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 .
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 2–4 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 2 & 4 recite computer-implemented functions including, among other limitations: (i) storing, in a data set, the layer thickness variations, determined between the measuring points, together with corresponding grayscale value variations between the grayscale values of pixels, wherein the pixels correspond to the measuring points (claim 2); and (ii) capturing at least one further grayscale image of the surface layer of the further substrate (claim 2); and (iii) utilizing the data set to create the layer thickness variation profile of the surface layer of the further substrate on a basis of grayscale values of the further grayscale image (claim 2); and (iv) linking a plurality of data sets in a data packet and using the data packet to train an artificial intelligence model, wherein the artificial intelligence model creates the respective layer thickness variation profile solely from the grayscale values or the grayscale value variations of grayscale images captured from further substrates (claim 4).
Applicant(s) is/are respectfully reminded that, for computer-implemented functional claims, “examiners should determine whether the specification discloses the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter.” MPEP §2161.01(I).
As an initial matter, the Examiner notes that claims 2–4 appear to be originally-filed claims. However, originally-filed claim language does not necessarily satisfy written description. Accord Ariad Pharm., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1349 (Fed. Cir. 2010) (original claim language does not necessarily satisfy written description). That is to say, originally-filed claim 2–4 themselves do not provide an algorithm that performs the functions in sufficient detail such that one of ordinary skill in the art can reasonably make and/or use the invention.
The Examiner further notes that claim 1 directly measures layer thicknesses at measuring points using a measuring device and correlates those measured thickness variations with grayscale value variations for the substrate, whereas claims 2–4 materially further recite additional limitations to creating thickness variation profiles for further substrates based on grayscale values alone and/or by an AI model “solely” from grayscale values or variations, which is the subject matter for which written description is lacking.
Furthermore, the specification does not describe an algorithm that performs the foregoing claimed functions in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at filing.
The specification states, in a results-oriented manner, that that a thickness variation profile “can be determined on the basis of the grayscale values of the grayscale image,” [0041] and that “the data set is then used to evaluate directly the grayscale image captured from a further substrate surface,” [0045] and further that a “data packet is used to train an artificial intelligence which creates … solely from the grayscale values,” [0049] without providing concrete algorithmic steps for achieving those results across the breadth of “further substrates”. These are functional descriptions of results at a high level (functional descriptions of results define what a system, product, or process is expected to do, outlining its intended behavior, features, and capabilities without specifying the exact method for achieving them). The specification fails to disclose, in algorithmic terms, how the computer: (i) applies or utilizes a data set derived from one substrate to create a thickness variation profile for a further substrate based on grayscale values (e.g., what normalization or calibration is performed so grayscale values or variations remain comparable across different capture conditions and across different substrate; what constraints are required for the mapping to remain valid on the further substrate); (ii) constructs the claimed “data packet” linking multiple data sets in a manner usable for training (e.g., what common representation, labeling or metadata is used to align data sets from different substrates and define training targets/ ground truth for the claimed “profile”); and (iii) trains the recited artificial intelligence model to create the thickness variation profile “solely” from grayscale values or variations for further substrates (e.g., the input feature representation, output definition for the “layer thickness variation profile,” model class or architecture, training methodology, and validation criteria sufficient to show possession of the broadly claimed “solely from grayscale” functionality across further substrates).
Applicant is also reminded: “if the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention including how to program the disclosed computer to perform the claimed function, a rejection under 35 U.S.C. 112(a) … for lack of written description must be made.” MPEP §2161.01(I).
Therefore, because an algorithm for the functions (a) utilizing a stored data set to create a layer thickness variation profile for a further substrate based on grayscale values, and (b) linking multiple data sets to train an artificial intelligence model that creates the respective layer thickness variation profile solely from grayscale values or variations for further substrates, is not disclosed in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor invented and possessed the claimed subject matter, and in accordance with MPEP §2161.01(I), claims 2–4 are rejected for lack of written description.
