DETAILED ACTION
This Office action is in response to application filed on 02/27/2023.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information Disclosure Statements (IDS) filed 02/27/2023 and 4/30/2025 have been considered.
Response to Amendment
Preliminary amendment filed on 02/27/2023 to the specification and claims are accepted.
Claims 1, 3-5, 7-12, and 14-15 have been amended.
Claims 16-20 have been added.
Claims 1-20 have been examined.
Claim Objections
Claims 19 is objected to because of the following informalities:
Claim 19 should read “The non-transitory computer readable storage medium …”
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 1-20 are rejected under 35 U.S.C. 101 as the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon,
or an abstract idea) without significantly more.
Under Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the
claims are directed to process and apparatus (claims 1, 4, and 13-14), which are statutory categories.
However, evaluating claims 1, 4, and 13-14 under at Step 2A, Prong One, the claims are directed to the judicial exception of an Abstract idea using the grouping of math concepts including “processing the measurement data to obtain metrology information about the target portion of the layer” (Claim 1); “deriving/obtain metrology information …” (Claims 4 and 13-14).
Next, Step 2A, Prong Two evaluates whether additional elements of the claims
"integrate the abstract idea into a practical application" in a manner that imposes a
meaningful limit on the judicial exception, such that the claims are more than a drafting effort designed to monopolize the exception. The additional limitations of “illuminating a target portion … and detecting …to obtain measurement data” (Claims 1 and 13); “obtaining a training dataset…, and using the obtained training dataset to train the machine leaning model …” (Claim 4); “obtain the measurement data…, and use a machine learning model to obtain metrology information … (Claim 13); and “perform a first/second measurement process…, use a training dataset … to train… (Claim 14), are merely data gathering that obtained from generic measurement system and using such generic measurement data to train, which are a form of insignificant extra-solution activities and/or mere computer implementation; and a data processing system is a generic computer component recited at a high level of generality. These additional limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to the abstract idea. Thus, claims 1, 4 and 13-14 are not patent eligible.
At Step 2B, consideration is given to additional elements that may make the
abstract idea significantly more. Under Step 2B, there are no additional elements that
make the claim significantly more than the abstract idea.
The additional limitations as recited above in step 2A Prong Two, are considered insignificant extra-solution activities or mere computer instructions to implement the judicial exception on a generic computer, which are not sufficient to integrate the claims into a practical application. The above additional limitations have been considered individually and as a whole and do not amount to significantly more than the abstract idea itself.
Dependent claims 2-3, 5-12, and 15-20 do not disclose limitations considered to be significantly more which would render the claimed invention a patent eligible application of the abstract idea. The claims mere extends (or narrow) the abstract idea which do not amount for “significant more” because it merely adds details to the algorithm which forms the abstract idea as discussed above.
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
Claims 1, 12, and 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated over US 2017/0059999 of Van Der Schaar et al., hereinafter Schaar.
As per Claim 1, Schaar teaches a method, comprising: a method of performing metrology, the method comprising:
illuminating a target portion of a layer formed on a substrate with a beam of radiation and detecting a distribution of radiation in a pupil plane, the radiation redirected by the target portion of the layer, to obtain measurement data ([0053]-[0056], [0058], [0067], [0107]), the layer comprising a two-dimensional material (two-dimensional periodic structure formed in a single material layer considered “two-dimensional material” [0012]); and
processing the measurement data to obtain metrology information about the target portion of the layer, wherein the illuminating, detecting and processing are performed for plural different target portions of the layer to obtain metrology information for the plural target portions of the layer (acquire different targets [0054], [0063]-[0064]).
As per Claim 12, Schaar teaches a method for providing a layer comprising a two-dimensional material on a substrate (two-dimensional periodic structure formed in a single material layer considered “two-dimensional material” [0012]), the method comprising: forming a layer comprising a two-dimensional material on a substrate using a formation process (see [0063], [0083]); performing metrology on the layer comprising the two-dimensional material using the method of any of claim 1 (see [0053]-[0054], [0130]); and modifying one or more process parameters of the formation process based on the obtained metrology information and repeating the formation process to form a layer comprising a two-dimensional material on a new substrate (measuring a parameter [0021], two-dimensional periodic arrangement considered “single material layer” [0133], iterative reconstruction process [0060]).
