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 .
Election/Restrictions
Claim 20 is withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 05/19/2026.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. 18798487, filed 08/08/2024
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/08/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
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.
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: ‘parameter optimizer’ and ‘overlay measurement device’ in claims 17-19.
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
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.
Claims 2-10 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.
Claim 2 recites the limitation "the image processing scores of the neighboring parameter sets”. There is insufficient antecedent basis for this limitation in the claim. The first instance of this is being interpreted as a new element.
Claims 3-10 are rejected as dependent on claim 2.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 13 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. The limitations of claim 13 are fully embodied in the last limitation of claim 1. The last limitation of claim 1 describes determining a second optimization parameter set corresponding to each cluster. Claim 13 is essentially identical but for breaking it up into a first and second cluster, which is implied by the “each cluster” of claim 1. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 11, 13, and 16 are rejected under 35 U.S.C. 101.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of determining parameters based on images, without significantly more.
The claim recites: A parameter optimization method comprising:
obtaining a plurality of SEM images portraying respective positions of a semiconductor wafer, wherein a first pattern and a second pattern are disposed on the semiconductor wafer, and wherein a relative positioning between the first pattern and the second pattern is based on overlay settings;
determining a primary optimization parameter set based on the plurality of SEM images, the primary optimization parameter set comprising one or more image processing parameters;
clustering the plurality of SEM images in a plurality of clusters based on image attributes of the plurality of SEM images; and
determining a secondary optimization parameter set corresponding to each cluster of the plurality of clusters, based on SEM images included in the cluster and based on the primary optimization parameter set.”
The limitations, as drafted, are processes that, under their broadest reasonable interpretation, cover performance of the limitation in the mind. A person can look at images and determine primary and secondary ‘optimization parameter sets’ and cluster the images according to image attributes. The ‘obtaining’ limitation amounts to insignificant, extra-solution activity (data collection).
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of a semiconductor wafer with patterns based on overlay settings. This is recited at a high level of generality such that it amounts to a generic semiconductor wafer produced using generic production means. Accordingly, the additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are recited at a high-level of generality. It is therefore a judicial exception that is not integrated into a practical application, and does not include additional elements that are sufficient to amount to significantly more than the judicial exception. This claim is not patent eligible.
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of attributes comprising average and standard deviation of brightness, which can be calculated mentally or using basic mathematical formulas, without significantly more.
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of determining parameter sets according to each cluster, which can be done mentally, without significantly more.
Claim 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to the insignificant elements of specific semiconductor wafer patterns, without significantly more.
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 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ho (US20200035571A1) in view of Almog (US11035803B1).
Regarding Claim 1, Ho teaches “A parameter optimization method comprising: obtaining a plurality of SEM images portraying respective positions of a semiconductor wafer,” (Ho, Paragraph 39, “FIG. 6 illustrates a chart 350 that shows the measurement data of the lateral dimensions 280-281 and their calculated differences for a plurality of samples. In more detail, the graph 350 contains a plurality of rows 1-28, each of which corresponds to a different sample image. These samples may be taken from different dies or different parts of the same die. In some embodiments, the samples are all taken from the dies that are located at or near the edge region of a wafer, and each SEM image is taken from the same location of a corresponding die, for example at an upper right location of the die, or a bottom left location of the die. The specific location on the die is where the region 210 of FIGS. 2A-2B (or a similar region) is located.” Accordingly, a plurality of SEM images (‘different sample images’), taken from an SEM image of a semiconductor wafer, portray 28 respective positions (at or near the edge region of a semiconductor wafer). Figure 1A also shows multiple dies (70) at the edge of the semiconductor wafer image taken by SEM, and Figure 2A shows one such die.)
“wherein a first pattern and a second pattern are disposed on the semiconductor wafer,” (Ho, Figure 2B, also described in Paragraph 27, shows via hole 230 (first pattern) and metal line trench 240 (second pattern) disposed on the semiconductor wafer.)