Dependent claims 3 fail to cure this deficiency of its respective base claim 2 and is rejected accordingly.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 5 and 6 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 5 recites "in the step of selecting the plurality of measurement fields, not taking into account regions in a substrate center and/or on a substrate edge of the substrate". This term is unclear in meaning and scope, such that it is not reasonably certain what constitutes the claimed meaning. (i) the terms “substrate center” and “substrate edge” are relative terms and the claim provides no objective boundaries for either region (e.g., a defined distance from the edge, a percentage of substrate area, or a defined geometric partition). (ii) The use of “and/or” renders unclear whether the exclusion applies to (a) center regions only, (b) edge regions only, or (c) both center and edge regions, and therefore introduces multiple alternative scopes without providing an objective standard for any of the alternatives. (iii) The phrase “not taking into account regions” is itself ambiguous as to what is excluded and how the exclusion is applied during “selecting the plurality of measurement fields”. For example, the claim is unclear whether the method: (a) excludes candidate measurement fields that fall within the center or edge regions; (b) excludes candidate measurement fields that partially overlap the center or edge regions; (c) excludes only the portions or pixels of a measurement field that are within the center or edge regions while still using the remainder of that measurement field; or (d) selects measurement fields first and then later disregards (“does not take into account”) data corresponding to the center or edge regions. Each of these reasonable interpretations produces materially different claim scope.
The terms in claim 5 are relative terms which renders the claim indefinite. The terms are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Accordingly, because the metes and bounds of claim 5 cannot be determined with reasonable certainty in view of the undefined “substrate center”, “substrate edge” and the multiple alternatives created by “and/or” and the ambiguity of “not taking into account regions” within the measurement-field selection step, claim 5 is indefinite under 35 U.S.C. 112(b).
Claim 6 depends from claim 5 and further recites selecting measurement fields “in which grayscale contrasts are highest in comparison with other regions of the substrate surface”. This limitation is indefinite for multiple, independent reasons: (i) the term “grayscale contrasts” lacks an objective definition in the claim. The claim does not specify what “contrast” metric is used (e.g., max–min grayscale difference, standard deviation or variance, or another quantitative criterion). Different reasonable contrast metrics would yield different “highest” regions and therefore different claim scope. (ii) The relative term “highest” is indefinite because the claim fails to specify the basis of comparison and the domain over which “highest” is evaluated (e.g. among all candidate measurement fields; or among all regions of the substrate surface (including regions that are not measurement fields); or within a local neighborhood around each candidate field; or under any thresholding/ selection rule. Each of these reasonable interpretations yields materially different claim scope). (iii) the claim is unclear as to what constitutes a “region” for purposes of the comparison.
Accordingly, because claim 6 uses relative terminology (“grayscale contrasts”, “highest” and “other regions”) without providing objective boundaries, metrics, or a comparison framework, the metes and bounds of claim 6 cannot be determined with reasonable certainty, and claim 6 is indefinite under 35 U.S.C. 112(b).
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 1–8 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more. The flow chart in MPEP §2106, Subject Matter Eligibility Test For Products and Processes, is referred to for establishing ineligible subject matter.
Step 1 (Statutory Category): Claim 1 recites a method for creating a layer thickness variation profile of a surface layer of a substrate, which is categorized as a process under the four recognized statutory categories.
Step 2A, Prong One (Judicial Exception): However, the claim is further directed to the abstract idea of (i) capturing a grayscale image of a surface layer of a substrate; (ii) selecting measurement fields and measuring layer thicknesses at measurement points; (iii) determining grayscale value variations between points/pixels; (iv) determining layer thickness variations by calculating differences between measured thicknesses; (v) associating the layer thickness variations with corresponding grayscale value variations; and (vi) creating a layer thickness variation profile based on grayscale values. These recitations encompass mental processes (evaluation/ comparison/ association steps that can be performed in the mind or by pen and paper at a high level, e.g., determining “variations,” “differences,” and generating a “profile” from observed values) and mathematical concepts (e.g., calculating differences and using a mapping, association between grayscale-value variation and thickness variation), which are judicial exceptions (see MPEP §2106.04(a)(2) (mental processes; mathematical relationships/ formulas)).
Step 2A, Prong Two (Integration into a Practical Application): The additional elements consist of a camera for capturing the grayscale image, a measuring device for measuring layer thicknesses at measurement points, and related functional steps (e.g., selecting measurement fields/points) that merely perform data acquisition and then feed the acquired data into the abstract processing and output results (the layer thickness variation profile). The claim does not recite any particularized rule-based constraints, specific image-processing or metrology mechanisms, or improvements to the functioning of the computer, camera, or thickness-measurement technology; rather, it uses functional steps to perform the abstract analysis and produce an information output. The recited “substrate/ surface layer” context amounts to a field-of-use environment for the abstract analysis. As such, the additional elements amount to merely applying the exception using conventional measurement and image capture and do not integrate the judicial exception into a practical application (see MPEP §2106.05(d) (mere instructions to apply an exception) and §2106.05(f) (insignificant extra-solution activity)).
