DETAILED ACTION
Notice of AIA Status
The present application is being examined under the AIA the first inventor to file provisions.
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/24/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 1, 11, and 19-20 are objected to because of the following informalities:
In claim 1, Line 6 the term “the target region is photographed,” should be changed to “the target region is photographed;” for typographical/grammar issues to avoid clarity issues.
In claim 1, Line 8 the term “the target region of the first image,” should be changed to “the target region of the first image;” for typographical/grammar issues to avoid clarity issues.
In claim 1, Line 9 the term “generate reference grid coordinates from the centroid coordinates,” should be changed to “generate reference grid coordinates from the centroid coordinates;” for typographical/grammar issues to avoid clarity issues.
In claim 11, Line 8 the term “by applying a segmentation learning model to the SEM image,” should be changed to “by applying a segmentation learning model to the SEM image;” for typographical/grammar issues to avoid clarity issues.
In claim 11, Line 10 the term “generate a plurality of bounding boxes for each of the plurality of target regions by using an object detection algorithm,” should be changed to “generate a plurality of bounding boxes for each of the plurality of target regions by using an object detection algorithm;” for typographical/grammar issues to avoid clarity issues.
In claim 11, Line 12 the term “acquire a second image in which a plurality of centroid coordinates are displayed for each of the plurality of bounding boxes,” should be changed to “acquire a second image in which a plurality of centroid coordinates are displayed for each of the plurality of bounding boxes;” for typographical/grammar issues to avoid clarity issues.
In claim 11, Line 14 the term “generate a plurality of reference grid coordinates from the plurality of centroid coordinates,” should be changed to “t generate a plurality of reference grid coordinates from the plurality of centroid coordinates;” for typographical/grammar issues to avoid clarity issues.
In claim 19, Line 7 the term “a scanning electron microscope (SEM) image in which a target pattern is photographed,” should be changed to “a scanning electron microscope (SEM) image in which a target pattern is photographed;” for typographical/grammar issues to avoid clarity issues.
In claim 19, Line 9 the term “are respectively displayed for each of the plurality of target regions,” should be changed to “are respectively displayed for each of the plurality of target regions;” for typographical/grammar issues to avoid clarity issues.
In claim 19, Line 12 the term “second horizontal directions by using the plurality of centroid coordinates,” should be changed to “second horizontal directions by using the plurality of centroid coordinates;” for typographical/grammar issues to avoid clarity issues.
In claim 20, Line 4 the term “by performing affine transformation for the plurality of centroid coordinates,” should be changed to “by performing affine transformation for the plurality of centroid coordinates;” for typographical/grammar issues to avoid clarity issues.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Claims 11-12, recites limitations that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claim 11; recites the limitation, “an observation device configured to acquire” [Line 4].
Claim 12; recites the limitation, “the observation device is configured to acquire” [Line 1-2].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claims 11-12:
“observation device” (Fig. 1, #100A. Paragraph [0030]- the observation device 100A may be a device for acquiring a scanning electron microscope (SEM) image (e.g., SEM image IM_S of FIG. 4) that will be described later. In example embodiments, the observation device 100A may be a scanning electron microscope (SEM). (wherein the observation device does have sufficient structure associated with it of a Scanning electron Microscope.).)).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 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 of this title, 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 1-5, 11-14, and 19 are rejected under 35 U.S.C 103 as being unpatentable over Yamaguchi et al. (US 20160320182 A1) hereafter referenced as Yamaguchi in view of Smith et al. (US 20230281819 A1) hereafter referenced as Smith.
Regarding claim 1, Yamaguchi teaches an image processing device comprising (Fig. 1, Paragraph [0011]- Yamaguchi discloses the pattern measurement device includes a computation device that measures dimensions between patterns formed in a sample, by using data which is obtained by irradiating the sample with a beam.):
a memory configured to store instructions (Fig. 8, #805 called a memory, paragraph [0078]- Yamaguchi discloses a computation processing unit 804 and a memory 805 are mounted in the computation processing device 803.);
and a processor configured to access the memory and execute the instructions (Fig. 1, Paragraph #123 called an image processing processor [0091]- Yamaguchi discloses when grid measurement is executed, grid information which has been registered in advance is read from the storage device 1231 or the memory 805 to the image processing processor 123 or the computation processing device 803 (S601).),
wherein, when executing the instructions, the processor is configured to: acquire a first image (Fig. 8, Paragraph [0078]- Yamaguchi discloses the computation processing unit 804 supplies a predetermined control signal to the control device 802, and executes signal processing of a signal obtained in the SEM main body 801. The memory 805 stores the obtained image information or recipe information.),
acquire a second image in which centroid coordinates are displayed in the target region of the first image (Fig. 12, Paragraph [0115]- Yamaguchi discloses a centroid position 1202 of the guide pattern 1202 in the SEM image, a reference line 1203 calculated from the centroid position, and a mask pattern displayed in FIG. 11 or the formed pattern position are rewritten.),
generate reference grid coordinates from the centroid coordinates (Fig. 1, Paragraph [0070]- Yamaguchi discloses each pattern edge or the centroid of each pattern is aligned with the reference lines Further in Fig. 10, Paragraph [0110]- Yamaguchi discloses grid coordinates (Xn, Ym) on an image are assigned to each pattern. At this time, the Mask pattern used in the lithography is stored as another attribute.),
and calculate a shift value of the target region by using the centroid coordinates and the reference grid coordinates (Fig. 40 Paragraph [0140]- Yamaguchi discloses deviation from the neighboring intersection point among intersection points between the grid 4002 and centroids 4011 calculated from edge point detected in pattern measurement is measured again, and thus it is possible to statistically find out the deviation from the ideal position. Further in Fig. 34, Paragraph [0159]- Yamaguchi discloses a measurement pattern edge 3402 is detected from an SEM image, and a measurement pattern centroid 3403 is also calculated. A deviated amount 3405 is also calculated from a reference line X3406 and a reference line Y3407.).
Yamaguchi fails to explicitly teach in which a target region is classified, by applying a segmentation learning model to an image in which the target region is photographed.