As per Claim 18, Schaar teaches a non-transitory computer-readable storage medium containing computer instructions, wherein the computer instructions, when executed by a computer system, are configured to cause the computer system to execute at least the method of claim 1 (see [0147]-[0148]).
Claim Rejections - 35 USC § 103
The following is a quotation under AIA of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action.
A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 2-5, 8, 13-14, 16, and 19-20 are rejected under 35 U.S.C. 103 as being obvious over Schaar in view of US 2021/0142466 of Ophir et al., hereinafter Ophir.
As per Claim 2, Schaar teaches the method of claim 1, wherein the processing of the measurement data to obtain the metrology information from the detected distribution of radiation in the pupil plane as stated in claim 1, but Schaar does not teach using machine learning model to obtain the metrology information. Ophir teaches using machine learning model to obtain the metrology information. (Fig 1 shows, i.e., data received 96 and training data 105, [0005], [0017]-[0018]). It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the present claimed invention, to modify the teaching of Schaar using machine learning model to train as taught by Ophir that would facilitate using the estimation model to provide estimations of the at least one metrology metric with respect to the measurement data and optionally compensating for process errors using the derived estimation model(s) (Ophir [0023]).
As per Claim 3, Schaar in view of Ophir teaches the method of claim 2, Schaar teaches obtaining data by performing a first measurement process on a target portion of a layer formed on a substrate for multiple different target portions of the layer, the layer comprising a two-dimensional material; and using the obtained data comprising a two-dimensional material from measurement data ([0012], [0054]). Ophir further teaches wherein a training method of the machine learning model comprises: obtaining a training dataset by performing a first measurement process (Fig 1: obtain training data 105, process images 95 at 0° considered “first measurement” [0003]); and using the obtained training dataset to train the machine learning model such that the trained machine learning model is capable of deriving metrology information about a new target portion of a layer comprising measurement data obtained by performing the first measurement process on the new target portion (deep learning 150 derived data 96 and process images 95, two images per target “0° and 180°, at 0° “first measurement” and rotates at 180° considered “a new target portion” [0003], [0017]-[0018], [0029]). It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the present claimed invention, to modify the teaching of Schaar using machine learning model to train as taught by Ophir that would facilitate using the estimation model to provide estimations of the at least one metrology metric with respect to the measurement data and optionally compensating for process errors using the derived estimation model(s) (Ophir [0023]).
As per Claim 4, Schaar teaches the method of training a machine learning model, the method comprising: obtaining data by performing a first measurement process on a target portion of a layer formed on a substrate for multiple different target portions of the layer, the layer comprising a two-dimensional material; and using the obtained data comprising a two-dimensional material from measurement data obtained ([0012], [0054]). Ophir further teaches wherein a training method of the machine learning model comprises: obtaining a training dataset by performing a first measurement process (Fig 1: obtain training data 105, process images 95 at 0° considered “first measurement” [0003]); and using the obtained training dataset to train the machine learning model such that the trained machine learning model is capable of deriving metrology information about a new target portion of a layer comprising measurement data obtained by performing the first measurement process on the new target portion (deep learning 150 derived data 96 and process images 95, two images per target “0° and 180°, at 0° “first measurement” and rotates at 180° considered “a new target portion” [0003], [0017]-[0018], [0029]). It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the present claimed invention, to modify the teaching of Schaar using machine learning model to train as taught by Ophir that would facilitate using the estimation model to provide estimations of the at least one metrology metric with respect to the measurement data and optionally compensating for process errors using the derived estimation model(s) (Ophir [0023]).
