This Office action is in response to application filed on 7/06/2026.
DETAILED ACTION
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 .
Response to Amendment
Applicant’s amendments filed on 7/06/2026 to the claims are entered.
Claims 1, 4-5, 8, 13-14, and 18-20 have been amended.
Claims 1-20 have been examined.
Response to Arguments
Applicant’s arguments filed on 7/06/2026 to the 101 and prior art rejections have been fully considered.
Applicant’s arguments regarding prior art are moot in view of new grounds rejection as necessitated by amendments.
Regarding the 101 rejection, Applicant’s argues that when the claimed subject matter is considered as a whole, it is apparent that it is directed to a physical and technical measurement technique. Surely a COVID test or method of performing a COVID test is patent eligible subject matter. Likewise, here the physical measurement technique of a 2D material is directed to patent eligible patent subject matter.
In response, the examiner respectfully disagrees. Applicant referred to a method of performing a COVID test as patent eligible. However, Applicant did not clarify what COVID testing application has been patented. Therefore, the argument is not persuasive. The claims as a whole are not directed to a physical and technical measurement technique because the current claims perform conventional metrology to obtain metrology information (i.e., performing the process to gather data; see MPEP 2106.05(b): “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more” and MPEP 2106.05(c): “A transformation that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more (or integrate a judicial exception into a practical application)”) for training a machine learning model, without reciting a practical application (see MPEP 2106.05(a)-(c) and (e)). Thus, the claims are not eligible.
Claim Objections
Claims 1, 3-4, 11, and 13-14 are objected to because of the following informalities:
Claim 1, line 12: “metrology information” should read “the metrology information”.
Claim 3, lines 2-3: “a target portion” should read “the target portion”, line 7: “metrology information” should read “the metrology information”, line 8: “measurement information” should read “the measurement information”
Claim 4, line 14: “measurement data” should read “the measurement data”
Claim 11: “measurement data” should read “the measurement data”.
Claim 13, line 12: “measurement data” should read “the measurement data”.
Claim 14, line 15: “measurement data” should read “the measurement data”.
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 under the grouping of mathematical concepts by reciting limitations including “processing the measurement data to obtain metrology information about the target portion of the layer (Claim 1); “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” (Claim 4); “obtain metrology information for the target portions from the respective detected distributions of radiation in the pupil plane” (Claim 13); and “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” (Claim 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 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, the layer comprising a two-dimensional material, the two-dimensional material having anisotropy of a material property in lateral directions within a plane of the material compared to no or less anisotropy of the material property in a direction perpendicular to the plane of the material … 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” (Claim 1);
“performing a first measurement process on a layer formed on a substrate for multiple different target portions of the layer to obtain a training dataset, the layer comprising a two-dimensional material having anisotropy of a material property in lateral directions within a plane of the material compared to no or less anisotropy of the material property in a direction perpendicular to the plane of the material and the first measurement process comprising illuminating a target portion of the layer with a beam of radiation and detecting a distribution of radiation, the radiation redirected by the target portion of the layer, to obtain measurement data; and using the obtained training dataset to train the machine learning model …” (Claim 4);
“A metrology apparatus configured to perform metrology on a substrate, the apparatus comprising: a measurement system 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, the two-dimensional material having anisotropy of a material property in lateral directions within a plane of the material compared to no or less anisotropy of the material property in a direction perpendicular to the plane of the material; and a data processing system configured to: control the measurement system to obtain the measurement data for plural different target portions; and use a machine learning model … (Claim 13); and
“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 layer of two-dimensional material for multiple different target portions of the layer, the two-dimensional material having anisotropy of a material property in lateral directions within a plane of the material compared to no or less anisotropy of the material property in a direction perpendicular to the plane of the material and the first measurement process comprising illumination of a target portion of the layer with a beam of radiation and detection of a distribution of radiation, the radiation redirected by the target portion of the layer, to obtain measurement data; and a data processing system configured to use a training dataset derived from the first measurement process to train a machine learning model …” (Claim 14)
add extra-solution activities (e.g., mere data gathering) using elements recited at a high level of generality (e.g., obtained from generic measurement system); append generic computer components/implementation (e.g., data processing system, using measurement data to train a machine learning model) and/or generally link the use of the judicial exception to a field of use. These additional limitations, when considered individually and in combination, 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.
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, mere computer implementation, and/or a field of use, 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.
Claims 1, 4, and 13-14 are not patent eligible under 35 U.S.C. 101.
Dependent claims 2-3, 5-12, and 15-20 do not disclose limitations that either integrate the judicial exception into a practical application or amount to significantly more. The claims merely extend (or narrow) the abstract idea by adding additional details to the algorithm which forms the abstract idea as discussed above.
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 1, 12, and 18 are rejected under 35 U.S.C. 103 as being obvious over US 2017/0059999 of Van Der Schaar et al., hereinafter Schaar (of record) in view of US 2019/0265556 of Miao et al., hereinafter Miao.
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]).
Schaar does not explicitly teach the two-dimensional material having anisotropy of a material property in lateral directions within a plane of the material compared to no or less anisotropy of the material property in a direction perpendicular to the plane of the material.
Miao teaches the two-dimensional material having anisotropy of a material property in lateral directions within a plane of the material compared to no or less anisotropy of the material property in a direction perpendicular to the plane of the material (Abstract, last 6 lines - a system with distinct directional (anisotropic) material properties, where lateral in-plane behavior differs significantly from the perpendicular out-of-plane direction ).
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 Miao to provide a system with distinct directional (anisotropic) material properties, which may increase the reflectivity, thereby improving the display brightness and/or improving the display accuracy (see Miao Abstract and [0048], [0063]).
