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
Acknowledgment is made of the information disclosure statements filed on 16 October 2025, U.S. patents and Foreign Patents have been considered.
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 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.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over US20230100578A1 (Cao) in view of US20240004305A1 (Tao) and further in view of US20120243772A1 (Yamanaka).
In regards to claim 1 (Cao) shows a mask layout correction method, comprising:
acquiring optical proximity correction (OPC)-ed layout images for masks using a pre-existing model, each of the masks including a curvilinear pattern; Cao [0050] teaches curvilinear masks including curvilinear SRAFs having polygonal shapes as opposed to Manhattan patterns having rectangular or staircase like shapes. Cao [0053] teaches curvilinear mask patterns may produce more accurate patterns on a substrate compared to Manhattan patterns.
correcting the OPC-ed layout images using the conversion model; Cao [0270-0272] teaches determining the mask pattern based on a difference between the simulated pattern and target pattern by modifying the input pattern and executing the model to generate a modified post-OPC pattern from which the mask pattern can be derived.
Cao differs from the claimed invention in that it does not explicitly disclose performing machine learning using the OPC-ed layout images and the mask contour images to generate a conversion model that is different from the pre-existing model; extracting mask contour images from scanning electron microscope (SEM) images of the masks manufactured based on the OPC-ed layout images;
Tao teaches performing machine learning using the OPC-ed layout images and the mask contour images to generate a conversion model that is different from the pre-existing model; Tao [0059] teaches a first OPC model may be an existing model employed in the OPC process, and a second model that is trained according to the present disclosure may be used to improve accuracy of the first OPC model where the first OPC model generates a mask image, and the second model generates improvements to the mask image. Tao [0061] teaches the first OPC model and the second model trained according to the present disclosure may be referred as two separate models where first OPC model may be a first CNN model and the second model may be a second CNN model. Tao [0103] teaches a design pattern DP may be input to a first machine learning model DL1 to generate a mask image MI where the mask image MI may be input to a second machine learning model DL2 trained according to the present disclosure to generate mask image modification data where DL1 and DL2 may be implemented as separate models.
Tao differs from the claimed invention in that it does not explicitly disclose extracting mask contour images from scanning electron microscope (SEM) images of the masks manufactured based on the OPC-ed layout images;
Yamanaka teaches extracting mask contour images from scanning electron microscope (SEM) images of the masks manufactured based on the OPC-ed layout images; Yamanaka [0015] teaches a method for extracting a contour of a pattern on a photo mask by acquiring, by a scanning electron microscope, information about a two-dimensional distribution of secondary electron intensity for a measurement target pattern formed on a photo mask. Yamanaka [0018] teaches acquiring a scanning electron microscope image of the mask pattern for an area around the measurement position. Yamanaka [0032] teaches data about the contour of the mask pattern is finally obtained from the SEM images. Yamanaka [0046] teaches the image processing system carries out edge position extraction on all edge positions contained in the SEM image to obtain final data on the contour of the mask pattern. Yamanaka [0057] teaches the contour of a pattern on a photo mask is extracted as part of a method for guaranteeing a photo mask. Yamanaka [0058] teaches the mask pattern is checked based on the extracted contour data, demonstrating inspection of manufactured masks.
The motivation to combine Cao and Tao at the effective filing date of the invention is to improve OPC model accuracy through sequential machine learning. Tao teaches using two separate models to improve accuracy over single model approaches like Cao, addressing the same OPC optimization problem.
The motivation to combine Cao, Tao, and Yamanaka at the effective filing date of the invention is to incorporate real manufactured mask feedback into the computational correction process. Yamanaka teaches using actual SEM mask measurements to improve model accuracy by incorporating manufacturing variations.
Claims 2, 4 – 7, 9, 10, 12, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over US20230100578A1 (Cao) in view of US20240004305A1 (Tao) and in view of US20120243772A1 (Yamanaka) and further in view of US20210216697A1 (Brink).
In regards to claim 2 (Cao) does not show: wherein the machine learning includes deep learning based on a generative adversarial network (GAN) algorithm;
Brink teaches wherein the machine learning includes deep learning based on a generative adversarial network (GAN) algorithm; Brink [0101] teaches that the machine learning model can be a neural network, convolutional neural network, Bayesian network, generalized linear model, deep learning model or other available machine learning models for predicting patterning device patterns.
The motivation to combine Cao and Tao at the effective filing date of the invention is to improve OPC model accuracy through sequential machine learning. Tao teaches using two separate models to improve accuracy over single model approaches like Cao, addressing the same OPC optimization problem.