However, Smith explicitly teaches in which a target region is classified, by applying a segmentation learning model to an image in which the target region is photographed (Fig. 1, Paragraph [0053]- Smith discloses the stored data may be accessible to a classification system that includes a classification model, neural network, or other machine learning algorithm. The classification system may classify each image (or sample associated with the image) as including a particular type of particle. For example, the classification system may analyze each image to classify or detect particles within the images.),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Yamaguchi of having an image processing device comprising: a memory configured to store instructions; and a processor configured to access the memory and execute the instructions, wherein, when executing the instructions, the processor is configured to: acquire a first image with the teachings of Smith in which a target region is classified, by applying a segmentation learning model to an image in which the target region is photographed.
Wherein having Yamaguchi’s system for pattern measurement wherein in which a target region is classified, by applying a segmentation learning model to an image in which the target region is photographed.
The motivation behind the modification would have been to improved accuracy of classification, since both Yamaguchi and Smith are both systems that determine locations of objects in image to determine offsets. Wherein Yamaguchi’s system wherein improved the manufacturing yield of a device, while Smith’s system improves accuracy and efficiency of identification and classification of objects of interest. Please see Yamaguchi et al. (US 20160320182 A1), Paragraph [0066] and Smith et al. (US 20230281819 A1) Paragraph [0005].
Regarding claim 2, Yamaguchi in view of Smith teaches the image processing device of claim 1, Yamaguchi further teaches wherein the processor is further configured to acquire the first image by using a plurality of scanning electron microscope (SEM) images obtained by photographing the target region at different locations based on a vertical direction (Fig. 25, Paragraph [0138]- Yamaguchi discloses FIG. 25 is a schematic diagram of a process in which self-aligned line patterns are formed in a horizontal direction and a vertical direction, and hole patterns are formed at portions at which the formed patterns respectively overlap space portions.).
Regarding claim 3, Yamaguchi in view of Smith teaches the image processing device of claim 1, Yamaguchi further teaches and generate the centroid coordinates for the bounding box (Fig. 12, Paragraph [0115]- Yamaguchi discloses a centroid position 1202 of the guide pattern 1202 in the SEM image, a reference line 1203 calculated from the centroid position, and a mask pattern displayed in FIG. 11 or the formed pattern position are rewritten (wherein the Guide patterns are seen as bounding boxes).).
Although Yamaguchi explicitly teaches wherein the processor is further configured to generate a bounding box for the target region (Fig. 12, Paragraph [0115]- Yamaguchi discloses a centroid position 1202 of the guide pattern 1202 in the SEM image, a reference line 1203 calculated from the centroid position, and a mask pattern displayed in FIG. 11 or the formed pattern position are rewritten (wherein the Guide patterns are seen as bounding boxes).).
Yamaguchi fails to explicitly teach wherein the processor is further configured to generate a bounding box for the target region by using an object detection algorithm.
However, Smith explicitly teaches wherein the processor is further configured to generate a bounding box for the target region by using an object detection algorithm (Fig. 3, Paragraph [0062]- Smith discloses the object detection pipeline utilizes YOLO to learn how to detect objects of interest given previously labelled objects the image processing algorithm has seen before. YOLO views the images as an S×S grid. Each grid cell learns to predict whether an objects exists within its cell, and if there is an object, the image processing algorithm defines a bounding box around the object and classifies the object.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Yamaguchi in view of Smith of having an image processing device comprising: a memory configured to store instructions; and a processor configured to access the memory and execute the instructions, wherein, when executing the instructions, the processor is configured to: acquire a first image with the teachings of Smith wherein the processor is further configured to generate a bounding box for the target region by using an object detection algorithm.
Wherein having Yamaguchi’s system for pattern measurement wherein the processor is further configured to generate a bounding box for the target region by using an object detection algorithm.
The motivation behind the modification would have been to improved accuracy of classification, since both Yamaguchi and Smith are both systems that determine locations of objects in image to determine offsets. Wherein Yamaguchi’s system wherein improved the manufacturing yield of a device, while Smith’s system improves accuracy and efficiency of identification and classification of objects of interest. Please see Yamaguchi et al. (US 20160320182 A1), Paragraph [0066] and Smith et al. (US 20230281819 A1) Paragraph [0005].
Regarding claim 4, Yamaguchi in view of Smith teaches the image processing device of claim 3, Yamaguchi further teaches wherein the bounding box is generated based on an area of the target region (Fig. 8, Paragraph [0082]- Yamaguchi discloses thus, in order to automatically select a pattern for position alignment in accordance with an evaluation purpose, the position alignment pattern measurement portion 812 selectively reads a region which is stored in the design data storage medium 814 and is created by SAxP, based on a measurement purpose or measurement target pattern information which is input by an input device 815. The read region is registered as an image for position alignment, in the position alignment processing portion 811.).
Regarding claim 5, Yamaguchi in view of Smith teaches the image processing device of claim 1, Yamaguchi further teaches wherein the processor is further configured to generate the reference grid coordinates at a location spaced apart from the centroid coordinates as much as a first horizontal distance (Fig. 28, Paragraph [0151]- Yamaguchi discloses in the grid setting portion 809, a pitch X2805 and a pitch Y2806 are calculated from a plurality of patterns. A reference line X2807 and a reference line Y2808 are calculated from the pitch information (wherein the Pitch X is seen as a first horizontal distance).).
