DETAILED ACTION
This office action is in response to Application No. 18/686,994, filed on 27 February 2024. Claims 1-11 and 13-21 are pending.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 13 is objected to because of the following informalities: the claim depends from claim 12, which is cancelled. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11 and 13-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract mental processes without significantly more. The claim(s) recite(s) a method of selecting a subset of representative portions from a set of representative portions according to criteria, which is an abstract process that could be performed by a person in the mind or with pen and paper.
This judicial exception is not integrated into a practical application because aside from the abstract idea itself, the claims recite merely generic computer implementation, which does not qualify as integration into a practical application. Similarly, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because generic computer implementation does not qualify as ‘significantly more’.
Claims 2, 3, 5-9, 11, 13, 18, recite characteristics of the portions/patterns, criteria, and selection, which do not change the abstract nature of the process. Claims 4 and 17 recite abstract mathematical algorithms. Claims 10, 14, and 21 recite additional steps of determining the set of representative portions of the layout and grouping repeating patterns, which are also abstract mental steps that could be performed by a person. Claims 15, 16, 20 recites providing the subset of representative portions as training data for a model for optical proximity correction or source mask optimization, which is merely insignificant post-solution activity and/or intended use. Claim 19 is analogous to claim 1 and rejected under the same reasoning.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-11, 18, and 19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Baidya (US 2019/0325246).
Regarding claims 1 and 19, Baidya discloses a non-transitory computer readable medium having instructions thereon or therein, the instructions, when executed by a computer system, configured to cause the computer system to at least (Fig. 9; ¶80): receive a set of representative portions of a pattern layout, wherein individual representative portions comprise one or more unique patterns of the pattern layout (Fig. 6; ¶¶43, 46, 47); and select a subset of the representative portions from the set of representative portions according to a prescribed criterion for unique patterns included in the subset of representative portions in combination (¶¶51-54).
Regarding claim 2, Baidya discloses that the subset of the representative portions is selected such that a number of the representative portions in the subset meet a first criterion, and a number of unique patterns encompassed in the subset meet a second criterion (¶¶53-55).
Regarding claim 3, Baidya discloses that the first criterion corresponds to a prescribed number of representative portions in the subset, and wherein the second criterion corresponds to including at least a threshold number of unique patterns in the prescribed number of representative portions in combination (¶¶53-55).
Regarding claim 4, Baidya discloses that the instructions configured to cause the computer system to select the subset of the representative portions are further configured to cause the computer system to use a set cover solver algorithm, and wherein the unique patterns in the set of representative portions of the pattern layout are configured as elements in a universe defined in the set cover solver algorithm (¶33).
Regarding claim 5, Baidya discloses that the prescribed criterion comprises inclusion of at least a threshold number of unique patterns from the set of representative portions of the pattern layout in the selected subset of the representative portions (¶¶54-55).
Regarding claim 6, Baidya discloses that the prescribed criterion is set such that the unique patterns included in the selected subset of representative portions in combination represent an entirety of the pattern layout or a portion of the pattern layout (¶¶54-55).
Regarding claim 7, Baidya discloses that the prescribed criterion comprises inclusion of an optimally diverse group of unique patterns in a predetermined number of representative portions that form the selected subset of representative portions (¶¶33, 47, 53).
Regarding claim 8, Baidya discloses that the optimally diverse group of unique patterns comprises a plurality of unique patterns having geometries that, in combination, represent at least a threshold amount of the pattern layout, given the predetermined number of representative portions that form the selected subset (¶¶47, 54-55).
Regarding claim 9, Baidya discloses that each unique pattern represents a group of identical or similar patterns across the pattern layout (¶47).
Regarding claim 10, Baidya discloses that the instructions are further configured to cause the computer system to: receive an original representation of the pattern layout; and determine the set of representative portions of the pattern layout such that the individual representative portions comprise different combinations of the one or more unique patterns of the pattern layout, and at least one of the unique patterns is included in more than one representative portion (¶¶39, 46, 47, 57, 58).
Regarding claim 11, Baidya discloses that the instructions configured to cause the computer system to select the subset of the representative portions are further configured to cause the computer system to select the subset of the representative portions based on a polygon representation of the pattern layout, or based on image or contour representations of patterns in the pattern layout (¶47).
Regarding claim 18, Baidya discloses that the subset of the representative portions is configured to include an optimally diverse group of unique patterns in a predetermined number of representative portions that form the selected subset of representative portions, wherein the subset of the representative portions are ranked based on the one or more unique patterns they include, and wherein the subset of the representative portions is determined based on rank, and wherein the subset of the representative portions are ranked based on a quantity and/or a rarity of the one or more unique patterns each representative portion includes (¶¶53, 57, 64).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 13-16, 20, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baidya in view of Chen (WO 2020/156777).
Regarding claim 13, Baidya does not appear to explicitly disclose that the instructions configured to cause the computer system to select the subset of the representative portions are further configured to cause the computer system to select the subset of the representative portions based on image or contour representations of patterns in the pattern layout and wherein the image or contour representations of patterns in the pattern layout comprise aerial images and/or mask images that result from simulation, inspection, or metrology; Chen discloses these limitations (¶¶70, 95). It would have been obvious to persons having ordinary skill in the art before the effective filing date of the application to combine the teachings of Baidya and Chen, because doing so would have involved merely the routine combination of known elements according to known techniques, or the routine use of a known technique to improve similar devices in the same way, to produce merely the predictable results of accounting for mask lithography in pattern selection. KSR Int’l Co. v. Teleflex Inc., 82 U.S.P.Q.2d 1385, 1395-1396. Baidya discloses selection patterns to provide sampling data for mask and lithographic process development. Chen teaches that such patterns should be selected in consideration of mask and lithography features. The teachings of Chen are directly applicable to Baidya in the same way, so that Baidya would similarly account for mask and lithography features when selecting patterns for mask and lithographic process development.