The motivation to combine Cao, Tao, and Yamanaka at the effective filing date of the invention is to incorporate real manufactured mask feedback into the computational correction process. Yamanaka teaches using actual SEM mask measurements to improve model accuracy by incorporating manufacturing variations.
The motivation to combine Cao, Tao, Yamanaka, and Brink at the effective filing date of the invention is to enhance the OPC system with advanced machine learning algorithms and data management. Brink teaches GAN-based deep learning and database management that complement the existing OPC correction methods.
In regards to claim 4 (Cao) does not show: wherein, in the correcting of the OPC-ed layout images, the OPC-ed layout images are corrected based on the mask contour images of a target using the reverse model;
Brink teaches wherein, in the correcting of the OPC-ed layout images, the OPC-ed layout images are corrected based on the mask contour images of a target using the reverse model; Brink [0094] teaches the inverse process model is configured to predict a patterning device pattern using a wafer target layout, teaching use of target layout information as input to a prediction model. Brink [0105] teaches the trained inverse process model predicts a patterning device pattern using a wafer target layout as input, which may be a final mask pattern requiring no additional adjustment, effectively converting mask contour images into corresponding OPC-ed layout images.
The motivation to combine Cao and Tao at the effective filing date of the invention is to improve OPC model accuracy through sequential machine learning. Tao teaches using two separate models to improve accuracy over single model approaches like Cao, addressing the same OPC optimization problem.
The motivation to combine Cao, Tao, and Yamanaka at the effective filing date of the invention is to incorporate real manufactured mask feedback into the computational correction process. Yamanaka teaches using actual SEM mask measurements to improve model accuracy by incorporating manufacturing variations.
The motivation to combine Cao, Tao, Yamanaka, and Brink at the effective filing date of the invention is to enhance the OPC system with advanced machine learning algorithms and data management. Brink teaches GAN-based deep learning and database management that complement the existing OPC correction methods.
In regards to claim 5 (Cao) shows the mask layout correction method of claim 1:
performing mask rule check (MRC) on the corrected OPC-ed layout images; Cao [0078] teaches that assist features may be modified based on a mask rule check (MRC) and sidelobe printing check, showing that MRC is performed on the patterns.
determining that there is not a defect in the performing of the MRC; Cao [0078] teaches assist features may be modified based on a mask rule check, implying determination of whether MRC requirements are satisfied or defects exist.
Cao differs from the claimed invention in that it does not explicitly disclose wherein, the acquiring of the OPC-ed layout images comprises generating a database (DB) including the OPC-ed layout images; wherein the mask layout correction method further comprises, after the correcting of the OPC-ed layout images to generate corrected OPC-ed layout images: generating a new DB including the corrected OPC-ed layout images; determining the corrected OPC-ed layout images to be final OPC-ed layout images;
Brink teaches wherein, the acquiring of the OPC-ed layout images comprises generating a database (DB) including the OPC-ed layout images; Brink [0080] teaches that the initial mask pattern may be obtained from another inverse lithographic process, a design layout, or from a library of mask patterns, indicating storage in a database form.
Brink teaches wherein the mask layout correction method further comprises, after the correcting of the OPC-ed layout images to generate corrected OPC-ed layout images: generating a new DB including the corrected OPC-ed layout images; Brink [0080] teaches the initial mask pattern may be obtained from a library of mask patterns, demonstrating storage of pattern data in databases. Brink [0139] teaches an image processing system with a storage medium configured to store digital images and corresponding datasets in a reference database, which would include the corrected OPC-ed layout images.
Brink teaches determining the corrected OPC-ed layout images to be final OPC-ed layout images; Brink [0105] teaches that the predicted mask pattern may be a final mask pattern which does not require additional adjustment, determining it to be the final OPC-ed layout.
The motivation to combine Cao and Tao at the effective filing date of the invention is to improve OPC model accuracy through sequential machine learning. Tao teaches using two separate models to improve accuracy over single model approaches like Cao, addressing the same OPC optimization problem.
The motivation to combine Cao, Tao, and Yamanaka at the effective filing date of the invention is to incorporate real manufactured mask feedback into the computational correction process. Yamanaka teaches using actual SEM mask measurements to improve model accuracy by incorporating manufacturing variations.
The motivation to combine Cao, Tao, Yamanaka, and Brink at the effective filing date of the invention is to enhance the OPC system with advanced machine learning algorithms and data management. Brink teaches GAN-based deep learning and database management that complement the existing OPC correction methods.
