Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This communication is in response to the action filed on 03/04/2026.
Claims 1, 9, and 12 are currently amended. Claims 1-19 are currently pending.
Response to Arguments
Applicant’s arguments filed on 03/04/2026 on pages 7-10, under REMARKS with respect to 35 U.S.C. 103 have been fully considered but they are not persuasive. Regarding claim 1 applicants on page 9 states that:
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The examiner respectfully disagrees. The examiner would like to point out a few particular sections of the primary reference of record US 2018/0330511 A1 to Ha et al. (hereinafter “HA”), specifically paragraphs [0073-0075] and states in paragraph [0075]: “the first and second modalities generate the first and second images with different, distortions of patterned features formed on the specimen. For example, the patterned features in the first and second images may be distorted differently, and in the case of CAD to scanning electron microscope (SEM) alignment, some CAD layers may be missing from the SEM images to which they are being aligned. In another example, a CAD image might have substantially little patterned feature distortion, while a SEM image may have more patterned feature distortion, and an optical image might have the most patterned feature distortion. However, the differences between the amount of distortion in different images of the same patterned features may vary from that described above as well. In this manner, the embodiments described herein
provide a robust learning-based approach to accurately align images across varying length scales, frequency spreads, different structures, and large shape distortions”. Which can clearly be mapped as the rendered images are the first and second image modalities generate the first and second images with different distortions (modifications of a different degree or amount) of patterned features formed on the specimen the patterned features (characteristic of the topology) in the first and second images (rendered images) may be distorted differently (to a different degree). For example, in the case of CAD to scanning electron microscope SEM alignment, a CAD image might have substantially little patterned feature distortion, while a SEM image may have more patterned feature distortion, and an optical image might have the most patterned feature distortion, the differences between the amount (degree) of distortion in different images of the same patterned features is not limited to the example described in HA, but provides a robust learning based approach to accurately align images across varying length scales, frequency spreads, different structures, and large shape distortions (which provide various characteristics of topology). The examiner would argue that the amended limitations recite a phrase “a degree of the modification based on characteristics of the topology” versus the allowed limitations recite “a degree of blurring is based on a characteristic of the topology” wherein the term blurring further narrows the claims 16-19 to being allowable as cited in the reasons for allowance section seen in the allowable subject matter section of this rejection. However, since the term modification which is used in amended claims 1 and 12 can map and means substantially the same as distortion which is a modification of a type but is not specifically blurring the existing combination reads on the independent claims 1, and 12 as currently amended. The examiner recommends amending claims 1 and 12 to include the blurring limitation of claim 16. Please see full rejection to the claims below.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-4, 10, 12-14 are rejected under 35 § U.S.C. 103 as being obvious over US 2018/0330511 A1 to Ha et al. (hereinafter “HA”) in view of US 2005/0198602 A1 to BRANKNER (hereinafter “BRANKNER”).
As per claim 1, HA discloses an image alignment method (a computing system adapted to perform an image alignment method by capturing a image of a specimen (sample) of a semiconductor wafer as a SEM image 200; fig 1-2; paragraphs [0081], [0085-0086]) comprising: obtaining an image of a sample (the system is adapted to perform the method of capturing an image of a semiconductor wafer specimen acting as the sample; fig 1-2; paragraphs [0081-0082], [0085-0089]); obtaining information associated with a corresponding reference image (reference image 202 makes a rendered modified image 212, if you look at 200, 202, 212 the layout (topology is preserved); fig 2; paragraphs [0010], [0085-0086]); generating a modified rendered image by modifying a rendered image of the corresponding reference image such that a topology of the rendered image is substantially preserved (generating (render) a CAD image 202 from the SEM image 200, using a learning based alignment algorithm 204 and can learn alignment rules it will be able to invert these rules to remove most of the differences between images acquired with different modalities ( e.g., SEM and CAD) and thus make the alignment task much easier and generates image 212 and the rendered image is 210 of reference image 202, such that topology of rendered image 210 is preserved, and 210 generates color overlay 212, it is seen in figure 2 that the topology (which is defined as layout or shape) is substantially preserved on the various images 200, 202, and 212; figs 1-2; paragraphs [0011], [0081-0089], [0139]) wherein a degree of the modification is based on a characteristic of the topology of the rendered image (the first and second modalities generate the first and second images with different distortions (modifications of a different degree or amount) of patterned features formed on the specimen the patterned features (characteristic of the topology) in the first and second images (rendered images) may be distorted differently, and in the case of CAD to scanning electron microscope SEM alignment, for example a CAD image might have substantially little patterned feature distortion, while a SEM image may have more patterned feature distortion, and an optical image might have the most patterned feature distortion, the differences between the amount (degree) of distortion in different images of the same patterned features is not limited to the example described in HA, but provides a robust learning based approach to accurately align images across varying length scales, frequency spreads, different structures, and large shape distortions; paragraphs [0073-0075]). HA fails to disclose and aligning the image of the sample with the modified rendered image.
