Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This office action is in response to the original filing of 06/20/2023. Claims 1-23 are pending and have been considered below.
Objection
3. Claims 1, 10 and 18 objected to because of the following informalities: the acronym “SRAF” is not defined in the claims. The claims should be amended to expressly recite the full term represented by the acronym “SRAF” upon its first occurrence, thereby providing clear antecedent terminology and ensuring that the scope of the claims is readily understood. Appropriate correction is required.
Claim Rejections - 35 USC § 101
4. 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 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention, when the claims are taken as a whole, is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1, the claim recites a method which falls into one of the statutory categories.
2A – Prong 1: Claim 1, in part, recites
“receiving a layout design”; “classifying layout features in the layout design into groups of layout features”; “dividing regions where sub-resolution assist features are likely to be placed into areas of interest, each of the areas of interest having the same size and shape as the small area, extracting a feature vector for each of the areas of interest based on a layout area centered at the each of the areas of interest, determining whether the each of the areas of interest should be part of a sub-resolution assist feature by using the feature vector as an input of the specific machine learning model” and “generating the sub-resolution assist features based on results of the determining” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper
2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, claim 1 further recites “performing a machine learning-based SRAF generation process to generate sub-resolution assist features for layout features in each of the groups of layout features, each of the groups of layout features having a specific machine learning model for determining whether a small area should be part of a sub-resolution assist feature, size and shape of the small area being preset, the machine learning-based SRAF generation process comprising:” mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f)
“storing information of the sub-resolution assist features” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites“ performing a machine learning-based SRAF generation process to generate sub-resolution assist features for layout features in each of the groups of layout features, each of the groups of layout features having a specific machine learning model for determining whether a small area should be part of a sub-resolution assist feature, size and shape of the small area being preset, the machine learning-based SRAF generation process comprising:” mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f)
“storing information of the sub-resolution assist features” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Claim 10
2A – Prong 1: Claim 10, in part, recites
“receiving a layout design”; “classifying layout features in the layout design into groups of layout features”; “dividing regions where sub-resolution assist features are likely to be placed into areas of interest, each of the areas of interest having the same size and shape as the small area, extracting a feature vector for each of the areas of interest based on a layout area centered at the each of the areas of interest, determining whether the each of the areas of interest should be part of a sub-resolution assist feature by using the feature vector as an input of the specific machine learning model” and “generating the sub-resolution assist features based on results of the determining” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper
2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, claim 1 further recites “performing a machine learning-based SRAF generation process to generate sub-resolution assist features for layout features in each of the groups of layout features, each of the groups of layout features having a specific machine learning model for determining whether a small area should be part of a sub-resolution assist feature, size and shape of the small area being preset, the machine learning-based SRAF generation process comprising:” mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f)
“storing information of the sub-resolution assist features” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
“one or more processors, and computer-readable media storing computer-executable instructions” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites“ performing a machine learning-based SRAF generation process to generate sub-resolution assist features for layout features in each of the groups of layout features, each of the groups of layout features having a specific machine learning model for determining whether a small area should be part of a sub-resolution assist feature, size and shape of the small area being preset, the machine learning-based SRAF generation process comprising:” mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f)
“storing information of the sub-resolution assist features” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)
“one or more processors, and computer-readable media storing computer-executable instructions” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 18
2A – Prong 1: Claim 18, in part, recites