“and wherein a relative positioning between the first pattern and the second pattern is based on overlay settings;” (Ho, Paragraphs 57-58 describe calculating overlay (relative positioning) between the patterns (via hole and metal line), and adjusting processing in accordance with overlay settings: “The method 800 includes a step 840 of determining an overlay between the via hole and the metal line based on the first dimension and the second dimension. In some embodiments, the determining step 840 comprises calculating a difference between the first dimension and the second dimension. The method 800 includes a step 850 of, in response to determining that the overlay is an excessive overlay, adjusting a subsequent lithography process performed to the wafer to compensate for the excessive overlay.”)
“determining a primary optimization parameter set based on the plurality of SEM images, the primary optimization parameter set comprising one or more image processing parameters;” (Ho, Paragraph 35, “In order to increase the contrast between the portions 260-261 of the via hole 230 and their neighboring regions, a gray level threshold 350 is defined, as is shown in the graph 300 but more clearly in the magnified portion 310 of the graph. As discussed above, the two portions 260-261 of the via hole 230 are substantially darker than the rest of the areas in the region 210 of the image 200. The pixels that make up the portions 260-261 of the via hole 230 are represented by the pixels below (i.e., to the left of) the threshold 350. In the illustrated embodiment, the threshold 350 is defined between a gray level of 3 and 5, for example 4, in a gray scale from 0 to 255. It is understood that the threshold 350 is not statically defined for all cases or SEM images. Rather, the threshold 350 may be adaptively defined in each image.” The adaptively defined threshold 350 for the images is mapped to the primary optimization parameter set. Paragraph 37 further describes this adaptive thresholding based on the plurality of SEM images: “In some embodiments, this “thresholding” analysis discussed above utilizes a clustering-based image thresholding known as “Otsu's method.” Otsu's method contains an algorithm to reduce a gray level image to a binary image. The algorithm assumes that the image contains two classes of pixels following bi-modal histogram (foreground pixels and background pixels). It then calculates the optimum threshold separating the two classes so that their combined spread (intra-class variance) is minimal, or equivalently (because the sum of pairwise squared distances is constant), so that their inter-class variance is maximal. Otsu's method (or any modified version thereof) may be carried out using Matlab code or JavaScript as well.”)
Ho does not expressly disclose “clustering the plurality of SEM images in a plurality of clusters based on image attributes of the plurality of SEM images; and determining a secondary optimization parameter set corresponding to each cluster of the plurality of clusters, based on SEM images included in the cluster and based on the primary optimization parameter set.”
Almog discloses clustering semiconductor images into a plurality of clusters based on image attributes of the semiconductor images, and further determining an optimization parameter set corresponding to each cluster based on the images of the cluster (Almog, Paragraph 7, “In accordance with certain aspects of the presently disclosed subject matter, there is provided a system of examination of a semiconductor specimen, the system comprising a processor and memory circuitry (PMC) configured to (1) for each potential defect of at least a first subset of potential defects of the semiconductor specimen, obtain pixel values representative of the potential defect in multiple images of the semiconductor specimen which differ from each other by at least one parameter, (2) classify the potential defects of the first subset into a plurality of first clusters, wherein the classification is based on pixel values representative of the potential defects in the multiple images, (3) for each first cluster, build, based on pixel values representative of potential defects of the first cluster in the multiple images, at least one first matching filter for the first cluster, (4) for at least a given potential defect not belonging to the first subset, process pixel values representative of the given potential defect in multiple images of the semiconductor specimen which differ from each other by at least one parameter, with each first matching filter obtained for each of the first clusters, and compare an output of the processing with at least one threshold, wherein the comparison is indicative of at least whether the given potential defect corresponds to a defect.” Note that Almog clusters the semiconductor images according to pixel values (mapped to the clustering based on image attributes), and determines a first matching filter for each cluster (secondary optimization parameter set corresponding to each cluster) based on the images of each cluster.)
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to a) perform the clustering based on image attributes of Almog using the plurality of SEM images of Ho, and b) determine the secondary optimization parameter set corresponding to the images included in the clusters, as taught by Almog, based on the thresholded SEM images of Ho (note that the thresholded images are themselves based on the SEM images and the primary optimization parameter set).
