DETAILED ACTION
[1] Remarks
I. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
II. Claims 1-11 are pending and have been examined, where claims 1-4, 6 and 9-11 is/are rejected, claim 5 and 7-8 is/are objected to. Explanations will be provided below.
III. Inventor and/or assignee search were performed and determined no double patenting rejection(s) is/are necessary.
IV. Patent eligibility (updated in 2019) shown by the following: Claims 1-11 pass patent eligibility test because there is/are no limitation or a combination of limitations amounting to an abstract idea. Also, the following limitation or the combinations of the limitations: “a determination part that determines, for each of the plurality of divided images, whether or not determination as to whether the inspection target object is good or defective can be made by using rule information, and determines, for a first divided image for which determination as to whether the inspection target object is good or defective can be made by using the rule information among the plurality of divided images” effects a transformation or a reduction of a particular article to a different state or thing / adds a specific limitation(s) other than what is well-understood, routine and conventional in the field, or adding unconventional steps that confine the claim to a particular useful application and providing improvements to the technical field of semiconductor manufacturing defect detection, which recite additional elements that integrate the judicial exception into a practical application and amounting significant more.
[2] 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.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function. Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
Claim(s) 1-10 are interpreted under 35 U.S.C. 112(f) or pre-AIA U.S.C. 112 6th paragraph because of the following reason(s): the claim limitations uses the term “means” or a term used as a substitute for “means” that is a generic placeholder; the term “means” or the generic placeholder is modified by functional language, typically linked by the transition word “for” or another linking word or phrase, such as “configured to” or “so that”; the term “means” or the generic placeholder is not modified by sufficient structure or material for performing the claimed function.
Claim(s) 11 are not interpreted under 35 U.S.C. 112(f) or pre-AIA U.S.C. 112 6th paragraph because of the following reason(s): limitations are modified by sufficient structure or material for performing the claimed function.
Upon examination of the specification and claims, the examiner has determined, under the best understanding of the scope of the claim(s), rejection(s) under 35 U.S.C. 112(a)/(b) is not necessitated because of the following reasons: sufficient support are provided in the written description / drawings of the invention.
[3] Grounds of Rejection
Claim Rejections - 35 USC § 103
1. 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.
2. Claims 1-4, 6, and 9-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kurihara (US 20080067371) in view of Kwon (US 20190188840).
Regarding claim 1, Kurihara discloses an image processing device comprising:
an acquisition part that acquires an original image showing an inspection target object (see paragraph 32, scanning deflectors 107, 1 is the inspected circuit);
a division part that generates a plurality of divided images by dividing the original image based on a feature of the inspection target object shown in the original image (see figure 6, S22, the die is divided into cells);
a determination part that determines, for each of the plurality of divided images, whether or not determination as to whether the inspection target object is good or defective can be made by using rule information (see figure 6, S23 determine whether defect detection is possible, paragraph 35, detail analyzer 915 calculates electron beam image feature quantities of the defects to be reviewed, that is, shapes, brightness, grayscale levels, texture, and other factors, from associated defect position information in a coordinate system of the defect-reviewing apparatus where shapes, brightness, grayscale levels, texture are the rule information), and
determines, for a first divided image for which determination as to whether the inspection target object is good or defective can be made by using the rule information among the plurality of divided images, whether the inspection target object is good or defective by using the rule information (see figure 6, S25 all defect detection operations completed, where S25 is performed use cell by cell, which is the first divided image, where the rule information are shapes, brightness, grayscale levels, texture are the rule information).
Kurihara is silent in disclosing an output part outputs a second divided image among the plurality of divided images to a learning module capable of machine learning or obtained as a learning result, the second divided image cannot be determined whether the inspection target object is good or defective by using the rule information.