In regards to claim 6 (Cao) does not show: wherein the performing of the MRC is a second performing of the MRC, the method comprising determining that there is a defect in a first performing the MRC; the method further comprising performing adjustment of an interval and a width of patterns in the corrected OPC-ed layout images, proceeded by the generating of the new DB;
Brink teaches wherein the performing of the MRC is a second performing of the MRC, the method comprising determining that there is a defect in a first performing the MRC; Brink [0110-0111] teaches that determining the patterning device layout is an iterative process that involves evaluating a cost function computing a difference between a simulated pattern and target layout, implying a first check identifying defects.
Brink teaches the method further comprising performing adjustment of an interval and a width of patterns in the corrected OPC-ed layout images, proceeded by the generating of the new DB; Brink [0086-0087] teaches adjusting by modifying the shape and size of features based on a gradient of the cost function, and determining a direction in which patterns should be modified to reduce the cost function.
The motivation to combine Cao and Tao at the effective filing date of the invention is to improve OPC model accuracy through sequential machine learning. Tao teaches using two separate models to improve accuracy over single model approaches like Cao, addressing the same OPC optimization problem.
The motivation to combine Cao, Tao, and Yamanaka at the effective filing date of the invention is to incorporate real manufactured mask feedback into the computational correction process. Yamanaka teaches using actual SEM mask measurements to improve model accuracy by incorporating manufacturing variations.
The motivation to combine Cao, Tao, Yamanaka, and Brink at the effective filing date of the invention is to enhance the OPC system with advanced machine learning algorithms and data management. Brink teaches GAN-based deep learning and database management that complement the existing OPC correction methods.
In regards to claim 7 (Cao) does not show: wherein the OPC-ed layout images are used as E-beam data for manufacturing the masks; wherein the E-beam data is updated or adjusted based on the correcting the OPC-ed layout images;
wherein the OPC-ed layout images are used as E-beam data for manufacturing the masks; Brink [0135-0137] teaches electron beam inspection involving focusing a beam of electrons on a substrate to form an image, showing e-beam technology used in the mask manufacturing process.
wherein the E-beam data is updated or adjusted based on the correcting the OPC-ed layout images; Brink [0135] teaches that electron beam inspection data is processed to identify defects, indicating that the e-beam data is updated based on the correction of the layouts.
The motivation to combine Cao and Tao at the effective filing date of the invention is to improve OPC model accuracy through sequential machine learning. Tao teaches using two separate models to improve accuracy over single model approaches like Cao, addressing the same OPC optimization problem.
The motivation to combine Cao, Tao, and Yamanaka at the effective filing date of the invention is to incorporate real manufactured mask feedback into the computational correction process. Yamanaka teaches using actual SEM mask measurements to improve model accuracy by incorporating manufacturing variations.
The motivation to combine Cao, Tao, Yamanaka, and Brink at the effective filing date of the invention is to enhance the OPC system with advanced machine learning algorithms and data management. Brink teaches GAN-based deep learning and database management that complement the existing OPC correction methods.
In regards to claim 9 (Cao) does not show: wherein responsive to the correcting the OPC-ed layout images to generate corrected OPC-ed layout images, the corrected OPC-ed layout images indicate a mask critical dimension (CD) offset in horizontal and vertical directions and an error occurring in the curvilinear pattern;
Brink teaches wherein responsive to the correcting the OPC-ed layout images to generate corrected OPC-ed layout images, the corrected OPC-ed layout images indicate a mask critical dimension (CD) offset in horizontal and vertical directions and an error occurring in the curvilinear pattern; Brink [0089] teaches that wafer data comprises measurements related to features including critical dimension, contour, edge placement error, and process window, which would indicate CD offsets and errors in the pattern.
The motivation to combine Cao and Tao at the effective filing date of the invention is to improve OPC model accuracy through sequential machine learning. Tao teaches using two separate models to improve accuracy over single model approaches like Cao, addressing the same OPC optimization problem.
The motivation to combine Cao, Tao, and Yamanaka at the effective filing date of the invention is to incorporate real manufactured mask feedback into the computational correction process. Yamanaka teaches using actual SEM mask measurements to improve model accuracy by incorporating manufacturing variations.
The motivation to combine Cao, Tao, Yamanaka, and Brink at the effective filing date of the invention is to enhance the OPC system with advanced machine learning algorithms and data management. Brink teaches GAN-based deep learning and database management that complement the existing OPC correction methods.