The motivation for doing so would have been to improve defect detection and classification as expressly described by Paragraph 24 of Almog: “Among advantages of certain embodiments of the presently disclosed subject matter is improvement of detection and classification of defects. In some embodiments, the proposed solution takes advantage of the acquisition of multiple images of a specimen which differ by at least one parameter, in order to improve detection and classification of defects. Other advantages include ability to detect defects among a large number of potential defects, which cannot be achieved by conventional techniques implementing machine learning networks.” Additionally, the motivation to use Ho’s foreground-background-separated thresholded images for the filter determination (secondary optimization parameter set) determination of Almog would have been to predictably build more reliable filters as a result of the decreased noise of the thresholded images. Further, one skilled in the art could have combined the elements as described above by known methods 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 Ho with the above teaching of Almog to fully disclose “clustering the plurality of SEM images in a plurality of clusters based on image attributes of the plurality of SEM images; and determining a secondary optimization parameter set corresponding to each cluster of the plurality of clusters, based on SEM images included in the cluster and based on the primary optimization parameter set.”
Regarding Claim 13, Ho in view of Almog teaches “The parameter optimization method of claim 1,”
“wherein determining the secondary optimization parameter set corresponding to each of the plurality of clusters includes: determining a secondary optimization parameter set corresponding to a first cluster of the plurality of clusters based on SEM images included in the first cluster; and determining a secondary optimization parameter set corresponding to a second cluster of the plurality of clusters based SEM images included in the second cluster.” (Almog, Paragraph 7, “In accordance with certain aspects of the presently disclosed subject matter, there is provided a system of examination of a semiconductor specimen, the system comprising a processor and memory circuitry (PMC) configured to (1) for each potential defect of at least a first subset of potential defects of the semiconductor specimen, obtain pixel values representative of the potential defect in multiple images of the semiconductor specimen which differ from each other by at least one parameter, (2) classify the potential defects of the first subset into a plurality of first clusters, wherein the classification is based on pixel values representative of the potential defects in the multiple images, (3) for each first cluster, build, based on pixel values representative of potential defects of the first cluster in the multiple images, at least one first matching filter for the first cluster, (4) for at least a given potential defect not belonging to the first subset, process pixel values representative of the given potential defect in multiple images of the semiconductor specimen which differ from each other by at least one parameter, with each first matching filter obtained for each of the first clusters, and compare an output of the processing with at least one threshold, wherein the comparison is indicative of at least whether the given potential defect corresponds to a defect.” Accordingly, a matching filter (secondary optimization parameter set) is determined for each cluster based on the pixel values of the images) in each cluster. Note that this was incorporated with motivation and rationale in the rejection of claim 1. Further note the 35 USC 112(d) rejection above.)
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ho in view of Almog further in view of Song (US 20200194437 A1).
Regarding Claim 16, Ho in view of Almog teaches “The parameter optimization method of claim 1,”
Ho in view of Almog do not expressly disclose, “wherein the first pattern on the semiconductor wafer is a lower electrode of a cell capacitor, and the second pattern on the semiconductor wafer is a support hole.”
Song discloses patterns of a semiconductor wafer including a lower electrode of a cell capacitor and a support hole (Song, Figure 5 and Paragraph 42 and last sentence of Paragraph 102, “Referring to FIG. 5, a circle may be obtained in plan view when connecting a sidewall of the first support hole 1a with central points of the bottom electrodes BE and DBE exposed through the first support hole 1a. For example, the first support hole 1a may have a cross-section of a circle in a plan view, with a perimeter of the circle extending through central points of three adjacent bottom electrodes BE and DBE, and with a center of the circle being in a center of a region between the three adjacent bottom electrodes BE and DBE. The first support hole 1a may partially expose sidewalls of the three adjacent bottom electrodes BE and DBE.”; “A capacitor CAP may be constituted by the bottom electrode BE, the dielectric layer DL, and the top electrode UE.”)
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the first and second patterns of Ho in view of Almog with the lower electrode of a cell capacitor and the support hole, respectively, of Song.