As per Claim 12, Schaar in view of Miao teaches a method for providing a layer, Schaar teaches 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 in view of Miao 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 cause executed of at least the method of claim 1 (see [0147]-[0148]).
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 Miao and further US 2021/0142466 of Ophir et al., hereinafter Ophir (of record).
As per Claim 2, Schaar in view of Miao teaches the method of claim 1, Schaar teaches 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, Schaar and Miao do 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 teachings of Schaar and Miao 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 Miao and 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 teachings of Schaar and Miao 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 a method comprising: illuminating a target portion of the layer with a beam of radiation and detecting a distribution of radiation (see [0013] ), the radiation redirected by the target portion of the layer, to obtain measurement data [0053]-[0056], [0058]), and using the obtained data comprising a two-dimensional material from measurement data obtained ([0012], [0054], [0064]-[0065]).
Schaar does not teach performing a first measurement process on a layer formed on a substrate for multiple different target portions of the layer to obtain a training dataset, the layer comprising a two-dimensional material having anisotropy of a material property in lateral directions within a plane of the material compared to no or less anisotropy of the material property in a direction perpendicular to the plane of the material and the first measurement process
Miao teaches the layer comprising a two-dimensional material having anisotropy of a material property in lateral directions within a plane of the material compared to no or less anisotropy of the material property in a direction perpendicular to the plane of the material and the first measurement process (Abstract, last 6 lines - a system with distinct directional (anisotropic) material properties, where lateral in-plane behavior differs significantly from the perpendicular out-of-plane direction ).
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 Miao to provide a system with distinct directional (anisotropic) material properties, which may increase the reflectivity, thereby improving the display brightness and/or improving the display accuracy (see Miao Abstract and [0048], [0063]). .
Schaar and Miao do not teach performing a first measurement process on a layer formed on a substrate for multiple different target portions of the layer to obtain a training dataset.
Ophir teaches performing a first measurement process on a layer formed on a substrate for multiple different target portions of the layer to obtain a training dataset (Fig 1 shows machine learning algorithm(s) 110 received training data 105, see [0016]-[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 teachings of Schaar and Miao 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 Miao and Ophir teaches the method of claim 4, Ophir further teaches comprising 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 teachings of Schaar and Miao 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 Miao and Ophir teaches the method of claim 4, Schaar teaches wherein the first measurement process comprises obtaining the 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 the two-dimensional material having anisotropy of a material property in lateral directions within a plane of the material compared to no or less anisotropy of the material property in a direction perpendicular to the plane of the material.
Miao teaches the two-dimensional material having anisotropy of a material property in lateral directions within a plane of the material compared to no or less anisotropy of the material property in a direction perpendicular to the plane of the material (Abstract, last 6 lines - a system with distinct directional (anisotropic) material properties, where lateral in-plane behavior differs significantly from the perpendicular out-of-plane direction ).
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 Miao to provide a system with distinct directional (anisotropic) material properties, which may increase the reflectivity, thereby improving the display brightness and/or improving the display accuracy (see Miao Abstract and [0048], [0063]). .
Schaar and Miao do not teach using 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 teachings of Schaar and Miao 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 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]), and the first measurement process comprising illumination of a target portion of the layer with a beam of radiation and detection of a distribution of radiation, the radiation redirected by the target portion of the layer, to obtain measurement data ( see [0013], [0053]-[0056], [0058] ).
Schaar does not teach having anisotropy of a material property in lateral directions within a plane of the material compared to no or less anisotropy of the material property in a direction perpendicular to the plane of the material, and 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.
Miao teaches having anisotropy of a material property in lateral directions within a plane of the material compared to no or less anisotropy of the material property in a direction perpendicular to the plane of the material (Abstract, last 6 lines - a system with distinct directional (anisotropic) material properties, where lateral in-plane behavior differs significantly from the perpendicular out-of-plane direction ).
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 Miao to provide a system with distinct directional (anisotropic) material properties, which may increase the reflectivity, thereby improving the display brightness and/or improving the display accuracy (see Miao, Abstract and [0048], [0063]).
Schaar and Miao 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 teachings of Schaar and Miao 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 Miao and 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, 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 teachings of Schaar and Miao 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 Miao and Ophir teaches the non-transitory 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]).
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 teachings of Schaar and Miao 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 Miao and 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 cause execution of 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 Miao, Ophir and further US 2007/0279611 of Baselmans et al., hereinafter Baselmans (of record).
As per Claim 6, Schaar in view of Miao and 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, Miao, 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, Miao, 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 Miao, Ophir, and Baselmans 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 a 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, Miao, and Ophir do 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, Miao, 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 Miao, Ophir, Baselmans and further US 2008/0165343 of Lewis et al., hereinafter Lewis.
As per Claim 7, Schaar in view of Miao, 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, Miao, 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 Miao, Ophir and further WO 2018/153711 of Tel et al., hereinafter Tel.
As per Claim 9, Schaar in view of Miao, and 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, Miao, 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 defection 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 Miao, and 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 teachings of Schaar, and Miao 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, Miao 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, Miao, and Ophir to provide a threshold of defection 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 Miao, Ophir, Tel and further US 2019/0147127 of Su et al., hereinafter Su.
As per Claim 11, Schaar in view of Miao, 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, Miao, 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 Miao, Ophir, and further Lewis.
As per Claim 15, Schaar in view of Miao, Ophir teaches the apparatus of claim 14, Schaar, Miao, 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, Miao, Ophir, and Lewis 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
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 extension fee 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 LYNDA DINH whose telephone number is (571) 270-
7150. The examiner can normally be reached on M-F 10 PM-6 PM ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an
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(AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Arleen M Vazquez can be reached on 571-272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LYNDA DINH/Examiner, Art Unit 2857
/LINA CORDERO/Primary Examiner, Art Unit 2857
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