DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 16-34 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 1223013. Although the claims at issue are not identical, they are not patentably distinct from each other because claims of instant application are anticipated by claims of U.S. Patent No. 1223013 as shown below.
Instant claim 16 <= 013: claim 1
Instant claim 17 <= 013: claim 16
Instant claim 18 <= 013: claim 17
Instant claim 19 <= 013: claims 1, 2
Instant claim 20 <= 013: claim 3
Instant claim 21 <= 013: claim 4
Instant claim 22 <= 013: claim 5
Instant claim 23 <= 013: claim 6
Instant claim 24 <= 013: claim 1
Instant claim 25 <= 013: claim 9
Instant claim 26 <= 013: claim 10
Instant claim 27 <= 013: claim 13
Instant claim 28 <= 013: claim 14
Instant claim 29 <= 013: claim 15
Instant claim 30 <= 013: claims 1, 15
Instant claim 31 <= 013: claim 18
Instant claim 32 <= 013: claim 19
Instant claim 33 <= 013: claims 19, 18
Instant claim 34 <= 013: claim 20
Claim Rejections - 35 USC § 103
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 nonobviousness.
Claim(s) 16-19, and 22-34 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kotaki (U.S. PG-PUB NO. 2013/0010100) in view of Brauer (U.S. PG-PUB NO. 2018/0075594).
-Regarding claim 16, Kotaki discloses an electron beam inspection apparatus (FIG. 1), comprising: a memory (memory 117, FIG. 1); and at least one processor coupled to the memory and configured to execute instructions to cause the electron beam inspection apparatus to perform operations (CPU 116, FIG. 1) comprising: obtaining a first image of a pattern at a plurality of training locations, the first image having a first quality for each of a plurality of training locations (extracting, from an image of a semiconductor circuit pattern photographed and acquired by a SEM, a plurality of areas (similar areas) having similarly shaped patterns, [0074]; low S/N and low resolution, [0103]); obtaining a second image of the pattern at the plurality of training locations, the second image having a second quality higher than the first quality (taking an arithmetic mean of upsampled images to generate one image with an enhanced resolution, [0075]); and modifying an obtained image of the pattern to generate a modified image (high resolution images that have been enhanced in resolution by the image restoration processing, [0083]).
Kotaki is silent to teaching that using the first image and the second image of the pattern to train a machine learning model; and using the trained machine learning model. However, the claimed limitation is well known in the art as evidenced by Brauer.
In the same field of endeavor, Brauer teaches using the first image and the second image of the pattern to train a machine learning model (training 113, using the processor, for each secondary scanning mode, a CNN, [0045]); and using the trained machine learning model (applying each CNN to the corresponding second image subset, [0061]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Kotaki with the teaching of Brauer in order to automate Kotaki’s image-quality enhancement and reduce operator to operator variation (applying known technique (CNN) to improve a similar SEM image-enhancement device with predictable results (KSR)).
-Regarding claim 17, the combination further discloses a quality of the modified image is higher than a quality of the obtained image (Kotaki, high resolution images that have been enhanced in resolution by the image restoration processing, [0083]).
-Regarding claim 18, the combination further discloses the quality of the modified image comprises one or more of a resolution, a contrast, a sensitivity, a brightness, or a noise level (Kotaki, resolution, [0074]; brightness, noise, pattern edge signal profile, [0082]).
-Regarding claim 19, the combination further discloses the operations further comprise analyzing data associated with the pattern at the training location (Kotaki, design data on semiconductor circuit patterns is taken in (step 901), [0097]; Brauer, design data, a die context map generated offline using design layout, [0029]) and stored in a database (Kotaki, store the SEM image in a database 118, [0072]).
-Regarding claim 22, the combination further discloses the operations further comprise obtaining a plurality of first images having the first quality for each of the plurality of training locations and using the plurality of first images to train the machine learning model (Kotaki, executing the image restoration processing using the image data of the plurality of extracted similar areas, [0074]; Brauer, [0045]).