The motivation for doing so would have been to expand the utility and applications of the method by including other types of patterns usable for the overlay measurement. Further, one skilled in the art could have combined the elements as described above by known methods 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 Ho in view of Almog with the above teaching of Song to fully disclose “wherein the first pattern on the semiconductor wafer is a lower electrode of a cell capacitor, and the second pattern on the semiconductor wafer is a support hole.”
Allowable Subject Matter
Claims 2-12 and 14-15 are objected to as being dependent upon a rejected base claim, claims 2-10 are rejected under 35 USC 112(b), and claim 11 is rejected under 35 USC 101, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, and amended to overcome the 35 USC 112(b) and 35 USC 101 rejections.
The following is a statement of reasons for the indication of allowable subject matter: With respect to claims 2, 11, 12, and 14 (along with dependent claims 3-10 and 15), in addition to other limitations in the claims the Prior Art of Record fails to teach, disclose or render obvious the applicant' s invention as claimed, in particular:
Claim 2 recites: “The parameter optimization method of claim 1, wherein determining the primary optimization parameter set comprises:
processing the plurality of SEM images using a target parameter set and neighboring parameter sets of the target parameter set, to obtain, for each of the target parameter set and the neighboring parameter sets, an image processing score; and
based on the image processing score of the target parameter set being less than a maximum image processing score of the image processing scores of the neighboring parameter sets, adjusting the target parameter set to a neighboring parameter set, of the neighboring parameter sets, having the maximum image processing score, or based on the image processing score of the target parameter set being greater than or equal to the maximum image processing score of the image processing scores of the neighboring parameter sets, determining that the target parameter set is the primary optimization parameter set.”
Claim 11 recites: “The parameter optimization method of claim 1, wherein the image attributes comprise an average of image brightness and a standard deviation of image brightness.”
Claim 12 recites: “The parameter optimization method of claim 1, wherein clustering the plurality of SEM images in the plurality of clusters comprises: determining centroids of the plurality of clusters using a K-means method; and labeling each of the plurality of SEM images using a K-nearest neighbor (K-NN) method based on the centroids.”
Claim 14 recites: “The parameter optimization method of claim 13, wherein determining the secondary optimization parameter set corresponding to the first cluster comprises:
generating image processing scores of the first cluster using each of the primary optimization parameter set and neighboring parameter sets based on the SEM images included in the first cluster; and
selecting at least one of a plurality of image processing parameters corresponding to the first cluster based on the image processing scores of the first cluster, and
wherein determining the secondary optimization parameter set corresponding to the second cluster comprises:
generating image processing scores of the second cluster using each of the primary optimization parameter set and the neighboring parameter sets based on the SEM images included in the second cluster; and
selecting at least one of a plurality of image processing parameters corresponding to the second cluster based on the image processing scores of the second cluster.”
Ho, Almog, and Kondo (US 20200411345 A1) represent the closest Prior Art to the invention of the instant application. Ho teaches measuring overlay of a semiconductor wafer based on SEM images and adjusting lithography steps as needed based on correct or incorrect pattern alignment. Almog teaches semiconductor specimen examination wherein images corresponding to defect types are clustered and used to generate matching filters to be used for subsequent identification of defect types. Kondo teaches wafer observation that inspects semiconductor wafers during manufacturing and detects alignment using a unique pattern (alignment mark). However, neither of these references disclose the bolded limitations above. Regarding claim 2, the primary optimization parameter of Ho in view of Almog corresponds to image thresholding performed using, for example, the Otsu method. This method is entirely distinct from the primary optimization parameter determination steps of claim 2. Regarding claim 11, the image attributes of Ho in view of Almog are mapped to pixel values. While these could include brightness, that is not expressly stated, and it is also not obvious to perform the mean and standard deviation determination of claim 11 for the determination of the pixel values. Such a combination would likely be inoperable in addition to non-obvious. Regarding claim 12, Almog does describe k-means method to determine clusters. However the claim requires using k-means method on the already-formed clusters. It would not make sense to incorporate such a feature into Ho in view of Almog because the k-means method was already done by Almog to generate clusters in the first place. Regarding claim 14, this claim provides fine detail regarding the determination of the secondary optimization parameter set, which is completely distinct from the determination of the secondary optimization parameter set of Ho in view of Almog, which relates to the generation of matching filters. It would not be obvious to replace the matching filter generation with the determination of the secondary optimization parameter set described in claim 14.