“receiving a layout design”; “classifying layout features in the layout design into groups of layout features”; “dividing regions where sub-resolution assist features are likely to be placed into areas of interest, each of the areas of interest having the same size and shape as the small area, extracting a feature vector for each of the areas of interest based on a layout area centered at the each of the areas of interest, determining whether the each of the areas of interest should be part of a sub-resolution assist feature by using the feature vector as an input of the specific machine learning model” and “generating the sub-resolution assist features based on results of the determining” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper
2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, claim 1 further recites “performing a machine learning-based SRAF generation process to generate sub-resolution assist features for layout features in each of the groups of layout features, each of the groups of layout features having a specific machine learning model for determining whether a small area should be part of a sub-resolution assist feature, size and shape of the small area being preset, the machine learning-based SRAF generation process comprising:” mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f)
“storing information of the sub-resolution assist features” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
“one or more processors, and computer-readable media storing computer-executable instructions” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites“ performing a machine learning-based SRAF generation process to generate sub-resolution assist features for layout features in each of the groups of layout features, each of the groups of layout features having a specific machine learning model for determining whether a small area should be part of a sub-resolution assist feature, size and shape of the small area being preset, the machine learning-based SRAF generation process comprising:” mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f)
“storing information of the sub-resolution assist features” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)
“one or more processors,” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 2 recites “wherein the specific machine learning model is trained using training samples labeled based on sub-resolution assist features generated by an inverse lithography technology (ILT) tool” mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claim 3 recites “wherein the small area is a square with a side length smaller than 50 nm, and the layout area centered at each of the areas of interest has a dimension smaller than 1 micron” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Claim 4 recites “wherein the generating the sub-resolution assist features based on results of the determining comprises: combining areas of interest determined to be part of a sub-resolution assist feature to form intermediate sub-resolution assist features” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).; and processing the intermediate sub-resolution assist features to derive the sub-resolution assist features” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g). This limitation is directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Claim 5 recites “wherein the classifying layout features is a machine learning-based clustering process” mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claim 6 recites “wherein the machine learning-based clustering process comprises: extracting a feature vector for each of the layout features; and mapping the set of feature vectors into hyperboxes of a hyperspace” mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claim 7 recites “combining the sub-resolution assist features with modified layout features to generate a first processed layout design, the modified layout features being generated by performing a first optical proximity correction process on the layout features in the layout design; and performing a second optical proximity correction process on the first processed layout design to generate a second processed layout design which is to be used to manufacture photo masks” ” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Claim 8 recites “wherein the first optical proximity correction process is a machine learning-based main feature generation process which generates the modified layout features for layouts features in each of the groups of layout features using a particular machine learning model for the each of the groups of layout features” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Claim 9 recites “manufacturing photo masks based on the information of the sub-resolution assist features” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Claims 10-23 contains subject matter similar to claims 1-9 and are rejected under the same rationale.
Claim Rejections - 35 USC § 103
5. 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.
6. Claims 1-5, 9-14 and 18-21 are rejected under 35 U.S.C. 103 as being unpatentable over XU et al. (Sub-Resolution Assist Feature Generation with Supervised Data Learning, 2018) in view of Abouelseoud et al. (US 20250069086).
Claim 1. XU discloses a method, executed by at least one processor of a computer, comprising:
receiving a layout design (supervised SRAF generation using training layout clips containing target patterns and model-based SRAFs and subsequently applying the trained classifier to layout grids) (Section III.A, p. 1227);
