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 .
Information Disclosure Statement
1. The information disclosure statement (IDS) submitted on 09/10/2024 has been considered by the examiner.
Double Patenting
2. 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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3. Claims 1, 4, 7, 9, 13, 15 and 18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 7-8, 15 and 18 of U.S. Patent No. 12,020,417 B2 (herein referred to as Buzaglo). Although the claims at issue are not identical, they are not patentably distinct from each other because
Regarding claim 1, claim 1 has been analyzed and rejected as per claim 7 of Buzaglo (claim 7 includes the subject matter of claim 7 and 1; and the citations have been made both in claim 1 and 7).
Regarding claim 1, Buzaglo discloses A method comprising:
obtaining one or more images of a defect located on a die of a semiconductor wafer (claim 1 – col. 12, lines 66-67;
applying a plurality of prediction models on the one or more images to obtain a plurality of classification decisions of the defect, each of the plurality of prediction models is configured to classify defects into one or more defect classes (claim 1 – col. 13, lines 11-20);
obtaining metrology information of the defect and utilizing the metrology information in combination with the plurality of classification decisions to determine a combined classification decision of the defect (claim 7 – col. 13, lines 38-42); and
outputting the combined classification decision (claim 1 – col. 13, line 21).
Regarding claim 4, claim 4 has been analyzed and rejected as per claim 8 of Buzaglo.
Regarding claim 7, claim 7 has been analyzed and rejected as per claim 1 of Buzaglo (col. 13, lines 1-4).
Regarding claim 9, claim 9 has been analyzed and rejected as per claim 1 of Buzaglo (col. 13, lines 1-4).
Regarding claim 13, claim 13 has been similarly analyzed and rejected as per claim 7; and further can also be rejected as per claim 18.
Regarding claim 15, claim 15 has been analyzed and rejected as per claim 8 of Buzaglo.
Regarding claim 18, claim 18 has been analyzed and rejected as per claim 1 of Buzaglo (col. 13, lines 1-4).
Claim Interpretation
4. 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.
5. 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.
6. 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. Such claim limitation(s) is/are: “imaging units”, “first imaging unit”, “second imaging unit” in claims 10, 13, and 20.
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.
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 § 112
7. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
8. Claims 7 and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 7 and 18 recite “the one or more images comprise at least two images of the defect”. One image can only comprise one image, not two. Proper correction is required.
35 USC § 101
9. Claims 1 and 13 are eligible under 35 USC 101, as the improvement as disclosed in the specification of “determining a combined classification decision of a defect by combining a plurality of prediction models that provide a plurality of classification decisions of the defect” is recited in the claims.
Claim Rejections - 35 USC § 103
10. 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.
11. 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.
12. Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al., U.S. Patent Publication No. 2018/0107928 A1, further in view of Stefano et al., 2009, “Learning Bayesian Networks by Evolution for Classifier Combination”, and further in view of Iwanaga, U.S. Patent No. 10,818,004 B2.
Regarding claim 1, Zhang discloses A method comprising:
obtaining one or more images of a defect located on a die of a semiconductor wafer (Figure 1; para 0001 – “the present invention generally relates to diagnostic methods and systems for deep learning models configured for semiconductor applications”; para 0004 – “Inspection processes are used at various steps during a semiconductor manufacturing process to detect defects on specimens”; paras 0011 and 0025 – “This system includes an imaging tool configured for generating images of a specimen”; para 0026 – the specimen is a wafer – where a semiconductor wafer comprises a plurality of dies”; para 0079 – determination of defective pixels are dependent on circuit design, and circuit design on wafer corresponds to die(s));
Claim 1 further recites “applying a plurality of prediction models on the one or more images to obtain a plurality of classification decisions of the defect, each of the plurality of prediction models is configured to classify defects into one or more defect classes”. Zhang discloses in (para 0067 – deep learning model for classifying defects on the specimen; para 0068 – deep learning model outputs a classification for a defect detected on the specimen, the deep learning model may output an image classification; para 0098 – Model training 328 may generate one or more trained models, which may then be sent to model selection 330) using best model selection out of multiple models evaluated for defect classification; but does not explicitly teach of using plurality of models for final classifications. However, Stefano teaches combining classifiers methods to generate a final classification (see figures 1 and 2; Abstract – Combining classifier methods have shown their effectiveness in a number of applications; page 967 – left column – Section 2 – The architecture of the combiner – “Consider the responses e1,…, eL provided by a set of L classifiers (experts) for an input sample x in a N class problem, and assume that such responses constitute the input to the combiner, as shown in figure 1. The combiner can be defined as a “higher level” classifier that works on L-dimensional discrete-value feature space…Once this conditional probability has been learned, the combiner provides the output u for each unknown input sample, as the most probable class give the expert observation”. Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Zhang with the teachings of DeStefano as set forth. A person having ordinary skill in the art would have been motivated before the effective filing date of the claimed invention to modify Zhang with the teachings of DeStefano, because DeStefano teaches combining classifiers has been shown to be effective, as it optimizes how individual classifier outputs (experts) are weighted and combined, directly mitigating consensus on wrong decisions, enhancing stability and better accuracy.