In regards to claim 10 (Cao) shows a mask layout correction method, comprising:
determining the corrected OPC-ed layout images to be final OPC-ed layout images, wherein the new DB is different from the DB; Cao [0074] teaches determining final corrected OPC patterns through iterative optimization processes that modify the original pattern database. Cao [0319] teaches the final OPC layout results differ from initial input patterns, creating a new database of optimized mask layouts distinct from the original design database.
determining that there is not a defect in the performing of the MRC; Cao [0078] teaches assist features may be modified based on a mask rule check, implying determination of whether MRC requirements are satisfied or defects exist.
performing mask rule check (MRC) on the corrected OPC-ed layout images; Cao [0078] teaches that assist features may be modified based on a mask rule check (MRC) and sidelobe printing check, showing that MRC is performed on the patterns.
correcting the OPC-ed layout images using the conversion model to generate corrected OPC-ed layout images; Cao [0270-0272] teaches determining the mask pattern based on a difference between the simulated pattern and target pattern by modifying the input pattern and executing the model to generate a modified post-OPC pattern from which the mask pattern can be derived.
Cao differs from the claimed invention in that it does not explicitly disclose performing deep learning based on a generative adversarial network (GAN) using the OPC-ed layout images and the mask contour images to generate a conversion model that is different from the pre-existing OPC model; extracting mask contour images from scanning electron microscope (SEM) images of the masks manufactured based on the OPC-ed layout images; generating a database (DB) including optical proximity correction (OPC)-ed layout images for masks using a pre-existing OPC model, each of the masks including a curvilinear pattern; generating a new DB including the corrected OPC-ed layout images;
Tao teaches performing deep learning based on a generative adversarial network (GAN) using the OPC-ed layout images and the mask contour images to generate a conversion model that is different from the pre-existing OPC model; Tao [0059] teaches a first OPC model may be an existing model employed in the OPC process, and a second model that is trained according to the present disclosure may be used to improve accuracy of the first OPC model where the first OPC model generates a mask image, and the second model generates improvements to the mask image. Tao [0061] teaches the first OPC model and the second model trained according to the present disclosure may be referred as two separate models where first OPC model may be a first CNN model and the second model may be a second CNN model. Tao [0103] teaches a design pattern DP may be input to a first machine learning model DL1 to generate a mask image MI where the mask image MI may be input to a second machine learning model DL2 trained according to the present disclosure to generate mask image modification data where DL1 and DL2 may be implemented as separate models.
Tao differs from the claimed invention in that it does not explicitly disclose extracting mask contour images from scanning electron microscope (SEM) images of the masks manufactured based on the OPC-ed layout images; generating a database (DB) including optical proximity correction (OPC)-ed layout images for masks using a pre-existing OPC model, each of the masks including a curvilinear pattern; generating a new DB including the corrected OPC-ed layout images;
Yamanaka teaches extracting mask contour images from scanning electron microscope (SEM) images of the masks manufactured based on the OPC-ed layout images; Yamanaka [0015] teaches a method for extracting a contour of a pattern on a photo mask by acquiring, by a scanning electron microscope, information about a two-dimensional distribution of secondary electron intensity for a measurement target pattern formed on a photo mask. Yamanaka [0018] teaches acquiring a scanning electron microscope image of the mask pattern for an area around the measurement position. Yamanaka [0032] teaches data about the contour of the mask pattern is finally obtained from the SEM images. Yamanaka [0046] teaches the image processing system carries out edge position extraction on all edge positions contained in the SEM image to obtain final data on the contour of the mask pattern. Yamanaka [0057] teaches the contour of a pattern on a photo mask is extracted as part of a method for guaranteeing a photo mask. Yamanaka [0058] teaches the mask pattern is checked based on the extracted contour data, demonstrating inspection of manufactured masks.
Tao differs from the claimed invention in that it does not explicitly disclose generating a database (DB) including optical proximity correction (OPC)-ed layout images for masks using a pre-existing OPC model, each of the masks including a curvilinear pattern; generating a new DB including the corrected OPC-ed layout images;
Brink teaches generating a database (DB) including optical proximity correction (OPC)-ed layout images for masks using a pre-existing OPC model, each of the masks including a curvilinear pattern; Brink [0072] teaches machine learning training using OPC layout images and mask contour data to build comprehensive databases of pattern relationships. Brink [0078] teaches obtaining a mask pattern that is a curvilinear mask pattern with curve-shaped features including SRAFs and Sherifs, which provides better wafer patterns compared to conventional Manhattan patterns. Brink [0101] teaches collecting OPC-ed layout images from the pre-existing model to form training datasets for subsequent processing.
Brink teaches generating a new DB including the corrected OPC-ed layout images; Brink [0139] teaches an image processing system with a storage medium configured to store digital images and corresponding datasets in a reference database, which would include the corrected OPC-ed layout images.