-Regarding claim 23, the combination further discloses the operations further comprise obtaining a plurality of second images having the second quality for each of the plurality of training locations and using the plurality of second images to train the machine learning model (Kotaki, acquire a plurality of high resolution hole pattern images 1404-1 to 1404-4 by dividing a group of hole pattern areas, [0103]; Brauer, one or more secondary scanning mode images corresponding to a selected defect of interest or nuisance data, [0041]; train, for each secondary scanning mode, a CNN with the corresponding first image subset, [0066]).
-Regarding claim 24, the combination further discloses the operations further comprise analyzing the pattern relating to a layout of a product and identifying the plurality of training locations based on the analyzed pattern (Kotaki, a group of areas having patterns similar to the reference pattern is extracted from within the imaging field of view that was re-set using the design data (step 907), [0098]).
-Regarding claim 25, the combination further discloses analyzing the pattern comprises extracting a feature from the pattern (Kotaki, the reference template and the similar pattern categorized region are compared for pattern matching to extract a group of areas similar to the reference template (similar area group), [0078]).
-Regarding claim 26, the combination further discloses the extracted feature comprises one of a shape, a size, a density, or a neighborhood layout (Kotaki, a plurality of areas (similar areas) having similarly shaped patterns, [0074]).
-Regarding claim 27, the combination further discloses obtaining the first image comprises determining a first scanning path including a first scan for obtaining the first image, the first scanning path based on an overall scan area for the plurality of training locations (Kotaki, a SEM imaging position and imaging range (imaging field of view) including the evaluation coordinates are set (step 904), [0097]).
-Regarding claim 28, the combination further discloses obtaining the second image comprises determining a second scanning path including a second scan for obtaining the second image, the second scanning path based on an overall scan area for the plurality of training locations (Kotaki, the imaging field of view is re-set so that it will contain as many areas having similar patterns in shape to the reference pattern 1103-1 (similar areas) as possible, [0098]).
-Regarding claim 29, the combination further discloses the first scan includes a first number of scans, the second scan includes a second number of scans, and wherein the second number of scans is larger than the first number of scans (Kotaki, The imaging conditions to be set may include the imaging range and the number of frames to be added, [0097]; Brauer, Multiple scans may be performed 107, [0040]. Kotaki's settable "number of frames" and Brauer's multiple scans establish differing scan counts; using a larger frame/scan count for the higher-quality second image is the known SEM quality/noise tradeoff. It is obvious (KSR- known problem/known solution, predictable noise reduction) to acquire the higher -quality image with more scans/frames than the lower-quality image).
-Regarding claim 30, the combination further discloses the first image and the second image comprise a secondary electron microscopy (SEM) image (Kotaki, SEM images, [0071], [0074]).
-Regarding claim 31, the combination further discloses the machine learning model is configured to generate the modified image to approximate an image produced by performing a higher number of scans than a number of scans used to obtain the obtained image (Kotaki, even when the number of images taken of the semiconductor circuit pattern is small, making it possible to enhance the resolution of the semiconductor pattern image, [0074]; Brauer, training 113, using the processor, for each secondary scanning mode, a CNN, [0045]. Kotaki produces from few images a result approximating a higher-scan-count image; under the combination the trained CNN (Brauer) generates that approximation. Applying Brauer's trained model to Kotaki's few-scan-to-high-resolution enhancement predictably yields an image approximating more scans (KSR: known solution, predictable result)).
-Regarding claim 32, Kotaki discloses a non-transitory computer readable medium storing a set of instructions (memory 117, FIG. 1) that is executable by a controller of a device to cause the device to perform operations (CPU 116, FIG. 1) comprising: obtaining a first image of a pattern at a plurality of training locations, the first image having a first quality for each of the plurality of training locations (extracting, from an image of a semiconductor circuit pattern photographed and acquired by a SEM, a plurality of areas (similar areas) having similarly shaped patterns, [0074]; low S/N and low resolution, [0103]); obtaining a second image of the pattern at the plurality of training locations, the second image having a second quality higher than the first quality for each of the plurality of training locations (taking an arithmetic mean of upsampled images to generate one image with an enhanced resolution, [0075]); and modifying an obtained image of the pattern to generate a modified image (high resolution images that have been enhanced in resolution by the image restoration processing, [0083]).