Regarding claim 14, Baidya discloses that the instructions are further configured to cause the computer system to group patterns that repeat across the pattern layout to determine the unique patterns, and determining the set of the representative portions based on grouped patterns (¶¶38, 47). If Baidya is found to be unclear regarding these limitations, Chen discloses the same (¶¶100, 106). It would have been obvious to persons having ordinary skill in the art before the effective filing date of the application to combine the teachings of Baidya and Chen, because doing so would have involved merely the routine combination of known elements according to known techniques, or the routine use of a known technique to improve similar devices in the same way, to produce merely the predictable results of determining which patterns are representative of unique patterns. KSR Int’l Co. v. Teleflex Inc., 82 U.S.P.Q.2d 1385, 1395-1396. Baidya discloses selecting a subset of representative patterns that are determined based on expected diversity, the subset being used for process development. Chen teaches that such representative patterns should be determined based on the unique patterns in the design. The teachings of Chen are directly applicable to Baidya in the same way, so that Baidya would similarly determine representative patterns based on the unique patterns in the design, so that the patterns used for process development would include patterns that are representative of unique patterns.
Regarding claim 15, Baidya does not appear to explicitly disclose that the instructions are further configured to cause the computer system to provide the subset of representative portions as training data for training a machine learning model; Chen discloses these limitations (¶¶9, 92-93). It would have been obvious to persons having ordinary skill in the art before the effective filing date of the application to combine the teachings of Baidya and Chen, because doing so would have involved merely the routine combination of known elements according to known techniques, or the routine use of a known technique to improve similar devices in the same way, to produce merely the predictable results of improving mask/process development with machine learning. KSR Int’l Co. v. Teleflex Inc., 82 U.S.P.Q.2d 1385, 1395-1396. Baidya discloses selecting patterns to use as data for mask/process development, e.g. optical proximity correction (OPC). Persons having ordinary skill in the art would recognize that machine learnings models for mask/process development, and patterns used as training data for such models, are conventional, as taught by Chen. The teachings of Chen are directly applicable to Baidya in the same way, so that Baidya would similarly use machine learning models to improve mask/process development.
Regarding claim 16, Baidya discloses that the machine learning model is associated with optical proximity correction (OPC) and/or source mask optimization (SMO) for a semiconductor lithography process (¶¶25, 29). Chen also discloses the same (¶¶9, 94). Motivation to combine remains consistent with claim 15.
Regarding claim 20, Baidya does not appear to explicitly disclose training a machine learning model using the subset of representative portions as training data; Chen discloses these limitations (¶¶9, 92-93). Motivation to combine remains consistent with claim 15.
Regarding claim 21, Baidya discloses a method comprising: receiving a complete representation of a design layout for forming a pattern on a semiconductor wafer (¶39); determining a set of representative clips of the design layout such that individual representative clips comprise different combinations of one or more geometrically unique patterns of the design layout, and at least one of the geometrically unique patterns is included in more than one representative clip (¶¶46, 47, 57, 58), wherein determining the set of representative clips comprises grouping the one or more geometrically unique patterns into groups of similar patterns (¶47); selecting a subset of the representative clips based on the one or more geometrically unique patterns, the subset of the representative clips configured to include: each geometrically unique pattern in a minimum number of representative clips; or as many geometrically unique patterns of the design layout as possible in a maximum number of representative clips (¶¶33, 53, 64). Baidya does not appear to explicitly disclose providing the subset of representative portions as training data for training a machine learning model; Chen discloses these limitations (¶¶9, 92-93). Furthermore, if Baidya is found to be unclear regarding determining the set of representative clips comprising grouping the one or more geometrically unique patterns into groups of similar patterns, Chen also discloses the same (¶¶100, 106). Motivation to combine remains consistent with claim 14 and 15.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baidya in view of Wikipedia (“Set Cover Problem” and “Integer programming”).
Regarding claim 17, Baidya discloses that the subset of the representative portions is optimized to include a maximum amount of unique geometry from the pattern layout (¶¶33, 53, 64), but does not appear to explicitly disclose that the instructions configured to cause the computer system to select the subset of the representative portions are further configured to cause the computer system to select the subset of the representative portions using a discrete optimizer, and wherein the discrete optimizer comprises an integer linear programming solver. However, Baidya discloses that the selection subset is a set cover problem, which is notoriously well-known to be solved using discrete optimization methods such as integer linear programming (ILP), as taught by Wikipedia (‘Integer linear program formulation’). It would have been obvious to persons having ordinary skill in the art before the effective filing date of the application to combine the teachings of Baidya and Wikipedia, because doing so would have involved merely the routine combination of known elements according to known techniques to produce merely the predictable results of solving set cover problems through canonical methods. KSR Int’l Co. v. Teleflex Inc., 82 U.S.P.Q.2d 1385, 1395. Baidya discloses selecting a subset of representative patterns as a set cover problem. Persons having ordinary skill in the art would recognize that set cover problems are solved by discrete optimization methods such as ILP, as stated by Wikipedia. The teachings of Wikipedia are directly applicable to Baidya in the same way, so that Baidya would use canonical techniques to solve the set cover problem of selecting a subset of representative patterns.
Conclusion
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13 September 2026
/ARIC LIN/ Examiner, Art Unit 2851
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