The motivation to combine Cao and Tao at the effective filing date of the invention is to improve OPC model accuracy through sequential machine learning. Tao teaches using two separate models to improve accuracy over single model approaches like Cao, addressing the same OPC optimization problem.
The motivation to combine Cao, Tao, and Yamanaka at the effective filing date of the invention is to incorporate real manufactured mask feedback into the computational correction process. Yamanaka teaches using actual SEM mask measurements to improve model accuracy by incorporating manufacturing variations.
The motivation to combine Cao, Tao, Yamanaka, and Brink at the effective filing date of the invention is to enhance the OPC system with advanced machine learning algorithms and data management. Brink teaches GAN-based deep learning and database management that complement the existing OPC correction methods.
In regards to claim 12 (Cao) does not show: wherein, in the correcting the OPC-ed layout images, the OPC-ed layout images are corrected based on mask contour images of a target using the reverse model;
Brink teaches wherein, in the correcting the OPC-ed layout images, the OPC-ed layout images are corrected based on mask contour images of a target using the reverse model; Brink [0105] teaches the trained inverse process model predicts a patterning device pattern using a wafer target layout as input, which may be a final mask pattern requiring no additional adjustment, effectively converting mask contour images into corresponding OPC-ed layout images.
The motivation to combine Cao and Tao at the effective filing date of the invention is to improve OPC model accuracy through sequential machine learning. Tao teaches using two separate models to improve accuracy over single model approaches like Cao, addressing the same OPC optimization problem.
The motivation to combine Cao, Tao, and Yamanaka at the effective filing date of the invention is to incorporate real manufactured mask feedback into the computational correction process. Yamanaka teaches using actual SEM mask measurements to improve model accuracy by incorporating manufacturing variations.
The motivation to combine Cao, Tao, Yamanaka, and Brink at the effective filing date of the invention is to enhance the OPC system with advanced machine learning algorithms and data management. Brink teaches GAN-based deep learning and database management that complement the existing OPC correction methods.
In regards to claim 13 (Cao) does not show: wherein the performing of the MRC is a second performing of the MRC, the method further comprising determining that there is a defect in a first performing of the MRC; wherein, when the defect is determined, an interval and a width of patterns in the corrected OPC-ed layout images are adjusted so that the MRC is satisfied, and the method proceeds to the generating the new DB;
Brink teaches wherein the performing of the MRC is a second performing of the MRC, the method further comprising determining that there is a defect in a first performing of the MRC; Brink [0110-0111] teaches that determining the patterning device layout is an iterative process that involves evaluating a cost function computing a difference between a simulated pattern and target layout, implying a first check identifying defects.
Brink teaches wherein, when the defect is determined, an interval and a width of patterns in the corrected OPC-ed layout images are adjusted so that the MRC is satisfied, and the method proceeds to the generating the new DB; Brink [0086-0087] teaches adjusting by modifying the shape and size of features based on a gradient of the cost function, and determining a direction in which patterns should be modified to reduce the cost function.
The motivation to combine Cao and Tao at the effective filing date of the invention is to improve OPC model accuracy through sequential machine learning. Tao teaches using two separate models to improve accuracy over single model approaches like Cao, addressing the same OPC optimization problem.
The motivation to combine Cao, Tao, and Yamanaka at the effective filing date of the invention is to incorporate real manufactured mask feedback into the computational correction process. Yamanaka teaches using actual SEM mask measurements to improve model accuracy by incorporating manufacturing variations.
The motivation to combine Cao, Tao, Yamanaka, and Brink at the effective filing date of the invention is to enhance the OPC system with advanced machine learning algorithms and data management. Brink teaches GAN-based deep learning and database management that complement the existing OPC correction methods.
Claims 15, 17, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US20230100578A1 (Cao) in view of US20240004305A1 (Tao) and in view of US20120243772A1 (Yamanaka) and in view of US20210216697A1 (Brink) and further in view of US20220299863A1 (Dong).
In regards to claim 15 (Cao) shows a mask manufacturing method comprising:
correcting the OPC-ed layout images using the conversion model to acquire final OPC-ed layout images; Cao [0270-0272] teaches determining the mask pattern based on a difference between the simulated pattern and target pattern by modifying the input pattern and executing the model to generate a modified post-OPC pattern from which the mask pattern can be derived.
