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 .
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 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) 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):
(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). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) 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). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) 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) 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) 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), 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) are: “controller” in claims 1, 4-6, 8-12, 16-20.
Because these claim limitation(s) are being interpreted under 35 U.S.C. 112(f), they 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 these limitations interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f).
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.
Claims 13-14 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 14 recites “the apparatus of claim 13, wherein the fourth signal includes composition information of the object and the defect”; the claim term “the fourth signal” was introduced in claim 12 and not claim 13 (claim 13 depends from claim 11 and not claim 12).; therefore, the antecedence of the fourth signal is unclear. Claim 14 should either depend from claim 12 and not claim 13, or claim 13 should depend from claim 12 and not claim 11 to maintain proper antecedence basis for the claim term; for examination, Examiner will be interpreting claim 14 to depend from claim 12 and claim 13 to maintain its current dependence from claim 11. Proper Corrections are Requested.
Claim 5 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claim 5 repeats the exact same limitations from claim 4 from which claim 5 depends; the repeated claim limitation are “determine morphology information of the defect based on the first, second, third images of the object; determine composition information of the defect based on the fourth image of the object; and identify a defect type based on the first, second, third, and fourth images of the object”; it is unclear if in claim 5, steps A, B, and C are done again before the new limitation “wherein the artificial intelligence assisted object detection algorithms include a YOLO (You Only Look Once) model”; so there is ambiguity as to whether claim 5 executes the three steps twice before the YOLO wherein clause, or whether the steps are only executed once in claim 4. Proper Corrections are Requested.
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.
Claims 1-4 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No.: 10,887,580 (Kolchin et al.) (hereinafter Kolchin), in view of U.S. Patent Publication No.: 5,777,729 (Aiyer et al.) (hereinafter Aiyer), and in view of U.S. Patent Application Publication No.: 2023/0152685 (Bauer).
Regarding claim 1, Kolchin teaches an apparatus comprising: a signal source configured to emit light signal to an object; a first detector positioned at a first location configured to receive a first signal reflected from the object; a second detector positioned at a second location configured to receive a second signal reflected from the object; a third detector positioned at a third location configured to receive a third signal reflected from the object (Kolchin, col. 5, lines 15-26; col. 6, lines 48-51; FIG 1: “FIG. 1 is a simplified schematic view of one embodiment of an optical inspection system 100 configured to perform detection and classification of defects of interest (DOI) on semiconductor wafers based on three-dimensional images. Optical inspection system 100 includes an illumination subsystem, a collection subsystem, one or more detectors, and a computing system. The illumination subsystem includes an illumination source 101 and all optical elements in the illumination optical path from the illumination source to the wafer. The collection subsystem includes all optical elements in the collection optical path from the specimen to each detector.; “Each of detectors 115, 120, and 125 generally function to convert the reflected and scattered light into an electrical signal, and therefore, may include substantially any photodetector known in the art.”;
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a controller configured to: (Kolchin, col. 7, lines 35-49: “System 100 also includes various electronic components (not shown) needed for processing the reflected and/or scattered signals detected by any of detectors 115, 120, and 125. For example, system 100 may include amplifier circuitry to receive output signals from any of detectors 115, 120, and 125 and to amplify those output signals by a predetermined amount and an analog-to-digital converter (ADC) to convert the amplified signals into a digital format suitable for use within processor 131.”)