Step 2B (Significantly More/Inventive Concept): Considered individually and in combination, the additional claim elements do not amount to significantly more than the judicial exception, as explained above. The claims recite well-understood, routine, and conventional components (e.g., a camera, a measuring device, and generic data storage/ processing) performing their routine functions in the field and are claimed at a high level of generality, without any non-conventional arrangement or improvement to the underlying computer or measurement technology (see MPEP §2106.05(d), (e), (h)). Therefore, claim 1 is ineligible under 35 U.S.C. §101.
Regarding claims 2–8, considered individually and in combination, the additional limitations (including, for example, creating/ storing/ using a dataset linking thickness-variation points to grayscale-variation points; training and/or using an AI model based on such data; selecting measurement fields based on location and/or grayscale contrast; and specifying a transparent surface layer and an optical thickness measuring device) amount to further data organization/ analysis, mathematical processing, and field-of-use limitations. These additional limitations do not integrate the judicial exception into a practical application and do not amount to significantly more than the judicial exception. Therefore, claims 2–8 are also ineligible under 35 U.S.C. §101.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1–3 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Iwanaga (Iwanaga et al., US 2015/0324970 A1, 2015).
Regarding claim 1, Iwanaga teaches a method for creating a layer thickness variation profile of a surface layer of a substrate, the method comprising the following steps:
capturing a grayscale image of a substrate surface of the substrate with a camera (Iwanaga teaches an imaging unit that images a surface of the substrate [0012], and the imaged image is RGB or a gray scale, [0059], [0086]);
selecting a plurality of measurement fields on the substrate surface from regions of the grayscale image (Iwanaga teaches extracting pixel values from the imaged image at pixels corresponding to a plurality of coordinates on the wafer surface (i.e., selecting, from regions of the imaged image, plural regions or pixels that correspond to plural locations on the substrate surface) [0069]; and further teaches extracting pixel values for a plurality of pixels in the imaged image (e.g., hundreds of points) and that the number of extracted pixels “can be arbitrarily set,” i.e., selecting a plurality of image regions/pixels as measurement fields [0072], [0086]);
measuring layer thicknesses of the surface layer of the substrate with a measuring device at a plurality of measuring points in each of the measurement fields (Iwanaga teaches measuring the film thickness of a film formed on the upper surface of a measurement preparation wafer using a film thickness measurement means, where a thicknessmeter utilizing reflectance spectroscopy is used [0065]; and further teaches that the measurement is performed at a plurality of points (e.g., 51 points) on the wafer and that the film thickness measured values are acquired for each measurement point together with corresponding coordinates in a measured value table [0066]–[0067], and similarly teaches that “the film thicknesses at a plurality of points are measured” and acquired for each measurement point [0119]);
determining grayscale value variations between the grayscale values present at measuring points of the measurement fields of the grayscale image (Iwanaga teaches extracting, for each coordinate corresponding to each measuring point, the pixel value from the imaged image and generating a pixel value extraction table [0069]; and further teaches evaluating or using the “change amount” of the pixel value in forming the correlation between pixel value and film thickness measured value [0070]–[0071], that a grayscale used for the imaged image [0086], which corresponds to determining variations (differences) between the grayscale/pixel values at the measuring points);
determining layer thickness variations by calculating a difference between the layer thicknesses measured at the measuring points of the measurement fields (Iwanaga teaches acquiring film thickness measured values at a plurality of points [0014], including evaluating “change amount(s)” of film thickness measured value(s) in the correlation processing [0072]);
associating the layer thickness variations determined between the measuring points with corresponding grayscale value variations determined between the respective measuring points (Iwanaga teaches that a grayscale used for the imaged image [0086], and teaches generating a correlation data table in which a film thickness measured value and a pixel value at the same coordinates are associated with each other, and further teaches generating a correlation between film thickness measured values and pixel values by using the change amount of the film thickness measured value and the change amount of the pixel value [0014], [0070]–[0071]); and
creating the layer thickness variation profile of the surface layer based on the grayscale values of the grayscale image (Iwanaga teaches that a grayscale is used for the imaged image [0086], and teaches calculating film thickness on the basis of a pixel value of the imaged image and the correlation data [0014], and generating a film thickness distribution chart/ profile over the substrate; [0074], [0080]).