The following additional references, while relevant to the Instant Application, do not disclose the above bolded elements. Song teaches architecture of semiconductor devices including support holes and bottom electrodes of capacitor. Pu (US 20220207713 A1) teaches adjustment of parameter sets, related to compensation data and loss values, in the manufacturing of integrated circuits. Sapiens (US20150323471A1) teaches parameter determination (including an overlay error) of a target image based on the imaging structure and scatterometry structure. Nakagaki (Automatic recognition of defect areas on a semiconductor wafer using multiple scanning electron microscope images) teaches a method for automated defect detection of semiconductor wafers imaged with SEM according to surface-roughness analysis.
Claims 17-19 are allowed.
The following is an examiner’s statement of reasons for allowance: With respect to claim 17 (and dependent claims 18-19), in addition to other limitations in the claims, the Prior Art of Record fails to teach, disclose or render obvious the applicant' s invention as claimed, in particular:
Claim 17 recites: “An on-cell overlay measurement system comprising:
a scanning electron microscope configured to capture a plurality of SEM images of a semiconductor wafer on which a first pattern and a second pattern are disposed, wherein a relative positioning between the first pattern and the second pattern is based on overlay settings;
a parameter optimizer configured to receive the plurality of SEM images from the scanning electron microscope and to transmit a parameter set and the plurality of SEM images to an overlay measurement device; and
the overlay measurement device, wherein the overlay measurement device is configured to generate overlay information of the plurality of SEM images based on the parameter set and the plurality of SEM images,
wherein the parameter optimizer is configured to:
determine a primary optimization parameter set based on the overlay settings and based on the overlay information of the plurality of SEM images, the primary optimization parameter set comprising one or more image processing parameters;
cluster the plurality of SEM images in a plurality of clusters based on image attributes of the plurality of SEM images; and
determine a secondary optimization parameter set corresponding to each of the plurality of clusters based on the primary optimization parameter set, based on the overlay settings, and based on overlay information of SEM images included in the cluster.”
Ho, Almog, and Kondo represent the closes Prior Art to the invention of the instant application. Ho teaches measuring overlay of a semiconductor wafer based on SEM images and adjusting lithography steps as needed based on correct or incorrect pattern alignment. Almog teaches semiconductor specimen examination wherein images corresponding to defect types are clustered and used to generate matching filters to be used for subsequent identification of defect types. Kondo teaches wafer observation that inspects semiconductor wafers during manufacturing and detects alignment using a unique pattern (alignment mark). However, neither of these references disclose the bolded limitations above. For example, Ho in view of Almog, as combined in the rejection of claim 1, determine overlay information (alignment) based on a primary optimization parameter set, whereas claim 17 describes the exact opposite operation. In particular, claim 17 determines overlay information before either the first or second optimization parameter is determined. Additionally, Ho in view of Almog, as combined in the rejection of claim 1, teach the secondary optimization parameter set as based on SEM images and the primary optimization parameter set, whereas claim 17 requires the secondary optimization parameter set as also based on the overlay settings and overlay information. This would not have been an obvious modification to be made to Ho in view of Almog.
The following additional references, while relevant to the Instant Application, do not disclose the above bolded elements. Song teaches architecture of semiconductor devices including support holes and bottom electrodes of capacitor. Pu teaches adjustment of parameter sets, related to compensation data and loss values, in the manufacturing of integrated circuits. Sapiens teaches parameter determination (including an overlay error) of a target image based on the imaging structure and scatterometry structure. Nakagaki teaches a method for automated defect detection of semiconductor wafers imaged with SEM according to surface-roughness analysis.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
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/AARON JOSEPH SORRIN/Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
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