BRANKNER discloses and aligning the image of the sample with the modified rendered image (at step 404 the system is adapted to perform the method of aligning the image of the detected anomaly (image of sample) with the design layout of the image of the detected anomaly (rendered image); figure 4; paragraphs [0409]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify HA to have aligning the image of the sample with the modified rendered image of BRANKNER reference. The Suggestion/motivation for doing so would have been to provide the ability to align the captured image with the design layout of the captured image by pattern matching which includes matching each vector of the captured image with each vector of the design layout of the image. If there is a sufficient match between the vectors of the captured image with the vectors of the design layout of the image, then the captured image may be aligned with the design layout of the image, ensuring an accurate alignment as suggested by BRANKNER paragraph [0049]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine BRANKNER with HA to obtain the invention as specified in claim 1.
As per claim 2, HA in view of BRANKNER discloses the method of claim 1. Modified HA further discloses further comprising aligning the image of the sample with the corresponding reference image based on an alignment between the image of the sample and the modified rendered image (learning based alignment 204 renders the first and second images into a common space to perform an alignment and generate alignment results 206 the alignment results produced by the learning based alignment along with SEM image 200 and CAD 202 may be input to crop and generate overlay color images 212; fig 2; paragraph [0085-0088]).
As per claim 3, HA in view of BRANKNER discloses the method of claim 1. Modified HA further discloses further comprising modifying the rendered image based on a characteristic of the topology of the rendered image (the rendered image (generated CAD image from the SEM images) is generated based on surface features such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc which describe the topology of a semiconductor component; paragraphs [0075], [0091], [0120-0122]; note the google definition of “topology as used in the semiconductor industry refers to the arrangement and interconnection of the components within an electronic circuit, especially within an integrated circuit (IC)).
As per claim 4, HA in view of BRANKNER discloses the method of claim 3. Modified HA further discloses wherein the characteristic of the topology comprises a size, a shape, an orientation, or a boundary of a feature, or a spacing between adjacent features of the rendered image (patterned feature (characteristic of topology) that renders the generated image different from other patterned features in the same image and in other images used for testing, examples of such characteristics include, but are not limited to, size, shape, orientation, density, proximity to other features, and number of features; paragraphs [0075], [0091], [0131]).
As per claim 10, HA in view of BRANKNER discloses the method of claim 1. Modified HA further discloses wherein the image of the sample comprises a charged-particle beam image, an optical image, or a digital image (optical and electron beam tools are used to capture optical images of the specimens; paragraphs [0064], [0075]).