Kwon discloses an output part outputs a second divided image among the plurality of divided images to a learning module capable of machine learning or obtained as a learning result, the second divided image cannot be determined whether the inspection target object is good or defective by using the rule information (see figure 10, S210 are a collection of image, where the first image is read as the first image, second image is read as the second image, third image read as third image, etc., where the collection of images are divided into first, second, third images, etc. if the imager is offset then the defect is not present, see paragraph 34, automatic defect review device 40 may be configured to review whether a defect is present in semiconductor patterns of the wafer, 110 performs whether there is a defect in the aligned image or not).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include output part outputs a second divided image among the plurality of divided images to a learning module capable of machine learning or obtained as a learning result to isolate only the specific of the image containing a region of interest, e.g., an anomaly, localized defect, or tumor boundary and the machine learning model focuses on feature extraction, which optimize efficiency and filter out irrelevant data.
Regarding claim 2, Kurihara discloses the image processing device according to Claim 1, wherein the output part outputs the second divided image to a display device to cause the display device to display the second divided image (see figure 4 and figure 5, the second divided image is a comparison between the current cell and a template cell are displayed):
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Regarding claim 3, Kwon discloses the image processing device according to Claim 2, further comprising a reception part that receives a determination result as to whether the second divided image is good or defective, wherein the output part outputs the second divided image and a determination result as to whether the inspection target object in the second divided image received by the reception part is good or defective to the learning module so as to cause the learning module to perform machine learning (see figure 10, S210 are a collection of image, where the first image is read as the first image, second image is read as the second image, third image read as third image, etc., where the collection of images are divided into first, second, third images, etc. if the imager is offset then the defect is not present, see paragraph 34, automatic defect review device 40 may be configured to review whether a defect is present in semiconductor patterns of the wafer, 110 performs whether there is a defect in the aligned image or not, wherein the classification result is output to the defect image database, where database receives the determination results). See the motivation for claim 1. In addition, the database receiving the determination results in order to retrieve said results in a later without rerunning the same algorithm to save processing time.
Regarding claim 4, Kurihara discloses the image processing device according to Claim 1, further comprising a link part that stores the plurality of divided images and a determination result as to whether the inspection target object in the plurality of divided images is good or defective in a storage in association with each other (see paragraph 35, the defect position information being stored within the image memory 916, and from the electron beam defect image formed above at the second magnification, where the second magnification is read as the second divided image and the defect position information indicates a determination result … stores the detailed analytical information as electron beam defect review results for each semiconductor wafer into a database 115, see figure 1, where the network is a link connecting the inspection apparatus to the electron microscope).
Regarding claim 6, Kurihara discloses the image processing device according to Claim 1, further comprising the learning module obtained as a learning result, wherein the output part outputs a determination result as to whether the inspection target object in the second divided image is good or defective by the learning module to a display device (see paragraph 35, detail analyzer 915 acquires detailed analytical information, category information and classification information of the defects, from the electron beam image feature quantities, where this detail analyzer includes learning module which perform classification task, see figure 4 and figure 5, the second divided image is a comparison between the current cell and a template cell shows the second divided image which is displays).
Regarding claim 9, Kurihara discloses the image processing device according to claim 1, wherein the output part outputs a determination result of the inspection target object in the first divided image by the determination part to a display device (see figure 10 illustration below, the first divided image is displayed):
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Regarding claim 10, Kurihara discloses the image processing device according to claim 6, wherein the output part outputs a determination result of the inspection target object in the first divided image by the determination part to the display device (see figure 10, the defect determination results are shown on the display, shown above).
Regarding claim 11 see the rationale and rejection for claim 1.
[4] Claim Objections
Claim(s) 5 and 7-8 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
With regards to claim 5, the examiner cannot find any applicable prior art providing teachings for the following limitation(s): “the image processing device according to Claim 4, wherein the link part stores, in the storage, the same number of divided images for which a determination result as to whether the inspection target object is good or defective is good and divided images for which a determination result as to whether the inspection target object is good or defective is defective among the plurality of divided images”; in combination with the rest of the limitations of claims 1 and 4.