Although different embodiments of Iwanaga have been referred to, it would have been exceedingly obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Iwanaga by combining Iwanaga’s similar embodiments in order to not limit the embodiments to themselves but include other evident combinations and extensions thereof (see Iwanaga, [0121]).
Regarding claim 2, Iwanaga teaches the method according to claim 1, which comprises:
storing in a data set the layer thickness variations determined between the measuring points together with the grayscale value variations determined between the corresponding measuring points (Iwanaga teaches storage part 160 storing or maintaining film thickness measured values with coordinates and extracting, storing pixel values corresponding to the coordinates in tables used to generate correlation data (pixel value extraction table/ correlation data table) [0062]);
capturing at least one further grayscale image of a further substrate surface of a further substrate (Iwanaga teaches imaging a subsequent wafer W (film thickness measurement object) to capture further an imaged image [0114]); and
utilizing the data set to create the layer thickness variation profile of a surface layer of the further substrate on a basis of grayscale values of the further grayscale image of the further substrate surface of the further substrate (Iwanaga teaches calculating film thickness of the resist film formed on the wafer W using the imaged image and the correlation data, thereby obtaining the film thickness distribution over the wafer W; [0072]-[0074], [0115]-[0116]).
Regarding claim 3, Iwanaga teaches the method according to claim 2, which comprises assigning the layer thickness variations contained in the data set to different layer thickness variation ranges, and assigning the individual layer thickness variation ranges to at least one of the grayscale value variations, respectively (Iwanaga teaches generating correlation data and a correlation graph/ approximation between film thickness measured values and pixel values [0070]–[0071], and using the correlation (e.g., an approximation expression including linear, quadratic or higher expression) to convert pixel values (including different pixel variations) into corresponding film thickness values (including different film thickness variation ranges) across the wafer to produce the film thickness distribution chart [0073]–[0074]. Applying the correlation function across the entire pixel domain necessarily establishes an assignment where every grayscale interval (range of pixel values) corresponds to a thickness interval (thickness variation ranges)).
Regarding claim 8, Iwanaga teaches the method according to claim 1, wherein the surface layer is transparent, and the measuring device is an optical measuring device (Iwanaga teaches the surface layer is a resist oxide film (transparent and can be measured optically via reflected light) formed on a wafer [0087] & [0103]–[0105], and teaches film thickness measurement means using a thicknessmeter utilizing reflectance spectroscopy [0102] (optical measuring device). In optical lithography, a photoresist film is required to be transparent for pattern transfer through the film thickness).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Iwanaga (Iwanaga et al, US 2015/0324970 A1, 2015), in view of Dhandapani (Dhandapani et al, US 2021/0407065 A1, 2021).
Regarding claim 4, with deficiencies of Iwanaga noted in square brackets [], Iwanaga teaches the method according to claim 2, which comprises [storing a plurality of the data sets which have been determined for different substrates and linking the plurality of data sets in a data packet], and [using the data packet to train an artificial intelligence model which creates the layer respective thickness variation profile solely] from the grayscale values or the grayscale value variations of grayscale images captured from further substrates (Iwanaga teaches capturing an imaged image of a wafer that is a film thickness measurement object in the imaging unit [0079], and teaches that a grayscale is used for the imaged image [0086], and further teaches extracting the pixel value of pixels of the imaged image and calculating film thickness in a unit of each pixel on a basis of the pixel value of the imaged image and the correlation data [0080]; Iwanaga further teaches using the change amount of the pixel value in the correlation processing [0071], which corresponds to using grayscale value variations).
As noted above in square brackets [], Iwanaga does not teach, but Dhandapani teaches:
storing a plurality of the data sets which have been determined for different substrates and linking the plurality of data sets in a data packet (Dhandapani teaches that, for each individual region on each calibration substrate, a calibration image is associated with a ground truth thickness measurement and that the images and associated thickness measurements can be stored in a database as records, with each record including a calibration image and a ground truth thickness measurement; further, the deep learning-based algorithm is trained using the combined data set [0048]–[0051])
using the data packet to train an artificial intelligence model which creates the layer respective thickness variation profile solely (Dhandapani teaches that film thickness can be measured by applying an input image to the neural network [0020]; that a model is established between images and thickness measurements [0046]; that intensity values of the input image are entered into the trained image processing algorithm to output an estimated thickness; and that the neural network is trained using the combined data set and performs regression analysis of intensity values of the input images with ground truth thickness measurements to generate a model that predicts layer thickness based on an image [0051–0052])
It would have been prima facie obvious to a POSITA, before the effective filing date of the claimed invention, motivated to modify Iwanaga’s method of storing pixel-value, thickness correlation data and using such data to generate a film thickness distribution from captured images, to instead store a plurality of such image-and-thickness data sets for different substrates and use the linked data sets as a training data packet to train an artificial intelligence model as taught by Dhandapani, because Dhandapani expressly teaches that training a neural network regression model on stored image intensity values paired with ground truth thickness measurements enables predicting film thickness from images, thereby improving model accuracy and generalization across different substrate conditions, more diverse training datasets yield better model performance. The predictable result would be a neural network with improved thickness estimation capability across the full range of substrate variations encountered in semiconductor manufacturing.