As per Claim 5, Schaar in view of Ophir teaches the method of claim 4, Ophir teaches wherein the obtaining of the training dataset further comprises performing a second measurement process on each of the target portions (images 95 rotates at 180° considered a “second measurement”. Fig 1 shows a plurality of images 95. It is noted images of targets, each image is performed at 0° and 180° [0018]). It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the present claimed invention, to modify the teaching of Schaar using machine learning model to train a second measurement as taught by Ophir that would facilitate using the estimation model to provide estimations of the metrology metrics with respect to the measurement data and optionally compensating for process errors using the derived estimation model(s) (Ophir [0023]).
As per Claim 8, Schaar in view of Ophir teaches the method of claim 4, Schaar teaches wherein the first measurement process comprises obtaining a detected distribution of radiation in a pupil plane, see [0056], [0058].
As per Claim 13, Schaar teaches a metrology apparatus configured to perform metrology on a substrate, the apparatus comprising:
a measurement system (Fig 2) configured to illuminate a target portion of a layer of two-dimensional material on a substrate and to detect a distribution of radiation in a pupil plane, the radiation redirected by the target portion, to obtain measurement data ([0053]-[0056], [0058], [0067], [0107], two-dimensional periodic structure formed in a single material layer considered “two-dimensional material” [0012]); and a data processing system configured to:
control the measurement system to obtain the measurement data for plural different target portions acquire different targets [0054], [0063]-[0064]); and obtain metrology information for the target portions from the respective detected distributions of radiation in the pupil plane ([0038], [0056]).
Schaar does not teach use a machine learning model to obtain metrology information. Ophir teaches use a machine learning model to obtain metrology information (Fig 1 shows, i.e., data received 96 and training data 105, [0005], [0017]-[0018]). It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the present claimed invention, to modify the teaching of Schaar using machine learning model to train as taught by Ophir that would facilitate using the estimation model to provide estimations of the at least one metrology metric with respect to the measurement data and optionally compensating for process errors using the derived estimation model(s) (Ophir [0023]).
As per Claim 14, Schaar teaches a metrology apparatus configured to train a machine learning model, the apparatus comprising:
a measurement system configured to perform a first measurement process and a second measurement process on a target portion of a layer of two-dimensional material for multiple different target portions of the layer (Fig 3 shows a first measurement target 31 and second measurement target 32 considered metrology measurements [0063],[0061]-[0062]). Schaar does not teach a data processing system configured to use a training dataset derived from the first measurement process to train a machine learning model such that the machine learning model is capable of deriving metrology information about a new target portion of a layer comprising a two-dimensional material from measurement data obtained by performing the first measurement process on the new target portion.
Ophir teaches a data processing system configured to use a training dataset derived from the first measurement process to train a machine learning model such that the machine learning model is capable of deriving metrology information about a new target portion of a layer comprising a two-dimensional material from measurement data obtained by performing the first measurement process on the new target portion (Fig 1: deep learning 150 derived data 96 and process images 95, two images per target “0° and 180°, at 0° “first measurement” and rotates at 180° considered “a second measurement” [0003], [0017]-[0018], [0029]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the present claimed invention, to modify the teaching of Schaar using machine learning model to train as taught by Ophir that would facilitate using the estimation model to provide estimations of the at least one metrology metric with respect to the measurement data and optionally compensating for process errors using the derived estimation model(s) (Ophir [0023]).
As per Claim 16, Schaar in view of Ophir teaches the method of claim 3, Ophir teaches wherein the obtaining of the training dataset further comprises performing a second measurement process on each of the target portions (images 95 rotates at 180° considered a “second measurement”. Fig 1 shows a plurality of images 95. It is noted images of targets, each image is performed at 0° and 180° [0018]). It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the present claimed invention, to modify the teaching of Schaar using machine learning model to train a second measurement as taught by Ophir that would facilitate using the estimation model to provide estimations of the metrology metrics with respect to the measurement data and optionally compensating for process errors using the derived estimation model(s) (Ophir [0023]).