Kurihara discloses the image processing device according to Claim 4, wherein the link part stores, in the storage, stored within the image memory 916, and from the electron beam defect image formed above at the second magnification, where the second magnification is read as the second divided image and the defect position information indicates a determination result … stores the detailed analytical information as electron beam defect review results for each semiconductor wafer into a database 115, see figure 1, where the network is a link connecting the inspection apparatus to the electron microscope).
Regarding claim 7, the examiner cannot find any applicable prior art providing teachings for the following limitation(s): “the image processing device according to Claim 6, further comprising: a link part that stores a defective image for which the inspection target object is determined to be defective by the determination part or the learning module among the plurality of divided images in a storage in association with a determination result indicating that the inspection target object is defective; and a reception part that receives a determination result as to whether the inspection target object in the defective image is good or defective, wherein the output part outputs the defective image to the display device, and in a case where a determination result as to whether the inspection target object in the defective image is good or defective received by the reception part is good, the link part changes a determination result stored in the storage in association with the defective image to a determination result indicating good” in combination with the rest of the limitations of claims 1 and 6.
Kurihara discloses the image processing device according to Claim 6, further comprising: a link part that stores a defective image for which the inspection target object is determined to be defective by the determination part or stored within the image memory 916, and from the electron beam defect image formed above at the second magnification, where the second magnification is read as the second divided image and the defect position information indicates a determination result … stores the detailed analytical information as electron beam defect review results for each semiconductor wafer into a database 115, see figure 1, where the network is a link connecting the inspection apparatus to the electron microscope, also see figure 2, S26 indicates all defects are determined).
Kwon discloses a reception part that receives a determination result as to whether the inspection target object in the defective image is good or defective, wherein the output part outputs the defective image to the display device, and in a case where a determination result as to whether the inspection target object in the defective image is good or defective received by the reception part is good (see figure 10, S210 are a collection of image, where the first image is read as the first image, second image is read as the second image, third image read as third image, etc., where the collection of images are divided into first, second, third images, etc. if the imager is offset then the defect is not present, see paragraph 34, automatic defect review device 40 may be configured to review whether a defect is present in semiconductor patterns of the wafer, 110 performs whether there is a defect in the aligned image or not, wherein the classification result is output to the defect image database, where database receives the determination results), the link part changes a learning model classification device 110 may be updated by the machine learning capability that uses the images stored in the defect image database 70, where the link is the connection between 110 and 70, and the updating steps is read on changes).
Regarding claim 8, the examiner cannot find any applicable prior art providing teachings for the following limitation(s): the image processing device according to Claim 1, further comprising a generator that classifies the plurality of divided images based on a feature of the inspection target object, and generates a plurality of inspection images by combining one or more divided images classified into a same group among the plurality of divided images, wherein the determination part determines, for each of the plurality of inspection images, whether or not determination as to whether the inspection target object is good or defective can be made by using the rule information; in combination with the rest of the limitations of claim 1.
PARK (US 20170003230) discloses dividing optical element for dividing the wafer image delivered from the lens portion; … an image processing portion for combining the images on different focus positions captured by the plurality of image pick-up elements to form a TSOM image (see abstract), but is silent in disclosing generates a plurality of inspection images by combining one or more divided images classified into a same group among the plurality of divided images, wherein the determination part determines, for each of the plurality of inspection images, whether or not determination as to whether the inspection target object is good or defective can be made by using the rule information.
CONTACT INFORMATION
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEX LIEW (duty station is located in New York City) whose telephone number is (571)272-8623 (FAX 571-273-8623), cell (917)763-1192 or email alexa.liew@uspto.gov. Please note the examiner cannot reply through email unless an internet communication authorization is provided by the applicant. The examiner can be reached anytime.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MISTRY ONEAL R, can be reached on (313)446-4912. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALEX KOK S LIEW/Primary Examiner, Art Unit 2674 Telephone: 571-272-8623
Date: 9/28/26
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