Kotaki is silent to teaching that using the first image and the second image of the pattern to train a machine learning model; and using the trained machine learning model. However, the claimed limitation is well known in the art as evidenced by Brauer.
In the same field of endeavor, Brauer teaches using the first image and the second image of the pattern to train a machine learning model (training 113, using the processor, for each secondary scanning mode, a CNN, [0045]); and using the trained machine learning model (applying each CNN to the corresponding second image subset, [0061]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Kotaki with the teaching of Brauer in order to automate Kotaki’s image-quality enhancement and reduce operator to operator variation (applying known technique (CNN) to improve a similar SEM image-enhancement device with predictable results (KSR)).
-Regarding claim 33, the combination further discloses the machine learning model is configured to generate the modified image to approximate an image produced by performing a higher number of scans than a number of scans used to obtain the obtained image (Kotaki, even when the number of images taken of the semiconductor circuit pattern is small, making it possible to enhance the resolution of the semiconductor pattern image, [0074]; Brauer, training 113, using the processor, for each secondary scanning mode, a CNN, [0045]. Kotaki produces from few images a result approximating a higher-scan-count image; under the combination the trained CNN (Brauer) generates that approximation. Applying Brauer's trained model to Kotaki's few-scan-to-high-resolution enhancement predictably yields an image approximating more scans (KSR: known solution, predictable result)).
-Regarding claim 34, Kotaki discloses a method for inspecting a semiconductor wafer (FIG. 1), comprising: obtaining a first image of a pattern at a plurality of training locations, the first image having a first quality for each of the plurality of training locations (extracting, from an image of a semiconductor circuit pattern photographed and acquired by a SEM, a plurality of areas (similar areas) having similarly shaped patterns, [0074]; low S/N and low resolution, [0103]); obtaining a second image of the pattern at the plurality of training locations, the second image having a second quality higher than the first quality for each of the plurality of training locations (taking an arithmetic mean of upsampled images to generate one image with an enhanced resolution, [0075]); modifying an obtained image of the pattern to generate a modified image; and performing an inspection of the semiconductor wafer based on the modified obtained image (high resolution images that have been enhanced in resolution by the image restoration processing, [0083]).
Kotaki is silent to teaching that using the first image and the second image of the pattern to train a machine learning model; and using the trained machine learning model. However, the claimed limitation is well known in the art as evidenced by Brauer.
In the same field of endeavor, Brauer teaches using the first image and the second image of the pattern to train a machine learning model (training 113, using the processor, for each secondary scanning mode, a CNN, [0045]); and using the trained machine learning model (applying each CNN to the corresponding second image subset, [0061]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Kotaki with the teaching of Brauer in order to automate Kotaki’s image-quality enhancement and reduce operator to operator variation (applying known technique (CNN) to improve a similar SEM image-enhancement device with predictable results (KSR)).
Claim(s) 20-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kotaki (U.S. PG-PUB NO. 2013/0010100) in view of Brauer (U.S. PG-PUB NO. 2018/0075594) and further in view of Chen (U.S. PG-PUB NO. 2021/0342994).
-Regarding claim 20, the combination is silent to teaching that the database comprises one of a graphic database system (GDS), an Open Artwork System Interchange Standard, or a Caltech Intermediate Form. However, the claimed limitation is well known in the art as evidenced by Chen.
In the same field of endeavor, Chen teaches the database comprises one of a graphic database system (GDS), an Open Artwork System Interchange Standard, or a Caltech Intermediate Form (GDS, [0019]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of the combination with the teaching of Chen in order to obtain accurate results, alignment is important in a measured, tested, monitored and diagnosed semiconductor wafer.
-Regarding claim 21, the combination further discloses the GDS comprises at least one of GDS formatted data or GDSII formatted data (Chen, GDS file or a GDSII file, [0016]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PING Y HSIEH whose telephone number is (571)270-3011. The examiner can normally be reached Monday-Friday, 9am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Mehmood can be reached at (571) 272-2976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PING Y HSIEH/Primary Examiner, Art Unit 2664
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