performing a machine learning-based SRAF generation process to generate sub-resolution assist features for layout features in each of the groups of layout features (supervised data-learning-based SRAF generation. The process operates on a 2-D grid, extracts training samples, trains a classification model, predicts SRAF labels, and generates SRAFs from the predictions) (Section III, p. 3; Sections IV–V, pp. 1228–1229), each of the groups of layout features having a specific machine learning model for determining whether a small area should be part of a sub-resolution assist feature (classification at individual grid locations using a trained classification model, with the classification output being an SRAF label of 1 or 0) (Section III.A, p. 1227, “Definition 4 (SRAF label)”), size and shape of the small area being preset (introduce an SRAF box at each grid and states that the SRAF box is rectangular and that its size is a parameter) (Section III.A, p. 1227, “SRAF Label Extraction.”), the machine learning-based SRAF generation process comprising:
dividing regions where sub-resolution assist features are likely to be placed into areas of interest, each of the areas of interest having the same size and shape as the small area (impose a 2-D grid on target patterns and model-based SRAFs, with grid coordinates determined by a preset grid size. An SRAF box is introduced at each grid for determining the SRAF label) (Section III.A, p. 1227; Fig. 4(a)),
extracting a feature vector for each of the areas of interest based on a layout area centered at the each of the areas of interest (extracting a feature vector for every grid point and states that the feature vector represents the optical conditions of the grid point with respect to target patterns) (Section III.A, p. 1227; Section III.B, p. 1228)…(The feature extraction procedure uses a sampling region centered at the grid point and produces a feature matrix that is subsequently flattened into a feature vector) (Section III.B, p. 1228),
determining whether the each of the areas of interest should be part of a sub-resolution assist feature by using the feature vector as an input of the specific machine learning model (binary classification at each test grid and predicts whether an SRAF should be inserted at the grid)(Section IV, pp. 1227-1228; Algorithm 2)…( SRAF label is 1 when an SRAF is inserted and 0 otherwise, and the classification model predicts the SRAF label from the extracted feature vector) ( Section III.A, p. 1227; Section IV.B, p. 1228), and generating the sub-resolution assist features based on results of the determining (generating SRAFs from the predicted grid results. Algorithm 2 performs prediction at probability maxima, merges SRAF grids into a polygon set, performs spacing-rule checking and shrinking, and produces final rectangular SRAFs) (Section V, pp. 1229–1230, Algorithm 2); and
Xu does not explicitly disclose classifying layout features in the layout design into groups of layout features; storing information of the sub-resolution assist features.
However, Abouelseoud discloses classifying layout features in the layout design into groups of layout features (a pattern-classification unit that divides geometric layout elements into layout-element groups and geometric space elements into space-element groups according to layout patterns… pattern-classification unit 420) (claim 1)...( further explains that large layout designs are reduced into plural groups and that resolution enhancement processing can be performed on representatives of those groups) (col. 17, lines 19-25); storing information of the sub-resolution assist features (storing information of the modified layout design and generating mask data from the modified layout) (claim 1; operations 570–590). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Xu further in view of Abouelseoud to incorporate the above cited features. One would have been motivated to do so in order to reduce a large layout to groups of representative patterns to improve processing speed and consistency.
Claim 2. Xu and Abouelseoud disclose the method recited in claim 1, Xu further discloses wherein the specific machine learning model is trained using training samples labeled based on sub-resolution assist features generated by an inverse lithography technology (ILT) tool (teaches training samples containing target patterns and model-based SRAFs and defining the SRAF label from those model-based SRAFs.) (Introduction, Section VI, p. 1230).
Claim 3. Xu and Abouelseoud disclose the method recited in claim 1, Xu further discloses wherein the small area is a square with a side length smaller than 50 nm, and the layout area centered at each of the areas of interest has a dimension smaller than 1 micron (a preset SRAF box and provides an exemplary implementation using a 40-nm SRAF box and a 10-nm grid) (Section V, experimental setup, pp. 123-1231)..( a local sampling region centered at each grid point for feature extraction) (Section III.B, p. 1229.).
Claim 4. Xu and Abouelseoud disclose the method recited in claim 1, Xu further discloses wherein the generating the sub-resolution assist features based on results of the determining comprises: combining areas of interest determined to be part of a sub-resolution assist feature to form intermediate sub-resolution assist features (1. determining probability/SRAF-positive grids; merging SRAF grids into a polygon set; and processing the resulting polygons) ( Section V, p. 1229, Algorithm 2, steps 1–4.); and processing the intermediate sub-resolution assist features to derive the sub-resolution assist features ( perform spacing-rule checking, polygon shrinking, bounding-box processing, and rectangular SRAF generation)(Section V, pp. 1229–1230, Algorithm 2, steps 3–14.).
Claim 5. Xu and Abouelseoud disclose the method recited in claim 1, Abouelseoud further discloses wherein the classifying layout features is a machine learning-based clustering process (classification of layout and space elements into groups according to layout patterns) (claim 1; pattern-classification unit 420). One would have been motivated to do so in order to reduce a large layout to groups of representative patterns to improve processing speed and consistency.