Regarding claim 11, Yamaguchi teaches an image processing system comprising (Fig. 1, Paragraph [0011]- Yamaguchi discloses the pattern measurement device includes a computation device that measures dimensions between patterns formed in a sample, by using data which is obtained by irradiating the sample with a beam.):
a memory configured to store instructions (Fig. 8, #805 called a memory, paragraph [0078]- Yamaguchi discloses a computation processing unit 804 and a memory 805 are mounted in the computation processing device 803.);
a processor configured to access the memory and execute the instructions (Fig. 1, Paragraph #123 called an image processing processor [0091]- Yamaguchi discloses when grid measurement is executed, grid information which has been registered in advance is read from the storage device 1231 or the memory 805 to the image processing processor 123 or the computation processing device 803 (S601).);
and an observation device configured to acquire a scanning electron microscope (SEM) image obtained by photographing a plurality of target patterns (Fig. 8, Paragraph [0078]- Yamaguchi discloses the computation processing unit 804 supplies a predetermined control signal to the control device 802, and executes signal processing of a signal obtained in the SEM main body 801. The memory 805 stores the obtained image information or recipe information. Further in paragraph [0080]- Yamaguchi discloses in the pattern centroid computation portion 810, a centroid position (coordinate) of a pattern is extracted from pattern data obtained based on design data or simulation data, edge information of a pattern included in an SEM image, and contour line data of a pattern, which is extracted from pattern edge information.),
acquire a second image in which a plurality of centroid coordinates are displayed for each of the plurality of bounding boxes (Fig. 12, Paragraph [0115]- Yamaguchi discloses a centroid position 1202 of the guide pattern 1202 in the SEM image, a reference line 1203 calculated from the centroid position, and a mask pattern displayed in FIG. 11 or the formed pattern position are rewritten (wherein the Guide patterns are seen as bounding boxes).),
generate a plurality of reference grid coordinates from the plurality of centroid coordinates (Fig. 1, Paragraph [0070]- Yamaguchi discloses each pattern edge or the centroid of each pattern is aligned with the reference lines Further in Fig. 10, Paragraph [0110]- Yamaguchi discloses grid coordinates (Xn, Ym) on an image are assigned to each pattern. At this time, the Mask pattern used in the lithography is stored as another attribute.),
and calculate a shift value of each of the plurality of target patterns by using the plurality of centroid coordinates and the plurality of reference grid coordinates (Fig. 40 Paragraph [0140]- Yamaguchi discloses deviation from the neighboring intersection point among intersection points between the grid 4002 and centroids 4011 calculated from edge point detected in pattern measurement is measured again, and thus it is possible to statistically find out the deviation from the ideal position. Further in Fig. 34, Paragraph [0159]- Yamaguchi discloses a measurement pattern edge 3402 is detected from an SEM image, and a measurement pattern centroid 3403 is also calculated. A deviated amount 3405 is also calculated from a reference line X3406 and a reference line Y3407.).
Although Yamaguchi explicitly teaches generate a plurality of bounding boxes for each of the plurality of target regions (Fig. 12, Paragraph [0115]- Yamaguchi discloses a centroid position 1202 of the guide pattern 1202 in the SEM image, a reference line 1203 calculated from the centroid position, and a mask pattern displayed in FIG. 11 or the formed pattern position are rewritten (wherein the Guide patterns are seen as bounding boxes).).
Yamaguchi fails to explicitly teach wherein, when executing the instructions, the processor is configured to: acquire a first image, in which a plurality of target regions are classified, by applying a segmentation learning model to the SEM image, generate a plurality of bounding boxes for each of the plurality of target regions by using an object detection algorithm.
However, Smith explicitly teaches wherein, when executing the instructions, the processor is configured to: acquire a first image, in which a plurality of target regions are classified, by applying a segmentation learning model to the SEM image (Fig. 1, Paragraph [0053]- Smith discloses the stored data may be accessible to a classification system that includes a classification model, neural network, or other machine learning algorithm. The classification system may classify each image (or sample associated with the image) as including a particular type of particle. For example, the classification system may analyze each image to classify or detect particles within the images.),
generate a plurality of bounding boxes for each of the plurality of target regions by using an object detection algorithm (Fig. 3, Paragraph [0062]- Smith discloses the object detection pipeline utilizes YOLO to learn how to detect objects of interest given previously labelled objects the image processing algorithm has seen before. YOLO views the images as an S×S grid. Each grid cell learns to predict whether an objects exists within its cell, and if there is an object, the image processing algorithm defines a bounding box around the object and classifies the object.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Yamaguchi of having an image processing system comprising: a memory configured to store instructions; a processor configured to access the memory and execute the instructions; and an observation device configured to acquire a scanning electron microscope (SEM) image obtained by photographing a plurality of target patterns with the teachings of Smith wherein, when executing the instructions, the processor is configured to: acquire a first image, in which a plurality of target regions are classified, by applying a segmentation learning model to the SEM image, generate a plurality of bounding boxes for each of the plurality of target regions by using an object detection algorithm.
Wherein having Yamaguchi’s system for pattern measurement wherein, when executing the instructions, the processor is configured to: acquire a first image, in which a plurality of target regions are classified, by applying a segmentation learning model to the SEM image, generate a plurality of bounding boxes for each of the plurality of target regions by using an object detection algorithm.
The motivation behind the modification would have been to improved accuracy of classification, since both Yamaguchi and Smith are both systems that determine locations of objects in image to determine offsets. Wherein Yamaguchi’s system wherein improved the manufacturing yield of a device, while Smith’s system improves accuracy and efficiency of identification and classification of objects of interest. Please see Yamaguchi et al. (US 20160320182 A1), Paragraph [0066] and Smith et al. (US 20230281819 A1) Paragraph [0005].
Regarding claim 12, Yamaguchi in view of Smith teaches the image processing system of claim 11, Yamaguchi further teaches wherein the observation device is configured to acquire a plurality of SEM images by photographing the plurality of target patterns at different locations based on a vertical direction (Fig. 25, Paragraph [0138]- Yamaguchi discloses FIG. 25 is a schematic diagram of a process in which self-aligned line patterns are formed in a horizontal direction and a vertical direction, and hole patterns are formed at portions at which the formed patterns respectively overlap space portions.).
Regarding claim 13, Yamaguchi in view of Smith teaches the image processing system of claim 11, Yamaguchi fails to explicitly teach wherein the processor is further configured to exclude an edge region of the second image, which is other than the plurality of bounding boxes, from an application target of the object detection algorithm.
However, Smith explicitly teaches wherein the processor is further configured to exclude an edge region of the second image, which is other than the plurality of bounding boxes, from an application target of the object detection algorithm (Fig. 1, Paragraph [0079]- Smith discloses the image processing algorithm may further be trained to generate exclude boxes. Exclude boxes override background, foreground, and classification region objects. During training, if an exclude box contains the center of a foreground box, then the foreground box is treated as if it does not exist. Further, if a background box overlaps with an exclude box, then only the overlapping region of the background box is ignored. Exclude objects are segmented into intuitive classes. For example, blurry foreground objects should be in one exclude class, while foreground objects whose classification is not known should go into another exclude class.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Yamaguchi in view of Smith of having an image processing system comprising: a memory configured to store instructions; a processor configured to access the memory and execute the instructions; and an observation device configured to acquire a scanning electron microscope (SEM) image obtained by photographing a plurality of target patterns with the teachings of Smith wherein the processor is further configured to exclude an edge region of the second image, which is other than the plurality of bounding boxes, from an application target of the object detection algorithm.