Claims 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Iwanaga (Iwanaga et al, US 2015/0324970 A1, 2015), in view of Chen (Chen et al, US 2013/0236085 A1, 2013).
Regarding claim 5, with deficiencies of Iwanaga noted in square brackets [], Iwanaga teaches the method according to claim 1, which comprises, [in the step of selecting the plurality of measurement fields, not taking into account regions in a substrate center and/or on a substrate edge of the substrate].
As noted above in square brackets, Iwanaga does not teach, but Chen teaches: in the step of selecting the plurality of measurement fields, not taking into account regions in a substrate center and/or on a substrate edge of the substrate (Chen teaches defining “edge exclusion for the wafer edge mask,” and further teaches that “polar and Cartesian masks can be added … to exclude certain areas of the wafer surface in the site metric calculation,” [0047] i.e., selecting or defining measurement fields while excluding (not taking into account) edge regions and other excluded regions such as a center region via masking or exclusion parameters).
It would have been prima facie obvious to a POSITA, before the effective filing date of the claimed invention, motivated to modify Iwanaga’s selection of the plurality of measurement fields to not take into account regions in a substrate center and/or on a substrate edge as taught by Chen, because Chen expressly teaches using edge-exclusion and masking to exclude certain wafer surface areas from site metric calculations in order to suppress a strong filter response caused by (i) sharp wafer edge roll-off and (ii) a data discontinuity created by an edge exclusion operation, thereby improving the reliability and accuracy of image-based measurements; applying the same exclusion to Iwanaga’s image-pixel/ thickness correlation and thickness profile generation would have been a predictable use of a known technique to improve measurement robustness without changing the underlying thickness-mapping approach.
Regarding claim 6, the combination of Iwanaga and Chen teaches the method according to claim 5, wherein the step of selecting the plurality of measurement fields comprises selecting measurement fields in which grayscale contrasts are highest in comparison with other regions of the substrate surface (Chen teaches filtering, processing wafer surface images to enhance “surface feature" or "background contrast” and further teaches reporting or grouping site metric values using thresholds to identify site regions whose metric values are outside a variation range, significantly higher than their surroundings [0061], which corresponds to selecting measurement fields having the highest grayscale contrast relative to other regions).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Iwanaga (Iwanaga et al, US 2015/0324970 A1, 2015), in view of Uziel (Uziel et al, US 2017/0084425 A1, 2017).
Regarding claim 7, with deficiencies of Iwanaga noted in square brackets [], Iwanaga teaches the method according to claim 1, wherein [the step of selecting the plurality of measurement fields comprises selecting measurement fields that partially overlap].
As noted above in square brackets, Iwanaga does not teach, but Uziel teaches: the step of selecting the plurality of measurement fields comprises selecting measurement fields that partially overlap (Uziel discloses that during scanning, the system acquires a plurality of image frames corresponding to multiple partially overlapping fields of view, which are used as the measurement fields, that can be consolidated to a single effective field of view [0062]).
It would have been prima facie obvious to a POSITA, before the effective filing date of the claimed invention, motivated to modify Iwanaga’s step of selecting the plurality of measurement fields to select measurement fields that partially overlap as taught by Uziel, because using overlapping fields is a well-known imaging, metrology technique so that the resulting thickness-variation profile is more accurate and less sensitive to local noise or edge effects at the borders of non-overlapping fields. The modification is a predictable use of a known technique (overlapping fields) to improve measurement reliability in Iwanaga’s image-based thickness-profiling workflow.
Conclusion
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KEN KUDO
Examiner
Art Unit 2671
/KEN KUDO/Examiner, Art Unit 2671
/VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671