Claim 9. Xu and Abouelseoud disclose the method recited in claim 1, Abouelseoud further discloses comprising: manufacturing photo masks based on the information of the sub-resolution assist features (processing the modified layout to generate mask data; providing the mask data to a mask-writing tool; creating photomasks; and using the photomasks for semiconductor fabrication) (claims 1–3; operations 580–590). One would have been motivated to do so in order to reduce a large layout to groups of representative patterns to improve processing speed and consistency.
Claims 10-14 and 18-21 represent the media and system of claims 1-5, respectively and are rejected along the same rationale.
7. Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over XU et al. (Sub-Resolution Assist Feature Generation with Supervised Data Learning, 2018) in view of Abouelseoud et al. (US 20250069086) and further in view of MA et al. (WO 2022/010468).
Claim 6. Xu and Abouelseoud disclose the method recited in claim 5, Xu further discloses wherein the machine learning-based clustering process comprises: extracting a feature vector for each of the layout features (feature vectors extracted for each SRAF grid)( Section III, pp. 1227); and fail to explicitly disclose mapping the set of feature vectors into hyperboxes of a hyperspace.
However, MA discloses mapping the set of feature vectors into hyperboxes of a hyperspace (accessing a feature-vector set; transforming the feature space; quantizing the transformed feature space into a hyperspace comprising hyperboxes; and processing data according to mapping of feature vectors into the hyperboxes) (¶¶[0012], [0031]–[0034], [0048]–[0050], [0060]–[0061], [0068]; claim 1). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Xu further in view of MA to incorporate the above cited features. One would have been motivated to do so in order to organize, cluster, or efficiently process the feature vectors.
Claims 15 represents the media of claim 6 and is rejected along the same rationale.
8. Claims 7-8, 16-17 and 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over XU et al. (Sub-Resolution Assist Feature Generation with Supervised Data Learning, 2018) in view of Abouelseoud et al. (US 20250069086) and further in view of Huang et al. (US 2015/0072272).
Claim 7. Xu and Abouelseoud disclose the method recited in claim 1, Abouelseoud further comprising: combining the sub-resolution assist features with modified layout features to generate a first processed layout design, the modified layout features being generated by performing a first optical proximity correction process on the layout features in the layout design (perform OPC on grouped geometric layout elements and extracts modified geometric layout elements from the OPC results) (claim 1; OPC-SRAF unit 430; modified-layout generation unit 440)…( generate a modified layout by replacing the original geometric layout elements with corresponding modified elements and inserting corresponding SRAFs) ( claim 1; modified-layout generation unit 440). One would have been motivated to do so in order to reduce a large layout to groups of representative patterns to improve processing speed and consistency.; and
However, Huang discloses performing a second optical proximity correction process on the first processed layout design to generate a second processed layout design which is to be used to manufacture photo masks (second OPC operation after incorporation of SRAF/layout modifications was also a known mask-preparation technique) (claims 1–2, which expressly recites first and second OPC processes with the second OPC performed after the first). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Xu further in view of Huang to incorporate the above cited features. One would have been motivated to do so in order to facilitate forming the masks of the metal interconnection system.
Claim 8. Xu Abouelseoud and Huang disclose the method recited in claim 7, Abouelseoud further wherein the first optical proximity correction process is a machine learning-based main feature generation process which generates the modified layout features for layouts features in each of the groups of layout features using a particular machine learning model for the each of the groups of layout features (grouping layout elements and applying OPC/SRAF processing to members of the respective groups) (claim 1; pattern classification unit 420 and OPC-SRAF unit 43). One would have been motivated to do so in order to reduce a large layout to groups of representative patterns to improve processing speed and consistency.
Claims 16-17 and 22-23 represent the media and system of claims 7-8, respectively and are rejected along the same rationale.
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (See PTO-892).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Phenuel S. Salomon whose telephone number is (571) 270-1699. The examiner can normally be reached on Mon-Fri 7:00 A.M. to 4:00 P.M. (Alternate Friday Off) EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Usmaan Saeed can be reached on (571) 272-4046. The fax phone number for the organization where this application or proceeding is assigned is 571-273-3800.
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/PHENUEL S SALOMON/Primary Examiner, Art Unit 2146
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