Wherein having Yamaguchi’s system for pattern measurement wherein the processor is further configured to exclude an edge region of the second image, which is other than the plurality of bounding boxes, from an application target of the object detection algorithm.
The motivation behind the modification would have been to improved accuracy of classification, since both Yamaguchi and Smith are both systems that determine locations of objects in image to determine offsets. Wherein Yamaguchi’s system wherein improved the manufacturing yield of a device, while Smith’s system improves accuracy and efficiency of identification and classification of objects of interest. Please see Yamaguchi et al. (US 20160320182 A1), Paragraph [0066] and Smith et al. (US 20230281819 A1) Paragraph [0005].
Regarding claim 14, Yamaguchi in view of Smith teaches the image processing system of claim 11, Yamaguchi further teaches wherein the processor is further configured to generate first reference grid coordinates spaced apart from each other as much as a first distance in a first horizontal direction (Fig. 28, Paragraph [0151]- Yamaguchi discloses in the grid setting portion 809, a pitch X2805 and a pitch Y2806 are calculated from a plurality of patterns. A reference line X2807 and a reference line Y2808 are calculated from the pitch information (wherein the Pitch X is seen as a first horizontal distance).).
and second reference grid coordinates spaced apart from each other as much as the first distance in a second horizontal direction (Fig. 39, Paragraph [0166]- Yamaguchi discloses a reference line X3905 and a reference line Y3906 are calculated from a plurality of line centroids (wherein Fig. 39 shows multiple horizontal lines spaced a first distance apart coming from each of multiple centroids).),
based on first centroid coordinates of the plurality of centroid coordinates (Fig. 31, Paragraph [0155]- Yamaguchi discloses a pitch X3105 and a pitch Y3106 are calculated based on centroid positions of a plurality of patterns which are set in the above-described manner. A reference line X3107 and a reference line Y3108 are calculated from the pitch information.).
Regarding claim 19, Yamaguchi teaches an image processing device comprising (Fig. 1, Paragraph [0011]- Yamaguchi discloses the pattern measurement device includes a computation device that measures dimensions between patterns formed in a sample, by using data which is obtained by irradiating the sample with a beam.):
a memory configured to store instructions (Fig. 8, #805 called a memory, paragraph [0078]- Yamaguchi discloses a computation processing unit 804 and a memory 805 are mounted in the computation processing device 803.);
and a processor configured to access the memory and execute the instructions (Fig. 1, Paragraph #123 called an image processing processor [0091]- Yamaguchi discloses when grid measurement is executed, grid information which has been registered in advance is read from the storage device 1231 or the memory 805 to the image processing processor 123 or the computation processing device 803 (S601).),
wherein, when executing the instructions, the processor is configured to: acquire a first image (Fig. 8, Paragraph [0078]- Yamaguchi discloses the computation processing unit 804 supplies a predetermined control signal to the control device 802, and executes signal processing of a signal obtained in the SEM main body 801. The memory 805 stores the obtained image information or recipe information.),
acquire a second image in which a plurality of centroid coordinates are respectively displayed for each of the plurality of target regions (Fig. 12, Paragraph [0115]- Yamaguchi discloses a centroid position 1202 of the guide pattern 1202 in the SEM image, a reference line 1203 calculated from the centroid position, and a mask pattern displayed in FIG. 11 or the formed pattern position are rewritten.),
generate a plurality of reference grid coordinates spaced apart from each other as much as a first distance in first (Fig. 28, Paragraph [0151]- Yamaguchi discloses in the grid setting portion 809, a pitch X2805 and a pitch Y2806 are calculated from a plurality of patterns. A reference line X2807 and a reference line Y2808 are calculated from the pitch information (wherein the Pitch X is seen as a first horizontal distance).)
and second horizontal directions by using the plurality of centroid coordinates (Fig. 39, Paragraph [0166]- Yamaguchi discloses A reference line X3905 and a reference line Y3906 are calculated from a plurality of line centroids (wherein Fig. 39 shows multiple horizontal lines spaced a first distance apart coming from each of multiple centroids).),
and calculate a shift value of each of the plurality of target regions by using the plurality of centroid coordinates and the plurality of reference grid coordinates (Fig. 40 Paragraph [0140]- Yamaguchi discloses deviation from the neighboring intersection point among intersection points between the grid 4002 and centroids 4011 calculated from edge point detected in pattern measurement is measured again, and thus it is possible to statistically find out the deviation from the ideal position. Further in Fig. 34, Paragraph [0159]- Yamaguchi discloses a measurement pattern edge 3402 is detected from an SEM image, and a measurement pattern centroid 3403 is also calculated. A deviated amount 3405 is also calculated from a reference line X3406 and a reference line Y3407.).
Yamaguchi fails to explicitly teach in which a plurality of target regions are detected, by applying a segmentation learning model to a scanning electron microscope (SEM) image in which a target pattern is photographed.
However, Smith explicitly teaches in which a plurality of target regions are detected, by applying a segmentation learning model to a scanning electron microscope (SEM) image in which a target pattern is photographed (Fig. 1, Paragraph [0053]- Smith discloses the stored data may be accessible to a classification system that includes a classification model, neural network, or other machine learning algorithm. The classification system may classify each image (or sample associated with the image) as including a particular type of particle. For example, the classification system may analyze each image to classify or detect particles within the images. Further in Fig. 1, Paragraph [0030]- Smith discloses example scanners or imagers that may be used include a digital microscope, bright-field microscope, polarized imager, phase contrast image, fluorescence imager, scanning electron microscope, dark-field microscope, or other types of scanners/imagers.),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Yamaguchi of having an image processing device comprising: a memory configured to store instructions; and a processor configured to access the memory and execute the instructions, wherein, when executing the instructions, the processor is configured to: acquire a first image with the teachings of Smith in which a plurality of target regions are detected, by applying a segmentation learning model to a scanning electron microscope (SEM) image in which a target pattern is photographed.
Wherein having Yamaguchi’s system for pattern measurement wherein in which a plurality of target regions are detected, by applying a segmentation learning model to a scanning electron microscope (SEM) image in which a target pattern is photographed.
