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
The information disclosure statement (IDS) submitted on is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claim(s) 1, 2, 7, and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Patwary et al. (US 20200244963 A1) referred to as Patwary hereinafter and further in view of Li et al. (CN 114936267 A) referred to as Li hereinafter.
Regarding claim 1, Patwary teaches A method for increasing Signal-to-Noise-Ratio (SNR) of defect detection (“the controller 104 may optimize the SNR of T−T.sub.B by using known defects as part of a recipe setup” Patwary, para. [0061]) in inspection of wafers or masks, the method comprising: (“a system and method for detecting defects on semiconductor wafers (e.g., samples)” Patwary, para. [0029])
receiving a current image; (“the target image frames 125 may be acquired across a plurality of regions” Patwary, para. [0047])
receiving a reference image; (“generate one or more reference tensors including one or more reference image frames based on the core tensor;” Patwary, para. [0043]) and (“It is noted herein that a single target image frame 125 could be used throughout method 200 to generate a reference tensor 136 (T.sub.B) including a single reference image frame.” Patwary, para. [0060])
receiving an indication for existence of a defect in the current image; (“The controller 104 may be configured to determine any characteristics of the sample 120 known in the art including, but not limited to, defects (e.g., defect location, defect type), measurements (e.g., critical dimensions), and the like.” Patwary, para. [0068])
producing a difference image between the current image and the reference image; (“by subtracting the estimated background structure of the reference image frame 145c from the target image frame 125c, a defect 137 within the target image frame 125c may be clearly shown in the generated difference image frame 155c.” Patwary, para. [0065])
performing singular value decomposition (SVD) on the difference image; (“generate one or more stacked difference images with the one or more acquired difference image frames; perform a set of one or more singular value decomposition (SVD) processes on the one or more stacked difference images to form a set of one or more singular vectors;” Patwary, para. [0008])
removing one or more lower-valued singular values from a matrix produced by the SVD, thereby producing a reduced middle matrix; (“the controller 104 is configured to generate the one or more reference tensors 136 by performing one or more low-rank approximations of the core tensor 132 (S) generated via the multilinear decompositions.” Patwary, para. [0054])
and producing an improved-SNR difference image by reconstructing the difference image using the reduced middle matrix. (“In step 910, a modified stacked difference image is generated based on the modified set of one or more singular vectors. For example, the controller 104 may be configured to reconstruct the decomposed stacked difference image (d.sub.stk (x, y)) as a high-order stacked difference image (d.sub.stk.sup.′(x, y)) based on the modified set of one or more singular vectors (e.g., set of remaining, non-truncated singular vectors). For instance, the controller 104 may be configured to generate the high-order stacked difference image using the first k number of singular vectors. In this regard, the controller 104 may be configured to reconstruct the stacked difference image (d.sub.stk(x, y)) by truncating one or more singular vectors. As noted previously herein, pattern noise may typically appear within the high-ranked vectors of the stacked difference image. Accordingly, by subtracting the effects of high-ranked vectors (e.g., subtracting high-order stacked difference image (d.sub.stk.sup.′(x, y))) from the original stacked difference image (d.sub.stk(x, y)), the effects of high-order singular vectors may be removed, thereby removing effects of pattern noise.” Patwary, para. [0085])
However, Patwary does not teach diagonal middle matrix.
Li teaches diagonal middle matrix. (“decomposing it into a block diagonal tensor and three projection matrix, wherein the block diagonal tensor for engraving the interactive relationship between multiple modes, it is composed of I same size of the tensor block in the form of block diagonal, as shown in FIG. 7 (a), in middle removing the rest elements are 0, projection matrix Ct and Cp of two input characteristics are corresponding to the diagonal tensor block” Li, p. 12)
Patwary and Li are combinable because they are from the same field of endeavor, image processing.
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 Patwary in light of Li’s diagonal middle matrix. One would have been motivated to do so because it can cause improved block diagonal decomposition control training parameter scale in which redundant information in the tensor of the picture interaction is reduced. (Li, abstract)
Regarding claim 2, Patwary teaches further comprising producing an improved-SNR current image by merging the improved-SNR difference image with the reference image. (“Using these conventional inspection techniques, the comparison of the respective images is typically carried out by subtraction after suitable sub-pixel alignment of all die used for the computation is performed. The subtraction operation between the images is intended to remove most of the intrinsic pattern of the sample, leaving any defect signals and residual noise components. The defect may then be detected if the defect signal value exceeds that of the residual noise signal. However, these conventional inspection techniques may include additional noise from adjacent reference imagery, leading to decreased sensitivity. For example, die-to-die inspection techniques may include process variation errors and alignment errors between the target die and the reference die.” Patwary, para. [0004])
Regarding claim 7, Li teaches wherein the removing one or more lower-valued singular values from the diagonal middle matrix produced by the SVD, comprises removing all but k of the higher-valued singular values from the diagonal middle matrix produced by the SVD, where k> 0. (“if the sample i belongs to the j-th vector in the j-dimensional value is 1, otherwise is 0, yi, j is the prediction result of the model, is the L2 regularization of all the training parameter in the model, for preventing the training model over-fitting, wherein λ represents the corresponding balance coefficient.” Li, p. 13) and (“Tensor for multi-modal fusion respectively is proportional to the dimension of two input characteristics, in order to ensure the complexity of the control model under the premise of rich interaction, the invention uses the improved block diagonal decomposition method for tensor Decomposition: decomposing it into a block diagonal tensor and three projection matrix, wherein the block diagonal tensor for engraving the interactive relationship between multiple modes, it is composed of I same size of the tensor block in the form of block diagonal, as shown in FIG. 7 (a), in middle removing the rest elements are 0, projection matrix Ct and Cp of two input characteristics are corresponding to the diagonal tensor block” Li, p. 12)
Regarding claim 8, Patwary teaches A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations comprising: (“the memory 108 may include a non-transitory memory medium, the memory 108 maintains program instructions for causing the one or more processors 106 to carry out the various steps described through the present disclosure.” Patwary, para. [0041])
Regarding rest of claim 8, refer to the explanation of claim 1.