Claim 1 further recites “obtaining metrology information of the defect; and utilizing the metrology information in combination with the plurality of classification decisions to determine a combined classification decision of the defect; and outputting the combined classification decision”. Zhang in para 0052 discloses that “the imaging tool can be configured as metrology tool”, but the combined invention of Zhang and DeStefano does not explicitly teach obtaining metrology information of the defect and utilizing the metrology information in combination with the plurality of classification decisions to determine a combined classification decision of the defect. However, Iwanaga teaches “The substrate defect inspection apparatus according to the present disclosure…captures an image of a wafer picked up by an imaging module provided….to perform defect inspection (col. 3, lines 58-63); further teaches “for the sake of convenience in illustration of the comprehensive determination, the estimation result of the first estimation part 4 is referred to as a deep learning (DL) classification, and the estimation result of the second estimation part 6 is referred to as a rule-based classification” (col. 11, lines 29-33); where the rule-based classification includes extracting the defect region and obtaining attributes of each defect region, where the attribute includes the shape, area, length dimension, width dimension, circumference length, extending direction, an average value of grayscale values, and the like (col. 9, line 61- col. 10, line 11) – where the attributes listed here are nothing but metrology information of the defect. Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to use the teachings of Iwanaga of obtaining and including metrology information of the defect along with Deep learning classification in the combined invention of Zhang and DeStefano. A person having ordinary skill in the art would have been motivated before the effective filing date of the claimed invention to use the teachings of Iwanaga of obtaining and including metrology information of the defect along with Deep-Learning classification in the combined invention of Zhang and DeStefano, for the sake of convenience in illustration of the comprehensive determination of the type of defect with high accuracy (see Iwanaga – col. 14, lines 36-41).
Claim 2 recites “The method of Claim 1, wherein the metrology information is collected by a scanner” (Iwanaga as cited teaches “The substrate defect inspection apparatus according to the present disclosure…captures an image of a wafer picked up by an imaging module provided….to perform defect inspection” (col. 3, lines 58-63); and further teaches “an imaging module 104 for imaging the entire front surface of the wafer (col. 4, lines 13-14) – where imaging the entire surface is nothing but scanning, hence imaging apparatus is a scanner).
Regarding claim 3, The combined invention of Zhang, DeStefano and Iwanaga discloses “The method of Claim 1, wherein the metrology information is collected by an Automated Optical Inspection (AOI) scanner” (as cited in rejection of claim 1, Zhang teaches AOI scanner; and so does Iwanaga as once the system is setup, the defect detection is automatically done using algorithms).
Regarding claim 4, The combined invention of Zhang, DeStefano and Iwanaga discloses “the method of Claim 1, wherein the metrology information comprises a size measurement of the defect, whereby the size measurement of the defect in combination of the classification decisions of the plurality of prediction models is utilized to determine the combined classification decision” (see the citation made in the rejection of claim 1 in Iwanaga – length, width, and/or circumference dimension provides the size measurement of the defect).
Regarding claim 5, The combined invention of Zhang, DeStefano and Iwanaga discloses “The method of Claim 1, wherein the metrology information comprises at least one of a histogram of the defect, a maximum color or grey level value of the defect; and a minimum color or grey level value of the defect.” (see Iwanaga – col. 7, lines 35-45 and lines 62-67 – black pixels being the defective pixels; col. 14, lines 13-19 - the binarized data may be created by binarizing values of received light intensity (degree of grayscale) of the respective R component, G component, and B component represented for each pixel of the picked-up image by using a threshold value).
Regarding claim 6, the combined invention of Zhang, DeStefano and Iwanaga discloses “The method of Claim 1, wherein at least some of the prediction models are deep learning models” (see the citation made in the rejection of claim 1).