The motivation behind the modification would have been to improved accuracy of classification, since both Yamaguchi and Smith are both systems that determine locations of objects in image to determine offsets. Wherein Yamaguchi’s system wherein improved the manufacturing yield of a device, while Smith’s system improves accuracy and efficiency of identification and classification of objects of interest. Please see Yamaguchi et al. (US 20160320182 A1), Paragraph [0066] and Smith et al. (US 20230281819 A1) Paragraph [0005].
Claims 6-7 and 15 are rejected under 35 U.S.C 103 as being unpatentable over Yamaguchi et al. (US 20160320182 A1) hereafter referenced as Yamaguchi in view of Smith et al. (US 20230281819 A1) hereafter referenced as Smith and Yan et al. (US 20190325581 A1) hereafter referenced as Yan.
Regarding claim 6, Yamaguchi in view of Smith teaches the image processing device of claim 1, Although Yamaguchi explicitly teaches wherein the processor is further configured to generate a cluster number matched with the centroid coordinates (Fig. 10, Paragraph [0109]- Yamaguchi discloses in the example, Pattern No. is prepared as an Id of each of all patterns. Then, Group No. is assigned in order to distinguish between the DSA pattern and the reference pattern. Here, the reference pattern is assigned as Group 1, and the DSA pattern is assigned as Group 2. Regarding reference patterns (Pattern No. 1, 2, 6, and 7), one pattern is created for one guide pattern (wherein pattern no. is seen as cluster number).) Yamaguchi fails to explicitly teach wherein the processor is further configured to generate a cluster number matched with the centroid coordinates by using a k-means clustering algorithm.
However, Yan explicitly teaches wherein the processor is further configured to generate a cluster number matched with the centroid coordinates by using a k-means clustering algorithm (Fig. 9, Paragraph [0066]- Yan discloses the goal of this algorithm is to find color clusters in the image data, with the number of color clusters represented by the variable K. The color cluster engine 360 may use the K-Mean clustering algorithm to detect the centroids of major color clusters in sky images. Each centroid defines a corresponding color cluster.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Yamaguchi in view of Smith of having an image processing device comprising: a memory configured to store instructions; and a processor configured to access the memory and execute the instructions, wherein, when executing the instructions, the processor is configured to: acquire a first image with the teachings of Yan wherein the processor is further configured to generate a cluster number matched with the centroid coordinates by using a k-means clustering algorithm.
Wherein having Yamaguchi’s system for pattern measurement wherein the processor is further configured to generate a cluster number matched with the centroid coordinates by using a k-means clustering algorithm.
The motivation behind the modification would have been to improved accuracy of object detection, since both Yamaguchi and Yan are both systems that determine locations of objects using centroids in images. Wherein Yamaguchi’s system wherein improved the manufacturing yield of a device, while Yan’s system improves accuracy of objects detection. Please see Yamaguchi et al. (US 20160320182 A1), Paragraph [0066] and Yan et al. (US 20190325581 A1) Paragraph [0019].
Regarding claim 7, Yamaguchi in view of Smith and Yan teaches the image processing device of claim 6, Yamaguchi further teaches wherein the processor is further configured to generate the reference grid coordinates matched with the centroid coordinates by using the cluster number (Fig. 10, Paragraph [0109]- Yamaguchi discloses in the example, Pattern No. is prepared as an Id of each of all patterns. Then, Group No. is assigned in order to distinguish between the DSA pattern and the reference pattern. Here, the reference pattern is assigned as Group 1, and the DSA pattern is assigned as Group 2. Regarding reference patterns (Pattern No. 1, 2, 6, and 7), one pattern is created for one guide pattern (wherein pattern no. is seen as cluster number).).
Regarding claim 15, Yamaguchi in view of Smith teaches the image processing system of claim 11, Yamaguchi further teaches generates the plurality of reference grid coordinates matched with each of the plurality of centroid coordinates by using the plurality of cluster numbers (Fig. 10, Paragraph [0109]- Yamaguchi discloses in the example, Pattern No. is prepared as an Id of each of all patterns. Then, Group No. is assigned in order to distinguish between the DSA pattern and the reference pattern. Here, the reference pattern is assigned as Group 1, and the DSA pattern is assigned as Group 2. Regarding reference patterns (Pattern No. 1, 2, 6, and 7), one pattern is created for one guide pattern (wherein pattern no. is seen as cluster number).).
Although Yamaguchi explicitly teaches wherein the processor is further configured to generate a plurality of cluster numbers matched with each of the plurality of centroid coordinates (Fig. 10, Paragraph [0109]- Yamaguchi discloses in the example, Pattern No. is prepared as an Id of each of all patterns. Then, Group No. is assigned in order to distinguish between the DSA pattern and the reference pattern. Here, the reference pattern is assigned as Group 1, and the DSA pattern is assigned as Group 2. Regarding reference patterns (Pattern No. 1, 2, 6, and 7), one pattern is created for one guide pattern (wherein pattern no. is seen as cluster number).)
Yamaguchi fails to explicitly teach wherein the processor is further configured to generate a plurality of cluster numbers matched with each of the plurality of centroid coordinates by using a k-means clustering algorithm.
However, Yan explicitly teaches wherein the processor is further configured to generate a plurality of cluster numbers matched with each of the plurality of centroid coordinates by using a k-means clustering algorithm (Fig. 9, Paragraph [0066]- Yan discloses the goal of this algorithm is to find color clusters in the image data, with the number of color clusters represented by the variable K. The color cluster engine 360 may use the K-Mean clustering algorithm to detect the centroids of major color clusters in sky images. Each centroid defines a corresponding color cluster.),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Yamaguchi in view of Smith of having an image processing system comprising: a memory configured to store instructions; a processor configured to access the memory and execute the instructions; and an observation device configured to acquire a scanning electron microscope (SEM) image obtained by photographing a plurality of target patterns with the teachings of Smith wherein the processor is further configured to generate a plurality of cluster numbers matched with each of the plurality of centroid coordinates by using a k-means clustering algorithm.
Wherein having Yamaguchi’s system for pattern measurement wherein the processor is further configured to generate a plurality of cluster numbers matched with each of the plurality of centroid coordinates by using a k-means clustering algorithm.