As per claim 12, HA discloses an image alignment system comprising (a computing system adapted to perform an image alignment method by capturing a image of a specimen (sample) of a semiconductor wafer as a SEM image 200; fig 1-2; paragraphs [0081], [0085-0086]): a memory storing a set of instructions (a computing system adapted to perform an image alignment method the computing system comprising a computing processor and memory to store and execute instructions respectively related to the image processing method; fig 9; paragraphs [0049-0050], [0147-0149]); and a processor configured to execute the set of instructions to cause the image alignment system to (the computing system comprising a computing processor and memory to store and execute instructions respectively related to the image processing method; fig 9; paragraphs [0049-0050], [0147-0149]): obtain an image of a sample (the system is adapted to perform the method of capturing an image of a semiconductor wafer specimen acting as the sample; fig 1-2; paragraphs [0081-0082], [0085-0089]); obtain information associated with a corresponding reference image (reference image 202 makes a rendered modified image 212, if you look at 200, 202, 212 the layout (topology is preserved); fig 2; paragraphs [0010], [0085-0086]); generate a modified rendered image by modifying a rendered image of the corresponding reference image such that a topology of the rendered image is substantially preserved (generating (render) a CAD image 202 from the SEM image 200, using a learning based alignment algorithm 204 and can learn alignment rules it will be able to invert these rules to remove most of the differences between images acquired with different modalities ( e.g., SEM and CAD) and thus make the alignment task much easier and generates image 212 and the rendered image is 210 of reference image 202, such that topology of rendered image 210 is preserved, and 210 generates color overlay 212, it is seen in figure 2 that the topology (which is defined as layout or shape) is substantially preserved on the various images 200, 202, and 212; figs 1-2; paragraphs [0011], [0081-0089], [0139]) wherein a degree of the modification is based on a characteristic of the topology of the rendered image (the first and second modalities generate the first and second images with different distortions (modifications of a different degree or amount) of patterned features formed on the specimen the patterned features (characteristic of the topology) in the first and second images (rendered images) may be distorted differently, and in the case of CAD to scanning electron microscope SEM alignment, for example a CAD image might have substantially little patterned feature distortion, while a SEM image may have more patterned feature distortion, and an optical image might have the most patterned feature distortion, the differences between the amount (degree) of distortion in different images of the same patterned features is not limited to the example described in HA, but provides a robust learning based approach to accurately align images across varying length scales, frequency spreads, different structures, and large shape distortions; paragraphs [0073-0075]). HA fails to disclose and align the image of the sample with the modified rendered image.
BRANKNER discloses and align the image of the sample with the modified rendered image (at step 404 the system is adapted to perform the method of aligning the image of the detected anomaly (image of sample) with the design layout of the image of the detected anomaly (rendered image); figure 4; paragraphs [0409]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify HA to have align the image of the sample with the modified rendered image of BRANKNER reference. The Suggestion/motivation for doing so would have been to provide the ability to align the captured image with the design layout of the captured image by pattern matching which includes matching each vector of the captured image with each vector of the design layout of the image. If there is a sufficient match between the vectors of the captured image with the vectors of the design layout of the image, then the captured image may be aligned with the design layout of the image, ensuring an accurate alignment as suggested by BRANKNER paragraph [0049]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine BRANKNER with HA to obtain the invention as specified in claim 12.
As per claim 13, HA in view of BRANKNER discloses the system of claim 12. Modified HA further discloses wherein the set of instructions further causes the image alignment system to align the image of the sample with the corresponding reference image based on an alignment between the image of the sample and the modified rendered image (learning based alignment 204 renders the first and second images into a common space to perform an alignment and generate alignment results 206 the alignment results produced by the learning based alignment along with SEM image 200 and CAD 202 may be input to crop and generate overlay color images 212; fig 2; paragraph [0085-0088]).
As per claim 14, HA in view of BRANKNER discloses the system of claim 12. Modified HA further discloses wherein the set of instructions further causes the image alignment system to modify the rendered image based on a characteristic of the topology of the rendered image (the rendered image (generated CAD image from the SEM images) is generated based on surface features such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc which describe the topology of a semiconductor component; paragraphs [0075], [0091], [0120-0122]; note the google definition of “topology as used in the semiconductor industry refers to the arrangement and interconnection of the components within an electronic circuit, especially within an integrated circuit (IC)), and wherein the characteristic of the topology comprises a size, a shape, an orientation, or a boundary of a feature, or a spacing between adjacent features of the rendered image (patterned feature (characteristic of topology) that renders the generated image different from other patterned features in the same image and in other images used for testing, examples of such characteristics include, but are not limited to, size, shape, orientation, density, proximity to other features, and number of features; paragraphs [0075], [0091], [0131]).
Claims 5-6, 9, 15 are rejected under 35 § U.S.C. 103 as being obvious over US 2018/0330511 A1 to Ha et al. (hereinafter “HA”) in view of US 2005/0198602 A1 to BRANKNER (hereinafter “BRANKNER”) in view of US 2008/0298719 A1 to SENGUPTA et al. (hereinafter “SENGUPTA”).
