPC mask pattern to print the target pattern (Ma in [0033] discloses about mask generation, “the contour shift prediction 110 generated by the trained neural network 108 is applied to the semi-physical model to generate a final multilayer model or a final contour for the current layer. The final multilayer model is then used by a correction engine for the mask design to apply the appropriate correction and generate a corrected or new physical mask for the current layer”).
Ma doesn’t disclose about the following limitation as further recited in the claim.
Britson discloses output from the machine learning model a post-optical proximity correction (OPC) result based on the input (Britson in [0136] discloses, “The neural network 605 is trained to be able to predict the determined model error of the base OPC model so as to output an improved model using the contour shift, prediction 615 provided by the trained neural network 605”. Furthermore, Britson in [0180] discloses, “training the neural network to output offset predictions for the selected one or more mask segments that correspond to the improved, simulated image contrast of the mask when moved, in. which the output offset predictions are to he applied to a new semi-physical simulation via Optical Proximity Correction (OPC); and fabricating a new OPC corrected mask from the new semi-physical simulation”).
It would have been obvious to one with one having an ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Britson into the system of Ma because predicted post-OPC image could be used by the system o determine mask correction resulting in increasing the correction accuracy and reduced computational resources to obtain final result.
Summary of Citations (Britson)
Paragraph [0136]; “The neural network 605 is trained to be able to predict the determined model error of the base OPC model so as to output an improved model using the contour shift, prediction 615 provided by the trained neural network 605”.
Paragraph [0180]; “training the neural network to output offset predictions for the selected one or more mask segments that correspond to the improved, simulated image contrast of the mask when moved, in. which the output offset predictions are to he applied to a new semi-physical simulation via Optical Proximity Correction (OPC); and fabricating a new OPC corrected mask from the new semi-physical simulation”.
Summary of Citations (Ma)
Paragraph [0015]; “Disclosed herein is a multilayer optical proximity correction (OPC) tool and model for OPC. The disclosed embodiments introduce a multilayer software tool that incorporates patterning effects from one or more neighboring reference layers of an IC design layout into full OPC correction of a current layer”.
Paragraph [0023]; “Figure 1 is a diagram illustrating a process for optical proximity correction (OPC) utilizing a multilayer model. As used herein, “current layer” will refer to the layer receiving OPC correction, and “reference layer(s)” will refer to the layer or layers whose design information aids the OPC correction of the current layer”.
Paragraph [0031]; “Referring again to Figure 1, responsive to determining that the current layer is deformed by one or more reference layers, the semi-physical model and the design information of the reference layers are input into a trained machine learning algorithm to generate a contour shift prediction for the current layer, the contour shift prediction estimating a residual error of the semi -physical model (block 104)”.
Paragraph [0033]; “In particular, the contour shift prediction 110 generated by the trained neural network 108 is applied to the semi-physical model to generate a final multilayer model or a final contour for the current layer. The final multilayer model is then used by a correction engine for the mask design to apply the appropriate correction and generate a corrected or new physical mask for the current layer ... the final model is re-applied via OPC correction to modify the physical mask such that features and structures are positioned in the correct location so that when a next silicon wafer is fabricated through the manufacturing process”.
Paragraph [0088]; “An apparatus comprising a machine-readable medium having instructions which when executed by a machine cause the machine to perform operations”.
Regarding claim 2, 7 – 11 the same grounds of rejection based on Ma in view of Britson from the last Office Action (Non-Final, 09/11/2025) applies in here.
Regarding claim 12, claim 12 is claim 1 except for train the machine learning model using the first target pattern and the first reference layer pattern such that a difference between the first reference post-OPC result and a predicted post-OPC result of the machine learning model is reduced, thus the rejection of claim 1 is incorporated herein. With respect to the addition limitation, Britson in [0137] discloses, “The neural network 605 is trained to be able to predict the determined model error of the base OPC model so as to output an improved model using the contour shift, prediction 615 provided by the trained neural network 605”.
Summary of Citations (Britson)
Paragraph [0137]; “The neural network 605 is trained to be able to predict the determined model error of the base OPC model so as to output an improved model using the contour shift, prediction 615 provided by the trained neural network 605”.
Regarding claim 13 – 15 the same grounds of rejection based on Ma in view of Britson from the last Office Action (Non-Final, 09/11/2025) applies in here.
Regarding claim 17, is a non-transitory computer readable storage medium claim
corresponds to computer readable medium claim 12. Therefore, the rejection analysis of claim 12 is applied in claim 17.