Claim(s) 3-6 are rejected under 35 U.S.C. 103 as being unpatentable over Patwary and Li as mentioned above and further in view of Honda et al. (US 9778206 B2) referred to as Honda hereinafter.
Regarding claim 3, the combination of Patwary and Li does not teach wherein the improved-SNR difference image is multiplied by a factor larger than 1 before merging, thereby emphasizing the defect within the improved-SNR current image.
Honda teaches wherein the improved-SNR difference image is multiplied by a factor larger than 1 before merging, thereby emphasizing the defect within the improved-SNR current image. (“FIG. 5B is a plan view of a semiconductor wafer and an enlarged view of a plurality of dies illustrating a configuration of a die of a semiconductor wafer serving as an inspection target in the defect inspection devices according to the first embodiment and the first and second modified examples of the present invention.” Honda, col. 5, lines 43-48) and (“Here, as a typical example, a determination formula is described based on the sum of values obtained by squaring a feature quantity, but a linear form may be used as it is without squaring a feature quantity. A next formula, that is, (Formula 3) is obtained by performing normalization based on the threshold of (Formula 1) and multiplying a gain G(k) of each detection condition, and indicates a determination formula in a plurality of detection conditions.” Honda, col. 15, lines 10-19)
Patwary, Li, and Honda are combinable because they are from the same field of endeavor, image processing.
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 Patwary and Li in light of Honda’s multiplying the improved SNR difference image. One would have been motivated to do so because it can improve the inspection sensitivity. (Honda, col. 30, lines 1-2)
Regarding claim 4, Honda teaches the indication for existence of a defect in the current image comprises a location within the current image where the defect is suspected to exist; (“In S1075, a defect likelihood is calculated together with the temporary defect determination.” Honda, col. 26, lines 1-2)
and further comprising: selecting a partial window within the current image which includes the suspected location of the defect; (“a partial image clipping unit that clips a partial image including the extracted defect candidate and a neighboring image of the defect candidate from the plurality of images acquired by the image acquiring unit based on position information of the defect candidate extracted by the defect candidate extracting unit” Honda, col. 4, lines 5-11)
selecting a partial window within the reference image corresponding to the partial window of the current image; (Honda, fig. 5A)
and producing the difference image between the partial window of the current image and the partial window of the reference image. (“In FIG. 15, 1504 indicates a defect determination feature quantity calculated based on the feature quantity accumulated in the feature quantity storing buffer 125-2 of FIG. 1C, and is typically a value obtained by dividing a difference between the inspection image and the reference image by a variation of a differential image calculated for each pixel. 1505 indicates a temporary defect determination result determined by the second defect determining section 181-2, and a determination result is output in a case in which a determination result is apparently understood such as scratch defect or a nuisance generated in an edge portion of a pattern with high intensity, and an indefinite result is output for the other cases.” Honda, col. 11, lines 57-67, col. 12, lines 1-3)
Regarding claim 5, Honda teaches wherein a size of the partial windows is selected to include all of an area of a suspected defect. (“digital image signals of regions 510, 520, 540, and 550 are set as a reference image for the same position of chips that are regularly arranged in a detection image, for example, a region 530 of the detection image of FIG. 5A, a comparison with a corresponding pixel of the reference image or another pixel in the detection image is performed, and a pixel having a large difference is detected as a defect candidate.” Honda, col. 16, lines 26-34) and figs. 5A-5B
Regarding claim 6, Honda teaches wherein a size of the partial windows is selected to include less than all of an area of a suspected defect. (“FIG. 16 illustrates an example of misalignment correction of a defect candidate by the first defect determining section 180-1. Defect candidates 1630 and 1640 used for misalignment detection are selected from a map 1610 of defect candidates detected from an image acquired in a first image acquisition condition and a map defect candidate 1620 of defect candidates detected from an image acquired in a second image acquisition condition, and a deviation amount is calculated from the selected defect candidate.” Honda, col. 28, lines 37-45)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PARDIS SOHRABY whose telephone number is (571)270-0809. The examiner can normally be reached Monday - Friday 9 am till 6pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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.
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.
/PARDIS SOHRABY/Examiner, Art Unit 2664
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664