As per claim 5, HA in view of BRANKNER discloses the method of claim 1. HA fails to disclose wherein modifying the rendered image comprises down sampling the rendered image based on a down sampling scale, the down sampling scale based on a characteristic of the topology.
SENGUPTA discloses wherein modifying the rendered image comprises down sampling the rendered image based on a down sampling scale, the down sampling scale based on a characteristic of the topology (the alignment system 100 aligns features in a higher-resolution reference image, e.g., a CAD image of an integrated circuit 175, with corresponding features (characteristics) in a lower-resolution (down sampled) acquired image, e.g., an optical image of the IC 175; fig 1; paragraphs [0035-0041]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify HA to have wherein modifying the rendered image comprises down sampling the rendered image based on a down sampling scale of SENGUPTA reference. The Suggestion/motivation for doing so would have been to provide the ability to probe via electron beam device a specific circuit element of the IC as suggested by SENGUPTA at paragraph [0040]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine SENGUPTA with modified HA to obtain the invention as specified in claim 5.
As per claim 6, HA in view of BRANKNER in view of SENGUPTA discloses the method of claim 5. HA fails to disclose wherein down sampling the rendered image is based on a maximum value of the down sampling scale that substantially preserves the topology of the rendered image.
SENGUPTA discloses wherein down sampling the rendered image is based on a maximum value of the down sampling scale that substantially preserves the topology of the rendered image (the image alignment system includes a maximum calculator 140 adapted to maximizes an offset value that maximizes the correlation of optical and CAD images of the IC 175 in order to preserve feature of the IC; paragraphs [0035-0041]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify HA to have wherein down sampling the rendered image is based on a maximum value of the down sampling scale that substantially preserves the topology of the rendered image of SENGUPTA reference. The Suggestion/motivation for doing so would have been to provide the ability to probe more effectively and accurately via electron beam device a specific circuit element of the IC as suggested by SENGUPTA at paragraph [0040]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine SENGUPTA with modified HA to obtain the invention as specified in claim 6.
As per claim 9, HA in view of BRANKNER in view of SENGUPTA discloses the method of claim 5. Modified HA fails to disclose wherein down sampling the rendered image of the corresponding reference image causes blurring of the rendered image.
SENGUPTA discloses wherein down sampling the rendered image of the corresponding reference image causes blurring of the rendered image (the alignment system 100 aligns features in a higher-resolution reference image, e.g., a CAD image of an integrated circuit 175, with corresponding features (characteristics) in a lower-resolution (down sampled) acquired image, e.g., an optical image of the IC 175 wherein the optical image of lower resolution would appear “blurrier” than the rendered CAD image; fig 1; paragraphs [0035-0041]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify HA to have wherein down sampling the reference image causes blurring of the rendered image of SENGUPTA reference. The Suggestion/motivation for doing so would have been to provide the ability to probe more effectively and accurately via electron beam device a specific circuit element of the IC as suggested by SENGUPTA at paragraph [0040]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine SENGUPTA with modified HA to obtain the invention as specified in claim 9.
As per claim 15, HA in view of BRANKNER discloses the system of claim 14. Modified HA fails to disclose wherein the set of instructions further cause the image alignment system to down sample the rendered image based on a down sampling scale, the down sampling scale based on the characteristic of the topology, and wherein down sampling is based on a maximum value of the down sampling scale that substantially preserves the topology of the rendered image.
SENGUPTA discloses wherein the set of instructions further cause the image alignment system to down sample the rendered image based on a down sampling scale, the down sampling scale based on the characteristic of the topology (the alignment system 100 aligns features in a higher-resolution reference image, e.g., a CAD image of an integrated circuit 175, with corresponding features (characteristics) in a lower-resolution (down sampled) acquired image, e.g., an optical image of the IC 175; fig 1; paragraphs [0035-0041]), and wherein down sampling is based on a maximum value of the down sampling scale that substantially preserves the topology of the rendered image (the image alignment system includes a maximum calculator 140 adapted to maximizes an offset value that maximizes the correlation of optical and CAD images of the IC 175 in order to preserve feature of the IC; paragraphs [0035-0041]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify HA to have wherein the set of instructions further cause the image alignment system to down sample the rendered image based on a down sampling scale of SENGUPTA reference. The Suggestion/motivation for doing so would have been to provide the ability to probe more effectively and accurately via electron beam device a specific circuit element of the IC as suggested by SENGUPTA at paragraph [0040]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine SENGUPTA with modified HA to obtain the invention as specified in claim 15.
