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 .
OFFICE ACTION
This is a response to the application filed on 12/14/2023.
Claims 1-3, 14-15 and 19-33 are pending.
Claim Rejections - 35 USC § 102
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 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.
(a)(2) The claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 and 19 are rejected under 35 U.S.C. 102(a) (1) being anticipated by the prior art of record WO 2017/194281 A1 (ASML NETHERLANDS)
16 November 2017
Regarding claim 1 the prior art discloses:
A method comprising:
obtaining, using a first machine learning model, a first predicted pattern representation associated with a target pattern to be printed on a substrate (par. 35-54, fig. 3,4);
obtaining cluster error data from the first predicted pattern representation, wherein the cluster error data is indicative of a first plurality of error clusters, the first plurality of error clusters including a first error cluster that is indicative of a collection of errors in a specified region in the first predicted pattern representation (par. 55,56,61-63, fig. 5,8A,8C); and
training, based on location information of the first plurality of error clusters, the first machine learning model to generate an adjusted predicted pattern representation (par. 64,65, fig. 8).
Claim 19 recite similar subject matter and rejected for the same reason.
Claims 1-2, 14-15, 19-20, 22-25, 27 and 30-33 are rejected under 35 U.S.C. 102(a) (1) being anticipated by the prior art of record Chu (US 2020/03800655).
Regarding claim 1 and similarly recited claim 19, the prior art discloses:
A method comprising:
obtaining, using a first machine learning model (see one or more of title, abstract, background summary), a first predicted pattern representation associated with a target pattern to be printed on a substrate (see one or more of background, summary, fig 10-22);
obtaining cluster error data (see one or more of abstract, background, summary or one more of fig 1-6, 8-21 about cluster error/ noise/ defect/ fault/ bad/ failure/ abnormal) from the first predicted pattern representation, wherein the cluster error data is indicative of a first plurality of error clusters data (see one or more of abstract, background, summary or one more of fig 1-6, 8-21 about cluster error/ noise/ defect/ fault/ bad/ failure/ abnormal), the first plurality of error clusters including a first error cluster that is indicative of a collection of errors in a specified region in the first predicted pattern representation data (see one or more of abstract, background, summary or one more of fig 1-6, 8-21 about cluster error/ noise/ defect/ fault/ bad/ failure/ abnormal); and
training, based on location information of the first plurality of error clusters, the first machine learning model to generate an adjusted predicted pattern representation (i.e., for generating or updating the clustering model, the data processing device 100 removes noises of the original data 30 and performs unsupervised learning for generating a clustering model 40 using the original data from which the noise has been removed (par 62),
learning for generating a defective pattern clustering model may be performed using data for each wafer after the first noise removal and the second noise removal are completed. Since the defect pattern clustering model is generated…(par 100),
self-organizing map algorithm, learning is performed in such a way that each node of a competition layer is …e a clustering model must be generated to achieve…(par 132))
(Claims 2, 20, 23, 33) wherein obtaining the cluster error data includes obtaining a prediction error map (see one or more of par 27, 66, 75, 91-93, 131-133, 156 and/or fig 2-14, 18-22 and related text) from the first predicted pattern representation, the prediction error map indicative of a plurality of errors in the first predicted pattern representation compared to a reference pattern representation (see one or more of par 27, 66, 75, 91-93, 131-133, 156 and/or fig 2-14, 18-22 and related text).