Cao differs from the claimed invention in that it does not explicitly disclose performing deep learning based on a generative adversarial network (GAN) using the OPC-ed layout images and the mask contour images to generate a conversion model that is different from the pre-existing OPC model; extracting mask contour images from scanning electron microscope (SEM) images of the masks manufactured based on the OPC-ed layout images before forming a pattern on a semiconductor substrate; generating a database (DB) including optical proximity correction (OPC)-ed layout images for masks using a pre-existing OPC model, the masks including a curvilinear pattern; preparing mask data based on the MTO design data; transferring the final OPC-ed layout images as mask tape-out (MTO) design data; exposing a mask substrate for the masks based on the mask data;
Tao teaches performing deep learning based on a generative adversarial network (GAN) using the OPC-ed layout images and the mask contour images to generate a conversion model that is different from the pre-existing OPC model; Tao [0059] teaches a first OPC model may be an existing model employed in the OPC process, and a second model that is trained according to the present disclosure may be used to improve accuracy of the first OPC model where the first OPC model generates a mask image, and the second model generates improvements to the mask image. Tao [0061] teaches the first OPC model and the second model trained according to the present disclosure may be referred as two separate models where first OPC model may be a first CNN model and the second model may be a second CNN model. Tao [0103] teaches a design pattern DP may be input to a first machine learning model DL1 to generate a mask image MI where the mask image MI may be input to a second machine learning model DL2 trained according to the present disclosure to generate mask image modification data where DL1 and DL2 may be implemented as separate models.
Tao differs from the claimed invention in that it does not explicitly disclose extracting mask contour images from scanning electron microscope (SEM) images of the masks manufactured based on the OPC-ed layout images before forming a pattern on a semiconductor substrate; generating a database (DB) including optical proximity correction (OPC)-ed layout images for masks using a pre-existing OPC model, the masks including a curvilinear pattern; preparing mask data based on the MTO design data; transferring the final OPC-ed layout images as mask tape-out (MTO) design data; exposing a mask substrate for the masks based on the mask data;
Yamanaka teaches extracting mask contour images from scanning electron microscope (SEM) images of the masks manufactured based on the OPC-ed layout images before forming a pattern on a semiconductor substrate; Yamanaka [0015] teaches a method for extracting a contour of a pattern on a photo mask by acquiring, by a scanning electron microscope, information about a two-dimensional distribution of secondary electron intensity for a measurement target pattern formed on a photo mask. Yamanaka [0018] teaches acquiring a scanning electron microscope image of the mask pattern for an area around the measurement position. Yamanaka [0032] teaches data about the contour of the mask pattern is finally obtained from the SEM images. Yamanaka [0046] teaches the image processing system carries out edge position extraction on all edge positions contained in the SEM image to obtain final data on the contour of the mask pattern. Yamanaka [0057] teaches the contour of a pattern on a photo mask is extracted as part of a method for guaranteeing a photo mask. Yamanaka [0058] teaches the mask pattern is checked based on the extracted contour data, demonstrating inspection of manufactured masks.
Yamanaka differs from the claimed invention in that it does not explicitly disclose generating a database (DB) including optical proximity correction (OPC)-ed layout images for masks using a pre-existing OPC model, the masks including a curvilinear pattern; preparing mask data based on the MTO design data; transferring the final OPC-ed layout images as mask tape-out (MTO) design data; exposing a mask substrate for the masks based on the mask data;
Brink teaches generating a database (DB) including optical proximity correction (OPC)-ed layout images for masks using a pre-existing OPC model, the masks including a curvilinear pattern; Brink [0072] teaches machine learning training using OPC layout images and mask contour data to build comprehensive databases of pattern relationships. Brink [0078] teaches obtaining a mask pattern that is a curvilinear mask pattern with curve-shaped features including SRAFs and Sherifs, which provides better wafer patterns compared to conventional Manhattan patterns. Brink [0101] teaches collecting OPC-ed layout images from the pre-existing model to form training datasets for subsequent processing.
Brink teaches preparing mask data based on the MTO design data; Brink [0122] teaches the mask pattern may be verified to determine its manufacturability and may be directly manufactured. Brink [0123] teaches a mask manufacturing process using an e-beam writer, teaching preparation of mask data for manufacturing.
Brink differs from the claimed invention in that it does not explicitly disclose transferring the final OPC-ed layout images as mask tape-out (MTO) design data; exposing a mask substrate for the masks based on the mask data;
Dong teaches transferring the final OPC-ed layout images as mask tape-out (MTO) design data; Dong [0060] teaches passing the complete simulated pattern to the next stage to produce a photomask with imprinted layouts corresponding to the simulated pattern.
Dong teaches exposing a mask substrate for the masks based on the mask data; Dong [0060] teaches passing the complete simulated pattern to the next stage to produce a photomask with imprinted layouts corresponding to the simulated pattern, teaching exposing mask substrate based on pattern data.