Claim 11 is rejected under 35 § U.S.C. 103 as being obvious over US 2018/0330511 A1 to Ha et al. (hereinafter “HA”) in view of US 2005/0198602 A1 to BRANKNER (hereinafter “BRANKNER”) in view of US 2007/0230770 A1 to KULKARNI et al. (hereinafter “KULKARNI”).
As per claim 11, HA in view of BRANKNER discloses the method of claim 1. Modified HA fails to disclose wherein the information associated with the reference image comprises information in Graphic Database System (GOS) format, Graphic Database System II (GDS II) format, or an Open Artwork System Interchange Standard (OASIS) format.
KULKARNI discloses wherein the information associated with the reference image comprises information in Graphic Database System (GOS) format, Graphic Database System II (GDS II) format, or an Open Artwork System Interchange Standard (OASIS) format (data preparation phase may include creating or acquiring design data reflecting the physical design layout of a device being fabricated on a wafer or to be fabricated on the wafer ( e.g., information obtained from a data structure such as a graphical data stream (GDS) file, GDSII file, or another standard file or database); paragraphs [0081], [0106]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify HA to have wherein the information associated with the reference image comprises information in GDSII format of KULKARNI reference. The Suggestion/motivation for doing so would have been to provide device fabrication files in a readable format, and further include off-line alignment of a CAD simulated image or a GDSII clip to optical or electron beam images of the predetermined alignment sites to determine mapping of device features as suggested by KULKARNI at paragraph [0106]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine KULKARNI with modified HA to obtain the invention as specified in claim 11.
Claims 7 and 8 are rejected under 35 § U.S.C. 103 as being obvious over US 2018/0330511 A1 to Ha et al. (hereinafter “HA”) in view of US 2005/0198602 A1 to BRANKNER (hereinafter “BRANKNER”) in view of US 2008/0298719 A1 to SENGUPTA et al. (hereinafter “SENGUPTA”) in view of US 8,203,570 B1 to PIPONI (hereinafter “PIPONI”).
As per claim 7, HA in view of BRANKNER in view of SENGUPTA discloses the method of claim 5. Modified HA fails to disclose wherein down sampling the rendered image is performed using a convex blurring function.
PIPONI discloses wherein down sampling the rendered image is performed using a convex blurring function (FIG. 2, illustrations of two exemplary convex integral polygons are presented that would be used as polygon kernels for processing, blurring images, one polygon 200 is represented in the first quadrant of a Cartesian coordinate system with corresponding x and y coordinates, in this example polygon 200 has a hexagonal shape, in other arrangements, other convex polygon shapes, rectangle, triangle, diamond-shaped, etc. may be used as a kernel; fig 2; column 3, lines 33-41).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify HA to have wherein down sampling the rendered image is performed using a convex blurring function of PIPONI reference. The Suggestion/motivation for doing so would have been to provide the ability to use multiple shapes for the convex blurring step performed via a convex polygon of a variety of shapes and provides the ability to use various shapes based on the image and which shapes may be best suited for the particular task as suggested by PIPONI at column 3, lines 33-41. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine PIPONI with modified HA to obtain the invention as specified in claim 7.
As per claim 8 HA in view of BRANKNER in view of SENGUPTA in further view of PIPONI discloses the method of claim 7. Modified HA fails to disclose wherein the convex blurring function comprises an ellipse function.