(Claims 14, 30, 32) obtaining a first evaluation result associated with the first predicted pattern, the first evaluation result including a first set of scores determined based on the first plurality of error clusters (fig 2, 8, 12, 16 and related text discloses iteration/ loop process for first/ second update/ filter/ optimizing, first/second learning/ first/second training, first/second execution by first/second neural network /train/learn for first/ second evaluations/ results/ predictions / representations / targets / patterns / models including a first set of scores based on error clusters. For scores, see par 11-14, 79-82, 112, 136-138, 178-181);
obtaining, using a second machine learning model, a second predicted pattern representation associated with the target pattern (fig 2, 8, 12, 16 and related text discloses iteration/ loop process for first/ second update/ filter/ optimizing, first/second learning/ first/second training, first/second execution by first/second neural network /train/learn for first/ second evaluations/ results/ predictions / representations / targets / patterns / models including a first set of scores based on error clusters. For scores, see par 11-14, 79-82, 112, 136-138, 178-181);
obtaining a second evaluation result associated with the second predicted pattern representation, the second evaluation result including a second set of scores determined based on a second plurality of error clusters associated with the second predicted pattern representation (fig 2, 8, 12, 16 and related text discloses iteration/ loop process for first/ second update/ filter/ optimizing, first/second learning/ first/second training, first/second execution by first/second neural network /train/learn for first/ second evaluations/ results/ predictions / representations / targets / patterns / models including a first set of scores based on error clusters. For scores, see par 11-14, 79-82, 112, 136-138, 178-181); and
evaluating the first machine learning model and the second machine learning model based on the first evaluation result and the second evaluation result(fig 2, 8, 12, 16 and related text discloses iteration/ loop process for first/ second update/ filter/ optimizing, first/second learning/ first/second training, first/second execution by first/second neural network /train/learn for first/ second evaluations/ results/ predictions / representations / targets / patterns / models including a first set of scores based on error clusters. For scores, see par 11-14, 79-82, 112, 136-138, 178-181);
(Claims 15, 22, 31) generating a mask pattern based on the adjusted predicted pattern representation (this is inherent for fabrication/manufacturing/production line).
(Claim 24) transformation on the prediction error map to derive the first plurality of error clusters (see one or more of par 20, 27, 66, 85, fig 2-3, 8, 16-19 and related text)
(Claim 25) evaluate the first plurality of error clusters to generate an evaluation result indicating a degree of error (see one or more of par 11, 79-82, 91, 91, 114, 124, 126, 142-145, fig 2, 13, 21-22 ) caused in printing of the target pattern on the substrate using the first predicted pattern representation.
(Claim 27) determine the evaluation result of the error cluster further based on a distance between the error cluster in the first predicted pattern representation and patterns corresponding to target features of the target pattern (see one or more of fig 4, 6, 9-15, 21).
Claims 1-3, 19-21, 23, 26, 29. 33 are rejected under 35 U.S.C. 102(a) (1)
being anticipated by the prior art of record Tarshish-Shapir (US 2014/0136137)
Regarding claim 1 and similarly recited claim 19, the prior art discloses:
A method comprising:
obtaining, using a first machine learning model (par 75, 77, 86, 88, 92), a first predicted pattern representation associated with a target pattern to be printed on a substrate (abstract, summary par 30-33);
obtaining cluster error data from the first predicted pattern representation, wherein the cluster error data is indicative of a first plurality of error clusters (clustering to indicate sources/productions errors (par 43, 73, 83, 92)), the first plurality of error clusters including a first error cluster that is indicative of a collection of errors in a specified region in the first predicted pattern representation (par 43, 73, 83, 92); and
training (par 31, 75, 77, 86, 88, 92), based on location information of the first plurality of error clusters, the first machine learning model to generate an adjusted (par 58-60, 77, 84-88) predicted pattern representation.
Claims (2, 20, 23, 33) wherein obtaining the cluster error data includes obtaining a prediction error map (see one or more of fig 1-4 and related text) from the first predicted pattern representation, the prediction error map indicative of a plurality of errors in the first predicted pattern representation compared to a reference pattern representation (see one or more of fig 1-4 and related text)
(Claims 3, 21, 26, 29) prediction error map comprises a difference between each pixel (par 68, 75) in the first predicted pattern representation and a corresponding pixel (par 68, 75) in the reference pattern representation
Allowable Subject Matter
Claim 28 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Correspondence Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL DINH whose telephone number is 571-272-1890. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s Supervisor, Jack Chiang can be reached on 571-272-7483. The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PAUL DINH/ Primary Examiner, Art Unit 2851
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