The motivation to combine Cao and Tao at the effective filing date of the invention is to improve OPC model accuracy through sequential machine learning. Tao teaches using two separate models to improve accuracy over single model approaches like Cao, addressing the same OPC optimization problem.
The motivation to combine Cao, Tao, and Yamanaka at the effective filing date of the invention is to incorporate real manufactured mask feedback into the computational correction process. Yamanaka teaches using actual SEM mask measurements to improve model accuracy by incorporating manufacturing variations.
The motivation to combine Cao, Tao, Yamanaka, and Brink at the effective filing date of the invention is to enhance the OPC system with advanced machine learning algorithms and data management. Brink teaches GAN-based deep learning and database management that complement the existing OPC correction methods.
The motivation to combine Cao, Tao, Yamanaka, Brink, and Dong at the effective filing date of the invention is to complete the manufacturing workflow from OPC correction to physical mask production. Dong teaches the final MTO data transfer and mask exposure steps.
In regards to claim 17 (Cao) shows the mask manufacturing method of claim 15:
performing mask rule check (MRC) on the corrected OPC-ed layout images; Cao [0078] teaches that assist features may be modified based on a mask rule check (MRC) and sidelobe printing check, showing that MRC is performed on the patterns.
determining that there is not a defect in the performing the MRC; Cao [0078] teaches assist features may be modified based on a mask rule check, implying determination of whether MRC requirements are satisfied or defects exist.
Cao differs from the claimed invention in that it does not explicitly disclose wherein the correcting the OPC-ed layout images using the conversion model to acquire the final OPC-ed layout images includes: generating a new database (DB) including corrected OPC-ed layout images; determining the corrected OPC-ed layout images to be the final OPC-ed layout images;
Brink teaches wherein the correcting the OPC-ed layout images using the conversion model to acquire the final OPC-ed layout images includes: generating a new database (DB) including corrected OPC-ed layout images; Brink [0139] teaches an image processing system with a storage medium configured to store digital images and corresponding datasets in a reference database, which would include the corrected OPC-ed layout images.
Brink teaches determining the corrected OPC-ed layout images to be the final OPC-ed layout images; Brink [0105] teaches that the predicted mask pattern may be a final mask pattern which does not require additional adjustment, determining it to be the final OPC-ed layout.
The motivation to combine Cao and Tao at the effective filing date of the invention is to improve OPC model accuracy through sequential machine learning. Tao teaches using two separate models to improve accuracy over single model approaches like Cao, addressing the same OPC optimization problem.
The motivation to combine Cao, Tao, and Yamanaka at the effective filing date of the invention is to incorporate real manufactured mask feedback into the computational correction process. Yamanaka teaches using actual SEM mask measurements to improve model accuracy by incorporating manufacturing variations.
The motivation to combine Cao, Tao, Yamanaka, and Brink at the effective filing date of the invention is to enhance the OPC system with advanced machine learning algorithms and data management. Brink teaches GAN-based deep learning and database management that complement the existing OPC correction methods.
In regards to claim 18 (Cao) does not show: wherein the performing the MRC is a second performing of the MRC, the method further comprising determining that there is a defect in a first performing of the MRC and adjusting an interval and a width of patterns in the corrected OPC-ed layout images so that the MRC is satisfied, and followed by the generating the new DB;
Brink teaches wherein the performing the MRC is a second performing of the MRC, the method further comprising determining that there is a defect in a first performing of the MRC and adjusting an interval and a width of patterns in the corrected OPC-ed layout images so that the MRC is satisfied, and followed by the generating the new DB; Brink [0110-0111] teaches that determining the patterning device layout is an iterative process that involves evaluating a cost function computing a difference between a simulated pattern and target layout, implying a first check identifying defects. Brink [0086-0087] teaches adjusting by modifying the shape and size of features based on a gradient of the cost function, and determining a direction in which patterns should be modified to reduce the cost function.
The motivation to combine Cao and Tao at the effective filing date of the invention is to improve OPC model accuracy through sequential machine learning. Tao teaches using two separate models to improve accuracy over single model approaches like Cao, addressing the same OPC optimization problem.
The motivation to combine Cao, Tao, and Yamanaka at the effective filing date of the invention is to incorporate real manufactured mask feedback into the computational correction process. Yamanaka teaches using actual SEM mask measurements to improve model accuracy by incorporating manufacturing variations.
The motivation to combine Cao, Tao, Yamanaka, and Brink at the effective filing date of the invention is to enhance the OPC system with advanced machine learning algorithms and data management. Brink teaches GAN-based deep learning and database management that complement the existing OPC correction methods.