The motivation behind the modification would have been to improved accuracy of object detection, since both Yamaguchi and Yan are both systems that determine locations of objects using centroids in images. Wherein Yamaguchi’s system wherein improved the manufacturing yield of a device, while Yan’s system improves accuracy of objects detection. Please see Yamaguchi et al. (US 20160320182 A1), Paragraph [0066] and Yan et al. (US 20190325581 A1) Paragraph [0019].
Claims 8-10, 16-18, and 20 are rejected under 35 U.S.C 103 as being unpatentable over Yamaguchi et al. (US 20160320182 A1) hereafter referenced as Yamaguchi in view of Smith et al. (US 20230281819 A1) hereafter referenced as Smith and Taylor et al. (US 20010033688 A1) hereafter referenced as Taylor.
Regarding claim 8, Yamaguchi in view of Smith teaches the image processing device of claim 1, Yamaguchi in view of Smith fails to explicitly teach wherein the processor is further configured to generate affine transformation centroid coordinates by performing affine transformation for the centroid coordinates.
However, Taylor explicitly teaches wherein the processor is further configured to generate affine transformation centroid coordinates by performing affine transformation for the centroid coordinates (Fig. 1, Paragraph [0042]- Taylor discloses then, the calculated affine transform is preferably applied to all of the anchor regions that have been selected, thereby producing transformed locations for each. Further in Fig. 12a Paragraph [0044]- Taylor discloses turning first to FIG. 12A, as an initial step four or more centroids 1220 are selected which preferably correspond to distinct features of the image. For each of these centroids 1220, an offset distance is calculated, preferably as described hereinafter, between the feature on the target form 10 and the same feature on the blank form 20. Thus, the result of this computation is an X and Y offset that is to be separately applied to each of the centroids 1220 in order to bring it into alignment.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Yamaguchi in view of Smith of having an image processing device comprising: a memory configured to store instructions; and a processor configured to access the memory and execute the instructions, wherein, when executing the instructions, the processor is configured to: acquire a first image with the teachings of Taylor wherein the processor is further configured to generate affine transformation centroid coordinates by performing affine transformation for the centroid coordinates.
Wherein having Yamaguchi’s system for pattern measurement wherein the processor is further configured to generate affine transformation centroid coordinates by performing affine transformation for the centroid coordinates.
The motivation behind the modification would have been to improved accuracy of orienting an object, since both Yamaguchi and Taylor are both systems that are used to detect and correct alignment errors. Wherein Yamaguchi’s system wherein improved the manufacturing yield of a device, while Taylor’s system improves accuracy of orientation and positioning of an object. Please see Yamaguchi et al. (US 20160320182 A1), Paragraph [0066] and Taylor et al. (US 20010033688 A1) Paragraph [0035].
Regarding claim 9, Yamaguchi in view of Smith and Taylor teaches the image processing device of claim 8, Yamaguchi in view of Smith fails to explicitly teach wherein the processor is further configured to acquire a third image by using the reference grid coordinates and the affine transformation centroid coordinates.
However, Taylor explicitly teaches wherein the processor is further configured to acquire a third image by using the reference grid coordinates and the affine transformation centroid coordinates (Fig. 12A Paragraph [0044]- Taylor discloses for each of these centroids 1220, an offset distance is calculated, preferably as described hereinafter, between the feature on the target form 10 and the same feature on the blank form 20. Thus, the result of this computation is an X and Y offset that is to be separately applied to each of the centroids 1220 in order to bring it into alignment (wherein the alignment from the X and Y offset would create the third image).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Yamaguchi in view of Smith of having an image processing device comprising: a memory configured to store instructions; and a processor configured to access the memory and execute the instructions, wherein, when executing the instructions, the processor is configured to: acquire a first image with the teachings of Taylor wherein the processor is further configured to acquire a third image by using the reference grid coordinates and the affine transformation centroid coordinates.
Wherein having Yamaguchi’s system for pattern measurement wherein the processor is further configured to acquire a third image by using the reference grid coordinates and the affine transformation centroid coordinates.
The motivation behind the modification would have been to improved accuracy of orienting an object, since both Yamaguchi and Taylor are both systems that are used to detect and correct alignment errors. Wherein Yamaguchi’s system wherein improved the manufacturing yield of a device, while Taylor’s system improves accuracy of orientation and positioning of an object. Please see Yamaguchi et al. (US 20160320182 A1), Paragraph [0066] and Taylor et al. (US 20010033688 A1) Paragraph [0035].
Regarding claim 10, Yamaguchi in view of Smith and Taylor teaches the image processing device of claim 8, Yamaguchi in view of Smith fails to explicitly teach wherein the processor is further configured to calculate a skew value of the target region by using the affine transformation centroid coordinates and the reference grid coordinates.
However, Taylor explicitly teaches wherein the processor is further configured to calculate a skew value of the target region by using the affine transformation centroid coordinates and the reference grid coordinates (Fig. 1, Paragraph [0042]- Taylor discloses then, the calculated affine transform is preferably applied to all of the anchor regions that have been selected, thereby producing transformed locations for each. A "transformed offset" is then computed for each of the transformed locations which represents the distance (after application of that particular affine transform) between the corresponding regions on the two forms.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Yamaguchi in view of Smith of having an image processing device comprising: a memory configured to store instructions; and a processor configured to access the memory and execute the instructions, with the teachings of Taylor wherein the processor is further configured to calculate a skew value of the target region by using the affine transformation centroid coordinates and the reference grid coordinates.
Wherein having Yamaguchi’s system for pattern measurement wherein the processor is further configured to calculate a skew value of the target region by using the affine transformation centroid coordinates and the reference grid coordinates.
The motivation behind the modification would have been to improved accuracy of orienting an object, since both Yamaguchi and Taylor are both systems that are used to detect and correct alignment errors. Wherein Yamaguchi’s system wherein improved the manufacturing yield of a device, while Taylor’s system improves accuracy of orientation and positioning of an object. Please see Yamaguchi et al. (US 20160320182 A1), Paragraph [0066] and Taylor et al. (US 20010033688 A1) Paragraph [0035].
Regarding claim 16, Yamaguchi in view of Smith teaches the image processing system of claim 11, Yamaguchi in view of Smith fails to explicitly teach wherein the processor is further configured to generate affine transformation centroid coordinates by performing affine transformation for the centroid coordinates.