Regarding claim 18, Britson in the combination discloses the computer-readable medium of claim 17, wherein the instructions configured to cause the computer system to train the machine learning model are further configured to cause the computer system to: provide the input to the machine learning model, generate the predicted post-OPC image using the machine learning model (Britson in [0180] discloses, “training the neural network to output offset predictions for the selected one or more mask segments that correspond to the improved, simulated image contrast of the mask when moved, in. which the output offset predictions are to be applied to a new semi-physical simulation via Optical Proximity Correction (OPC); and fabricating a new OPC corrected mask from the new semi-physical simulation”), compute a cost function that is indicative of a difference between the predicted post-OPC image and the first reference post-OPC image Britson in [0136] discloses, “The neural network 605 is trained to be able to predict the determined model error of the base OPC model so as to output an improved model using the contour shift, prediction 615 provided by the trained neural network 605”.
Ma further discloses adjust one or more parameters of the machine learning model such that the difference between the predicted post-OPC image and the first reference post-OPC image is reduced (Ma in [0054] discloses, “iterative training of the neural network further improves the predictive capability and thus improves the eventual final model and final contour for any mask structures and features”. Furthermore, Ma in [0047] discloses, “the target variables and the calculated density parameters of the reference layers are then used to train a machine learning algorithm to characterize the difference between a measured contour and a prediction of the residual errors in a multilayer OPC model (block 312)”).
Summary of Citations (Ma)
Paragraph [0047]; “the target variables and the calculated density parameters of the reference layers are then used to train a machine learning algorithm to characterize the difference between a measured contour and a prediction of the residual errors in a multilayer OPC model (block 312)”.
Paragraph [0054]; “iterative training of the neural network further improves the predictive capability and thus improves the eventual final model and final contour for any mask structures and features”.
Regarding claim 19, Claim 19 is similar in scope to claim 13, thus rejected under the same rationale.
Regarding claim 20, Claim 20 is similar in scope to claim 14, thus rejected under the same rationale.
Claims 3, 4, 6 and 16 are rejected under 35 U.S.C 103 as being unpatentable over Ma in view of Britson and further in view of Kang US Patent Publication No. US-12118708-B2 (hereinafter Kang).
Regarding claim 3, 4 and 6, the same grounds of rejection based on Ma in view of Britson and Kang from the last Office Action (Non-Final, 09/11/2025) applies in here.
Regarding claim 16, claim 16 is claims 1 – 3 except for train a machine learning model with the composite image to predict a post-optical proximity correction (OPC) image until a difference between the predicted post-OPC image and a reference post-OPC image corresponding to the composite image is minimized, wherein the post-OPC image is for use in obtaining a post-OPC mask for printing a target pattern on a substrate, thus the rejection of claim 1 – 3 is incorporated herein. With respect to the addition limitation, Britson in [0137] discloses, “the delta between the initial OPC contour 620 and the SEM con tour 625 is the determined model error of the base OPC model. The neural network 605 is trained to be able to predict the determined model error of the base OPC model so as to output an improved model using the contour shift, prediction 615 provided by the trained neural network 605”. And Ma in [0082] discloses about printing a target pattern on substrate, “the contour shift prediction for multilayer OPC correction of the current layer further comprises: applying the contour shift prediction to the semi-physical model to generate a final multilayer model; and using the final multilayer model by a correction engine for mask design to apply appropriate correction and generate a corrected new physical mask for the current layer”
Summary of Citations (Britson)
Paragraph [0133]; “The OPC" model provides a forward function which connects what is on the mask to what is on the wafer”.
Paragraph [0137]; “the delta between the initial OPC contour 620 and the SEM con tour 625 is the determined model error of the base OPC model. The neural network 605 is trained to be able to predict the determined model error of the base OPC model so as to output an improved model using the contour shift, prediction 615 provided by the trained neural network 605”.
Summary of Citations (Ma)
Paragraph [0047]; “Referring again to Figure 3, the target variables and the calculated density parameters of the reference layers are then used to train a machine learning algorithm to characterize the difference between a measured contour and a prediction of the residual errors in a multilayer OPC model (block 312)”.
Paragraph [0082]; “the contour shift prediction for multilayer OPC correction of the current layer further comprises: applying the contour shift prediction to the semi-physical model to generate a final multilayer model; and using the final multilayer model by a correction engine for mask design to apply appropriate correction and generate a corrected new physical mask for the current layer”.
Claims 5 is rejected under 35 U.S.C 103 as being unpatentable over Ma in view of Britson and Kang and further in view of Hsu Patent Application Publication No. WO-2019158682-A1 (hereinafter Hsu).
Regarding claim 5, the same grounds of rejection based on Ma in view of Britson, Kang and Hsu from the last Office Action (Non-Final, 09/11/2025) applies in here.
Conclusion
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/ZAID MUHAMMAD SALEH/
Examiner, Art Unit 2668
03/06/2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668