In regards to claim 19 (Cao) does not show:
Brink teaches wherein the mask data includes E-beam data; Brink [0123] teaches a mask manufacturing process using an e-beam writer, teaching that mask data is formatted as e-beam data for the e-beam writer. Brink [0135] teaches an electron beam apparatus involving focusing a beam of electrons onto a surface of a substrate. Brink [0137] teaches a primary electron beam emitted from an electron source that passes through beam deflectors to irradiate a substrate, teaching e-beam writing is performed using e-beam data to expose the mask substrate.
Brink teaches wherein E-beam writing is performed using the E-beam data in the exposing the mask substrate; Brink [0137] teaches a primary electron beam emitted from an electron source that is converged by condenser lens and passes through beam deflectors to irradiate a substrate, showing that E-beam writing is performed using the E-beam data in the exposing operation.
Brink teaches wherein the E-beam data is updated or adjusted based on the corrected OPC-ed layout images; Brink [0123] teaches a mask manufacturing process using an e-beam writer. Brink [0105] teaches the trained inverse process model results in a final mask pattern, teaching that corrected layout images update the pattern data used for manufacturing. Brink [0135] teaches that electron beam inspection data is processed to identify defects, indicating that the e-beam data is updated based on the correction of the layouts.
The motivation to combine Cao and Tao at the effective filing date of the invention is to improve OPC model accuracy through sequential machine learning. Tao teaches using two separate models to improve accuracy over single model approaches like Cao, addressing the same OPC optimization problem.
The motivation to combine Cao, Tao, and Yamanaka at the effective filing date of the invention is to incorporate real manufactured mask feedback into the computational correction process. Yamanaka teaches using actual SEM mask measurements to improve model accuracy by incorporating manufacturing variations.
The motivation to combine Cao, Tao, Yamanaka, and Brink at the effective filing date of the invention is to enhance the OPC system with advanced machine learning algorithms and data management. Brink teaches GAN-based deep learning and database management that complement the existing OPC correction methods.
Response to Argument
Applicant's arguments filed on June 26, 2025 have been fully considered but are moot in view of new ground of rejections as cited above.
With respect to independent claim 1, Applicant argues that the prior art fails to disclose performing machine learning to generate a conversion model that is different from the pre-existing model. This argument fails because Tao explicitly teaches using two separate models in sequence. Tao [0059] teaches a first OPC model may be an existing model employed in the OPC process, and a second model that is trained according to the present disclosure may be used to improve accuracy of the first OPC model where the first OPC model generates a mask image, and the second model generates improvements to the mask image. Tao [0061] teaches the first OPC model and the second model may be referred as two separate models where first OPC model may be a first CNN model and the second model may be a second CNN model. One of ordinary skill would have been motivated to combine Tao's sequential two-model architecture with Cao's OPC correction methodology and Yamanaka's SEM mask inspection to create a comprehensive feedback-based mask layout correction system.
With respect to independent claim 10, Applicant argues that the prior art does not teach the specific combination of performing machine learning with generating databases, extracting mask contours from SEM, and performing MRC. This argument fails because the combination of Tao, Brink, Yamanaka, and Cao teaches all claimed limitations through their respective teachings of sequential ML models [Tao 0059, 0061], database management [Brink 0139], SEM mask inspection [Yamanaka 0015, 0032, 0046], and MRC processes [Cao 0078]. The combination would have been obvious to create a comprehensive mask layout correction system.
With respect to independent claim 15, Applicant argues that the prior art fails to disclose the specific mask manufacturing method including extracting mask contours, performing machine learning, correcting layouts, and mask tape-out processes. This argument fails because the combination of Tao, Yamanaka, Cao, Brink, and Dong teaches all claimed limitations through their respective teachings of sequential ML models [Tao 0059, 0061], SEM mask inspection [Yamanaka 0015, 0032], pattern correction [Cao 0270-0272], database management [Brink 0139], and mask tape-out [Dong 0060]. The combination would have been obvious to create a complete mask manufacturing workflow.
The remaining arguments with respect to dependent claims 2, 4-7, 9, 12-13, 17-19 have been considered but are not persuasive for the reasons stated above. The dependent claims incorporate the limitations of their respective independent claims and are properly rejected over the same combination of references for the same reasons. The rejections are maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANWER AHMED ALAWDI whose telephone number is (703)756-1018. The examiner can normally be reached Monday - Friday 8:00 am - 5:30 pm.
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/ANWER AHMED ALAWDI/Examiner, Art Unit 2851
/JACK CHIANG/Supervisory Patent Examiner, Art Unit 2851