However, Taylor explicitly teaches wherein the processor is further configured to generate affine transformation centroid coordinates by performing affine transformation for the centroid coordinates (Fig. 1, Paragraph [0042]- Taylor discloses then, the calculated affine transform is preferably applied to all of the anchor regions that have been selected, thereby producing transformed locations for each. Further in Fig. 12a Paragraph [0044]- Taylor discloses turning first to FIG. 12A, as an initial step four or more centroids 1220 are selected which preferably correspond to distinct features of the image. For each of these centroids 1220, an offset distance is calculated, preferably as described hereinafter, between the feature on the target form 10 and the same feature on the blank form 20. Thus, the result of this computation is an X and Y offset that is to be separately applied to each of the centroids 1220 in order to bring it into alignment.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Yamaguchi in view of Smith of having an image processing system comprising: a memory configured to store instructions; a processor configured to access the memory and execute the instructions; and an observation device configured to acquire a scanning electron microscope (SEM) image obtained by photographing a plurality of target patterns with the teachings of Taylor wherein the processor is further configured to generate affine transformation centroid coordinates by performing affine transformation for the centroid coordinates.
Wherein having Yamaguchi’s system for pattern measurement wherein the processor is further configured to generate affine transformation centroid coordinates by performing affine transformation for the centroid coordinates.
The motivation behind the modification would have been to improved accuracy of orienting an object, since both Yamaguchi and Taylor are both systems that are used to detect and correct alignment errors. Wherein Yamaguchi’s system wherein improved the manufacturing yield of a device, while Taylor’s system improves accuracy of orientation and positioning of an object. Please see Yamaguchi et al. (US 20160320182 A1), Paragraph [0066] and Taylor et al. (US 20010033688 A1) Paragraph [0035].
Regarding claim 17, Yamaguchi in view of Smith and Taylor teaches the image processing system of claim 16, Yamaguchi in view of Smith fails to explicitly teach wherein the processor is further configured to calculate the shift value of each of the plurality of target patterns by using outer point coordinates of the plurality of reference grid coordinates and the affine transformation centroid coordinates.
However, Taylor explicitly teaches wherein the processor is further configured to calculate the shift value of each of the plurality of target patterns by using outer point coordinates of the plurality of reference grid coordinates and the affine transformation centroid coordinates (Fig. 1, Paragraph [0042]- Taylor discloses then, the calculated affine transform is preferably applied to all of the anchor regions that have been selected, thereby producing transformed locations for each. A "transformed offset" is then computed for each of the transformed locations which represents the distance (after application of that particular affine transform) between the corresponding regions on the two forms.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Yamaguchi in view of Smith of having an image processing system comprising: a memory configured to store instructions; a processor configured to access the memory and execute the instructions; and an observation device configured to acquire a scanning electron microscope (SEM) image obtained by photographing a plurality of target patterns with the teachings of Taylor wherein the processor is further configured to calculate the shift value of each of the plurality of target patterns by using outer point coordinates of the plurality of reference grid coordinates and the affine transformation centroid coordinates.
Wherein having Yamaguchi’s system for pattern measurement wherein the processor is further configured to calculate the shift value of each of the plurality of target patterns by using outer point coordinates of the plurality of reference grid coordinates and the affine transformation centroid coordinates.
The motivation behind the modification would have been to improved accuracy of orienting an object, since both Yamaguchi and Taylor are both systems that are used to detect and correct alignment errors. Wherein Yamaguchi’s system wherein improved the manufacturing yield of a device, while Taylor’s system improves accuracy of orientation and positioning of an object. Please see Yamaguchi et al. (US 20160320182 A1), Paragraph [0066] and Taylor et al. (US 20010033688 A1) Paragraph [0035].
Regarding claim 18, Yamaguchi in view of Smith and Taylor teaches the image processing system of claim 16, Yamaguchi in view of Smith fails to explicitly teach wherein the processor is further configured to respectively calculate skew values of the plurality of target patterns by using the affine transformation centroid coordinates and the plurality of reference grid coordinates.
However, Taylor explicitly teaches wherein the processor is further configured to respectively calculate skew values of the plurality of target patterns by using the affine transformation centroid coordinates and the plurality of reference grid coordinates (Fig. 1, Paragraph [0042]- Taylor discloses then, the calculated affine transform is preferably applied to all of the anchor regions that have been selected, thereby producing transformed locations for each. A "transformed offset" is then computed for each of the transformed locations which represents the distance (after application of that particular affine transform) between the corresponding regions on the two forms.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Yamaguchi in view of Smith of having an image processing system comprising: a memory configured to store instructions; a processor configured to access the memory and execute the instructions; and an observation device configured to acquire a scanning electron microscope (SEM) image obtained by photographing a plurality of target patterns with the teachings of Taylor wherein the processor is further configured to respectively calculate skew values of the plurality of target patterns by using the affine transformation centroid coordinates and the plurality of reference grid coordinates.
Wherein having Yamaguchi’s system for pattern measurement wherein the processor is further configured to respectively calculate skew values of the plurality of target patterns by using the affine transformation centroid coordinates and the plurality of reference grid coordinates.
The motivation behind the modification would have been to improved accuracy of orienting an object, since both Yamaguchi and Taylor are both systems that are used to detect and correct alignment errors. Wherein Yamaguchi’s system wherein improved the manufacturing yield of a device, while Taylor’s system improves accuracy of orientation and positioning of an object. Please see Yamaguchi et al. (US 20160320182 A1), Paragraph [0066] and Taylor et al. (US 20010033688 A1) Paragraph [0035].
Regarding claim 20, Yamaguchi in view of Smith teaches the image processing device of claim 19, Yamaguchi in view of Smith fails to explicitly teach wherein the processor is further configured to: generate a plurality of affine transformation centroid coordinates by performing affine transformation for the plurality of centroid coordinates, and calculate the shift value of each of the plurality of target regions by using outer point coordinates of the plurality of reference grid coordinates and the plurality of affine transformation centroid coordinates.