Regarding claim 7, the combined invention of Zhang, DeStefano and Iwanaga discloses “The method of Claim 1, wherein the one or more images comprise at least two images of the defect” (see Zhang – para 0034 – one or more detection channels (two-detection channels); para 0037 – dark field (DF) imaging and bright field (BF) imaging detection channels together; para 0038 – The one or more detection channels may include any suitable detectors known in the art. For example, the detectors may include photo-multiplier tubes (PMTs), charge coupled devices (CCD), time delay integration (TDI) cameras, and any other suitable detectors known in the art….are configured to generate images of the specimen; para 0040 - Computer subsystem 36 of the imaging tool may be coupled to the detectors of the imaging tool in any suitable manner (e,g., via one or more transmission media, which may include “wired” and/or “wireless” transmission media) such that the computer subsystem can receive the output generated by the detectors during scanning of the specimen).
Regarding claim 8, the combined invention of Zhang, DeStefano and Iwanaga discloses “The method of The method of wherein the at least two images of the defect comprise at least two images in two different imaging modalities, wherein at least one prediction model is a fusion model that is based on features extracted from the at least two images of the two different imaging modalities” (see Zhang – para 0034 – one or more detection channels (two-detection channels); para 0037 – dark field (DF) imaging and bright field (BF) imaging detection channels together; para 0038 – The one or more detection channels may include any suitable detectors known in the art. For example, the detectors may include photo-multiplier tubes (PMTs), charge coupled devices (CCD), time delay integration (TDI) cameras, and any other suitable detectors known in the art….are configured to generate images of the specimen; para 0040 - Computer subsystem 36 of the imaging tool may be coupled to the detectors of the imaging tool in any suitable manner (e,g., via one or more transmission media, which may include “wired” and/or “wireless” transmission media) such that the computer subsystem can receive the output generated by the detectors during scanning of the specimen; para 0052 – The image inputs to the deep learning model described herein are generated by an inspection tool; para 0067 – “the deep learning model includes one or more fully connected layers configured for classifying defects on the specimen. A “fully connected layer” may be generally defined as a layer in which each of the nodes is connected to each of the nodes in the previous layer. The fully connected layer(s) may perform classification based on the features extracted by convolutional layer(s), which may be configured as described further herein. The fully connected layer(s) are configured for feature selection and classification. In other words, the fully connected layer(s) select features from a feature map and then classify the defects in the image(s) based on the selected features.” – the deep learning model receives images from multiple detectors and hence acts as fusion model).
Regarding claim 9, the combined invention of Zhang, DeStefano and Iwanaga discloses “The method of Claim 1, wherein the one or more images are obtained from a plurality of imaging units” (see the citations made in the rejections of claims 7 and 8 – separate detection channels are considered here as different modalities).
Regarding claim 10, claim 10 has been similarly analyzed and rejected as per citations made in rejection of claim 8.
Regarding claim 11, the combined invention of Zhang, DeStefano and Iwanaga discloses “The method of Claim 1, wherein at least one prediction model is configured to provide a classification prediction based on an image of the one or more images and based on a reference image.” (Zhang as cited used Deep learning algorithms, which are trained on the images/data of defects; and further based on these training images, is used to classify defects, therefore, the training images can be considered here as reference image(s); para 0092 – training images. Further adding, comparison inspection is very well known to be used in the industry).
Regarding claim 12, the combined invention of Zhang, DeStefano and Iwanaga discloses “The method of Claim 11, wherein the reference image is a golden die image” (as cited before, Zhang discloses detecting defect(s) with respect to circuit design (para 0078); and as further discussed in the rejection of claim 11, training images can be considered here as reference images that recite the circuit design as golden image. None the references use the term “golden die image” explicitly, but reference image used in AOI is well-known to be termed as golden die image).
Regarding claim 13, claim 13 has been similarly analyzed and rejected as per citations made in rejection of claim 1.
Regarding claim 14, claim 14 has been similarly analyzed and rejected as per citations made in rejection of claim 2.
Regarding claim 15, claim 15 has been similarly analyzed and rejected as per citations made in rejection of claim 4.
Regarding claim 16, claim 16 has been similarly analyzed and rejected as per citations made in rejection of claim 5.
Regarding claim 17, claim 17 has been similarly analyzed and rejected as per citations made in rejection of claim 1.
Regarding claim 18, claim 18 has been similarly analyzed and rejected as per citations made in rejection of claim 7.
Regarding claim 19, claim 19 has been similarly analyzed and rejected as per citations made in rejection of claim 8.
Regarding claim 20, claim 20 has been similarly analyzed and rejected as per citations made in rejection of claim 10.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Manav Seth whose telephone number is (571) 272-7456. The examiner can normally be reached on Monday to Friday from 8:30 am to 5:00 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Sumati Lefkowitz, can be reached on (571) 272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Manav Seth/
Primary Examiner, Art Unit 2672
April 28, 2026
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