As per Claim 19, Schaar in view of Ophir teaches the computer-readable storage medium of claim 18, Schaar teaches wherein the processing of the measurement data to obtain the metrology information from the detected distribution of radiation in the pupil plane ([0038], [0056]). Schaar does not teach uses a machine learning model to obtain the metrology information. Ophir teaches using a machine learning model to obtain metrology information (Fig 1 shows, i.e., data received 96 and training data 105, [0005], [0017]-[0018]). It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the present claimed invention, to modify the teaching of Schaar using machine learning model to train as taught by Ophir that would facilitate using the estimation model to provide estimations of the at least one metrology metric with respect to the measurement data and optionally compensating for process errors using the derived estimation model(s) (Ophir [0023]).
As per Claim 20, Schaar in view of Ophir teaches a non-transitory computer-readable storage medium containing computer instructions, Schaar teaches wherein the computer instructions, when executed by a computer system, are configured to cause the computer system to execute at least the method of claim 4 (see [0147]-[0148]).
Claims 6 and 17 are rejected under 35 U.S.C. 103 as being obvious over Schaar in view of Ophir and further US 2007/0279611 of Baselmans et al., hereinafter Baselmans.
As per Claim 6, Schaar in view of Ophir teaches the method of claim 5, Schaar teaches wherein either or both of the first measurement process and the second measurement process comprise illuminating each target portion of the layer with an beam of radiation and detecting radiation redirected by the target portion (Fig 3 shows a first measurement targets 31 and 32 considered metrology measurements [0063], [0061]-[0062]). Schaar and Ophir do not teach Baselmans teaches Fig 19, [0015], [0143]-[0144]). It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the present claimed invention, to modify the teachings of Schaar and Ophir using incoherent beam as taught be Baselmans that would facilitate projecting on a target portion of a substrate (Baselmans, [0144]).
As per Claim 17, Schaar in view of Ophir teaches the method of claim 16, Schaar teaches wherein either or both of the first measurement process and the second measurement process comprise illuminating each target portion of the layer with an beam of radiation and detecting radiation redirected by the target portion (Fig 3 shows a first measurement targets 31 and 32 considered metrology measurements [0063],[0061]-[0062]). Schaar does not teach incoherent beam of radiation. Baselmans teaches Fig 19, [0015], [0143]-[0144]). It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the present claimed invention, to modify the teachings of Schaar and Ophir using incoherent beam as taught be Baselmans that would facilitate projecting on a target portion of a substrate (Baselmans, [0144]).
Claim 7 is rejected under 35 U.S.C. 103 as being obvious over Schaar in view of Ophir, Baselmans and further US 2008/0165343 of Lewis et al., hereinafter Lewis.
As per Claim 7, Schaar in view of Ophia and Baselmans teaches the method of claim 6, but the combination does not explicitly teach wherein: the first measurement process comprises detecting an image in a bright field imaging mode; and the second measurement process comprises detecting an image in a dark field imaging mode. Lewis teaches the first measurement process comprises detecting an image in a bright field imaging mode; and the second measurement process comprises detecting an image in a dark field imaging mode ([0016], [0040]). It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the present claimed invention, to modify the teachings of Schaar, Ophir, and Baselmans detecting bright field and dark field imaging as taught by Lewis that would have desirable to provide a bright field system where such deficiencies are not present and dark field system with
adequate sensitivity but improved throughput (Lewis, [0005], [0008]).
Claims 9-10 are rejected under 35 U.S.C. 103 as being obvious over Schaar in view of Ophir and further WO 2018/153711 of Tel et al., hereinafter Tel.
As per Claim 9, Schaar in view of Ophir teaches the method of claim 4, Schaar teaches wherein the layer comprising the two-dimensional material used to obtain the data is supported on a non-planar support surface, a surface topography of the non-planar support surface (asymmetry “non-planar” [0023], non-linear considered “non-planar” [0090], [0112], [0171], [0180]), Ophir teaches obtaining the training dataset (Fig 1). It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the present claimed invention, to modify the teaching of Schaar using machine learning model to train as taught by Ophir that would facilitate using the estimation model to provide estimations of the at least one metrology metric with respect to the measurement data and optionally compensating for process errors using the derived estimation model(s) (Ophir [0023]).