However, Taylor explicitly teaches wherein the processor is further configured to: generate a plurality of affine transformation centroid coordinates by performing affine transformation for the plurality of centroid coordinates (Fig. 1, Paragraph [0042]- Taylor discloses then, the calculated affine transform is preferably applied to all of the anchor regions that have been selected, thereby producing transformed locations for each. Further in Fig. 12a Paragraph [0044]- Taylor discloses turning first to FIG. 12A, as an initial step four or more centroids 1220 are selected which preferably correspond to distinct features of the image. For each of these centroids 1220, an offset distance is calculated, preferably as described hereinafter, between the feature on the target form 10 and the same feature on the blank form 20. Thus, the result of this computation is an X and Y offset that is to be separately applied to each of the centroids 1220 in order to bring it into alignment.),
and calculate the shift value of each of the plurality of target regions by using outer point coordinates of the plurality of reference grid coordinates and the plurality of affine transformation centroid coordinates (Fig. 1, Paragraph [0042]- Taylor discloses then, the calculated affine transform is preferably applied to all of the anchor regions that have been selected, thereby producing transformed locations for each. A "transformed offset" is then computed for each of the transformed locations which represents the distance (after application of that particular affine transform) between the corresponding regions on the two forms.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Yamaguchi in view of Smith of having an image processing device comprising: a memory configured to store instructions; and a processor configured to access the memory and execute the instructions, wherein, when executing the instructions, the processor is configured to: acquire a first image with the teachings of Taylor wherein the processor is further configured to: generate a plurality of affine transformation centroid coordinates by performing affine transformation for the plurality of centroid coordinates, and calculate the shift value of each of the plurality of target regions by using outer point coordinates of the plurality of reference grid coordinates and the plurality of affine transformation centroid coordinates.
Wherein having Yamaguchi’s system for pattern measurement wherein the processor is further configured to: generate a plurality of affine transformation centroid coordinates by performing affine transformation for the plurality of centroid coordinates, and calculate the shift value of each of the plurality of target regions by using outer point coordinates of the plurality of reference grid coordinates and the plurality of affine transformation centroid coordinates.
The motivation behind the modification would have been to improved accuracy of orienting an object, since both Yamaguchi and Taylor are both systems that are used to detect and correct alignment errors. Wherein Yamaguchi’s system wherein improved the manufacturing yield of a device, while Taylor’s system improves accuracy of orientation and positioning of an object. Please see Yamaguchi et al. (US 20160320182 A1), Paragraph [0066] and Taylor et al. (US 20010033688 A1) Paragraph [0035].
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered
pertinent to applicant`s disclosure.
Wang et al. (US 11567413 B2)- A method for determining measurement data of a printed pattern on a substrate. The method involves obtaining (i) images of the substrate including a printed pattern corresponding to a reference pattern, (ii) an averaged image of the images, and (iii) a composite contour based on the averaged image. Further, the composite contour is aligned with respect to a reference contour of the reference pattern and contours are extracted from the images based on both the aligned composite contour and the output of die-to-database alignment of the composite contour. Further, the method determines a plurality of pattern measurements based on the contours and the measurement data corresponding to the printed patterns based on the plurality of the pattern measurements. Further, the method determines a one or more process variations such as stochastic variation, inter-die variation, intra-die variation and/or total variation.....................Please see Fig. 1. Abstract.
Chu et al. (US 20200356718 A1)- Techniques are presented for the application of neural networks to the fabrication of integrated circuits and electronic devices, where example are given for the fabrication of non-volatile memory circuits and the mounting of circuit components on the printed circuit board of a solid state drive (SSD). The techniques include the generation of high precision masks suitable for analyzing electron microscope images of feature of integrated circuits and of handling the training of the neural network when the available training data set is sparse through use of a generative adversary network (GAN).....................Please see Fig. 1. Abstract.
Ryu et al. (US 20250005350 A1)- A method and system for passenger propensity classification for purpose-built vehicles perform operations including: generating time series data by collecting driving propensity data of passengers, who use the purpose-built vehicles, in a time order; outputting a plurality of new features which are an intermediate result by reducing a dimension of the time series data by training the time series data by an artificial neural network; and classifying driving propensities of passengers by applying a K-means clustering algorithm to the new features generated by reducing the dimension of the time series data.....................Please see Fig. 1. Abstract.
BRODERICK et al. (US 20240264084 A1)- Disclosed herein are substrates for surface-enhanced Raman spectroscopy (SERS), methods of fabrication of the same using soft and nanoparticle lithography or laser-induced nano structuring of thin films (LINST), and their use to characterize extracellular vesicles (EVs) from a range of sources including but not limited to cancers, bacteria, viruses and/or placental cells. Also disclosed are machine learning methods for classifying and/or identifying SERS spectra from particles including EVs, the machine learning methods including bottleneck classifiers or layers configured to reduce the dimension of the network. In further methods the bottleneck classifier is combined with an autoencoder in either a supervised or unsupervised manner to identify of classify SERs spectra features.....................Please see Fig. 1. Abstract.
Amanullah et al. (US 20160125583 A1)- A skeleton wafer inspection system includes an expansion table displaceable relative to a camera configured for capturing segmental images of a skeleton wafer on a film frame. During segmental image capture, illumination is directed to the top and/or bottom of the film frame. Segmental images are digitally stitched together to pro duce a composite image, which can be processed to identify die presence or absence therein at active area die positions having counterpart die positions in a process wafer map. A composite image of a diced wafer on a film frame can also be generated, and used as a navigation aid or guide during die sort operations, or to verify whether a die sort apparatus has correctly detected a reference die prior to die sort operations. A composite image of a skeleton wafer can similarly be generated for use as a navigation aid or guide for film frame repopulation operations......................Please see Fig. 1. Abstract.
Yang et al. (US 20120275722 A1)- A computer implemented method for evaluating a one-to-one mapping between a first spatial point set and a second spatial point set in nD comprising the steps of receiving a first and a second spatial point sets and a one-to-one mapping between the two spatial point sets; defining a spatial agent; generating multiple mapped (n+1)-combinations in the first point set; computing multiple affine transformations that transform the multiple mapped (n+1)-combinations to correspondents in the second point set; applying the multiple affine transformations to the spatial agent to generate multiple transformed spatial agents; and computing a distance measure using the multiple transformed spatial agents.......................Please see Fig. 1. Abstract.
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/LUCIUS CAMERON GREEN ALLEN/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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