PIPONI discloses wherein the convex blurring function comprises an ellipse function (in this example polygon 200 has a hexagonal shape, in other arrangements, other convex polygon shapes, rectangle, triangle, diamond-shaped, etc. may be used as a kernel it would easy based on the language cited that the user would use an elliptical shape or a diamond shape which is substantially similar to an elliptical shape and produces ellipse like results as per the visual results displayed in figure 1; figs 1- 2; column 3, lines 33-41).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify HA to have wherein the convex blurring function comprises an ellipse function of PIPONI reference. The Suggestion/motivation for doing so would have been to provide the ability to use multiple shapes for the convex blurring step performed via a convex polygon of a variety of shapes and provides the ability to use various shapes based on the image and which shapes may be best suited for the particular task as suggested by PIPONI at column 3, lines 33-41. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to further combine PIPONI with Modified HA to obtain the invention as specified in claim 8.
Allowable Subject Matter
The claims 16-19 allowed.
The following is an examiners statement of reason for allowance:
The cited art of record, and the primary art of record US 2018/0330511 A1 to Ha et al. (hereinafter “HA”) discloses an image alignment system comprising: a memory storing a set of instructions (a computing system adapted to perform an image alignment method the computing system comprising a computing processor and memory to store and execute instructions respectively related to the image processing method; fig 9; paragraphs [0049-0050], [0147-0149]); and a processor configured to execute the set of instructions to cause the image alignment system to (the computing system comprising a computing processor and memory to store and execute instructions respectively related to the image processing method; fig 9; paragraphs [0049-0050], [0147-0149]): obtain an image of a sample (the computing system adapted to capture an image of a specimen (sample) of a semiconductor wafer as a SEM image 200; paragraphs [0010], [0047], [0085]); obtain information associated with a corresponding reference image (feature maps are generated based on obtained features (obtaining information) is performed by aligning different images generated for a specimen to each other by using alignment features in the images and on the specimen and aligning the different images to a common reference/design); paragraphs [0010], [0085-0086]).
The second closest prior art of record US 2005/0198602 A1 to BRANKNER (hereinafter “BRANKNER”) discloses and aligning the image of the sample with the modified rendered image (at step 404 the system is adapted to perform the method of aligning the image of the detected anomaly (image of sample) with the design layout of the image of the detected anomaly (rendered image); figure 4; paragraphs [0409]).
The third closest prior art of record US 8,203,570 B1 to PIPONI (hereinafter “PIPONI”) discloses wherein down sampling the rendered image is performed using a convex blurring function (FIG. 2, illustrations of two exemplary convex integral polygons are presented that would be used as polygon kernels for processing, blurring images, one polygon 200 is represented in the first quadrant of a Cartesian coordinate system with corresponding x and y coordinates, in this example polygon 200 has a hexagonal shape, in other arrangements, other convex polygon shapes, rectangle, triangle, diamond-shaped, etc. may be used as a kernel; fig 2; column 3, lines 33-41).
Further, the cited arts of the record fail to disclose the limitations/features of “generate a modified rendered image by blurring a rendered image of the corresponding reference image such that a topology of the rendered image is substantially preserved, wherein a degree of blurring is based on a characteristic of the topology”, as recited in claim 16. The cited arts of record including HA, BRANKNER, and PIPONI taken solo or in combination fail to disclose the claimed limitations when taken in combination with the claimed invention as a whole. Particularly, the prior arts fail to describe or disclose specifically wherein the degree of blurring is based on a characteristic of topology is not disclosed by a simple blurring function of PIPONI and would not make sense to combine with HA in view of BRANKNER to cover claim 16 as written. Dependent claims 17-19 are allowed for the same reasons as the corresponding independent claim.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Examiner's Note: Examiner has cited figures, and paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested for the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Examiner has also cited references in PTO892 but not relied on, which are relevant and pertinent to the applicant’s disclosure, and may also be reading (anticipatory/obvious) on the claims and claimed limitations. Applicant is advised to consider the references in preparing the response/amendments in-order to expedite the prosecution.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. These prior arts include the following:
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEVIN JACOB DHOOGE whose telephone number is (571) 270-0999. The examiner can normally be reached 7:30-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached on (571) 270-5183. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800- 786-9199 (IN USA OR CANADA) or 571-272-1000.
/Devin Dhooge/
USPTO Patent Examiner
Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
Prosecution Insights