Schaar and Ophir do not explicitly teach providing a predetermined defect distribution in the layer. Tel teaches providing a predetermined defect distribution in the layer (a pattern on the substrate exceeds a certain threshold, one or more defects may likely be produced, page 37 lines 16-20). It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the present claimed invention, to modify the teachings of Schaar and Ophir to provide a threshold of defect as taught by Tel that would predict before etching process takes place based the pattern on the substrate.
As per Claim 10, Schaar in view of Ophir teaches the method of claim 4, Schaar teaches wherein the layer comprising the two-dimensional material used to obtain the data is supported on a support surface having a non-uniform composition, a spatial variation of the composition in the support surface (asymmetry “non-planar” [0023], non-linear considered “non-planar” [0077], [0090], [0112], [0171]) Ophir teaches obtaining the training dataset (Fig 1). It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the present claimed invention, to modify the teaching of Schaar using machine learning model to train as taught by Ophir that would facilitate using the estimation model to provide estimations of the at least one metrology metric with respect to the measurement data and optionally compensating for process errors using the derived estimation model(s) (Ophir [0023]).
Schaar and Ophir do not explicitly teach providing a predetermined defect distribution in the layer. Tel teaches providing a predetermined defect distribution in the layer (a pattern on the substrate exceeds a certain threshold, one or more defects may likely be produced, page 37 lines 16-20). It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the present claimed invention, to modify the teachings of Schaar and Ophir to provide a threshold of defect as taught by Tel that would predict before etching process takes place based the pattern on the substrate.
Claim 11 is rejected under 35 U.S.C. 103 as being obvious over Schaar in view of Ophir, Tel and US 2019/0147127 of Su et al., hereinafter Su.
As per Claim 11, Schaar in view of Ophir and Tel teaches the method of claim 9, the above combination does not teach wherein the machine learning model is a supervised machine learning model and the predetermined defect distribution is used directly to provide labels for measurement data from the first measurement process in the training dataset. Su teaches the machine learning model is a supervised machine learning model (see [0039]) and the predetermined defect distribution is used directly to provide labels for measurement data from the first measurement process in the training dataset ([0040], [0047], hot spot considered “defect” [0055]). It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the present claimed invention, to modify the teachings of Schaar, Ophir, and Tel having a supervised machine learning to train a hot spot “pattern” as taught by Su that would provide a SVM model that represents samples of points in spaces (Su {0047]).
Claim 15 is rejected under 35 U.S.C. 103 as being obvious over Schaar in view of Ophir, and further Lewis.
As per Claim 15, Schaar in view of Ophir teaches the apparatus of claim 14, Schaar and Ophir do not teach wherein: the first measurement process comprises detecting an image in a bright field imaging mode; and the second measurement process comprises detecting an image in a dark field imaging mode. Lewis teaches the first measurement process comprises detecting an image in a bright field imaging mode; and the second measurement process comprises detecting an image in a dark field imaging mode ([0016], [0040]). It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the present claimed invention, to modify the teachings of Schaar, Ophir, and Baselmans detecting bright field and dark field imaging as taught by Lewis that would have desirable to provide a bright field system where such deficiencies are not present and dark field system with adequate sensitivity but improved throughput (Lewis, [0005], [0008]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US20180299794 of Pandet et al. Method of measuring, device manufacturing method, metrology apparatus, and lithographic system.
US patent 10935893 of Pandev et al. Differential methods and apparatus for metrology of semiconductor targets.
US 2019/0378012 of Tripodi et al. Metrology Apparatus and Method for Determining a Characteristic of One or More Structures on a Substrate.
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/LYNDA DINH/Examiner, Art Unit 2857
/LINA CORDERO/Primary Examiner, Art Unit 2857