analyze the first signal to produce a first image of the object including a defect on the object; analyze the second signal to produce a second image of the object including the defect on the object; analyze the third signal to produce a third image of the object including the defect on the object (Kolchin, col. 6, lines 31-37; col. 9, lines 13-27; col. 14, lines 54-65; FIG. 2; FIG. 3: “System 100 includes collection optics 116, 117, and 118 to collect the light scattered and/or reflected by wafer 103 and focus that light onto detector arrays 115, 120, and 125, respectively. The outputs of detectors 115, 120, and 125 are communicated to computing system 130 for processing the signals and determining the presence of defects and their locations.”; “In one aspect, a three-dimensional image of a thick semiconductor structure is generated from a volume measured in two lateral dimensions (e.g., parallel to the wafer surface) and a depth dimension (e.g., normal to the wafer surface). In the embodiment depicted in FIG. 1, computing system 130 arranges the outputs from one or more of the measurement channels (e.g., from one or more of detectors 115, 120, and 125) into a volumetric data set that corresponds to the measured volume. FIG. 2 depicts a plot 150 of a cross-sectional view (y=0) of a measured three-dimensional image illustrating a peak signal near a focus offset of −0.5 micrometers. FIG. 3 depicts a plot 151 of another cross-sectional view (x=0) of the measured three-dimensional image also illustrating a peak signal near a focus offset of −0.5 micrometers.”; “As used herein, the term “wafer” generally refers to substrates formed of a semiconductor or non-semiconductor material … a “reticle” may be a reticle at any stage of a reticle fabrication process, or a completed reticle that may or may not be released for use in a semiconductor fabrication facility. A reticle, or a “mask,” is generally defined as a substantially transparent substrate having substantially opaque regions formed thereon and configured in a pattern. The substrate may include, for example, a glass material such as quartz.”;
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wherein the controller includes artificial intelligence assisted object detection algorithms (Kolchin, col. 10, lines 24-36: “In some embodiments, a three-dimensional image is processed algorithmically to identify and classify defects of interest. In some examples, processor 131 is configured to detect and classify defects from a three-dimensional image. The processor may include any appropriate processor known in the art. In addition, the processor may be configured to use any appropriate defect detection and classification algorithm or method known in the art. For example, the processor may use a die-to-database comparison, a three-dimensional filter, a clustering algorithm such as a principal component analysis or spectral clustering, a thresholding algorithm, a deep learning algorithm, or any other suitable algorithm to detect and classify defects on the specimen.”; Kolchin teaches using deep learning for identification and classification of defects in semiconductor wafer/substrates; the term “deep learning” meets the broadest reasonable interpretation of the claim term “artificial intelligence assisted object detection algorithm”; the 3D visualization of the wafer/substrate).
Kolchin fails to teach
a fourth detector positioned at a fourth location configured to receive a fourth signal reflected from the object; and analyze the fourth signal to produce a fourth image of the object including the defect on the object.
Aiyer teaches
a fourth detector positioned at a fourth location configured to receive a fourth signal reflected from the object; and analyze the fourth signal to produce a fourth image of the object including the defect on the object (Aiyer, col. 5, lines 7-13; col. 2, lines 22-25; col. 5, lines 24-33; FIG. 7: “Further, in one embodiment of this apparatus as shown in more detail in FIG. 7, several cameras (detectors) are used. The multiple CCD (charge coupled device) video cameras 48A, 48B, 48C, 48D in this embodiment are positioned such that each camera receives the designated first order beam 12 from one grating type. The use of CCD cameras as the detectors is illustrative, not limiting … Multiple CCD cameras 48A, 48B, 48C, 48D (the detectors) are mounted on a guide beam 52 which in turn is held by vertical support 54. The number of cameras needed for semiconductor industry standard Class 2 defect inspection depends on the number of grating types that are to be inspected.”; “In another embodiment where a particular grating includes features of several different pitches (patterns), the detector is moved to several predetermined angles to inspect each of the different patterns. In another embodiment, several different detectors are located at different angles of reflective diffraction to receive light diffracted from the various patterns.”; “Cameras 48A, . . . , 48D provide their output signals (via camera multiplexer 58) to a commercially available type image processor unit 60. Through camera multiplexing, automatic inspection of different grating types is achieved with a single image processor unit 60. The position of each camera 48A, . . . , 48D in the vertical plane varies in this example from 500 mm to 600 mm from the plane of wafer 10. (For clarity, only four of the five cameras used in one embodiment are shown in FIG. 7. The X-Y-Z axes are shown for orientation purposes.)”;
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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 1) the apparatus, as taught by Kolchin, to include a fourth detector positioned at a fourth location configured to receive a fourth signal reflected from the object, as taught by Aiyer, and 2) the controller, as taught by Kolchin, to be configured to analyze the fourth signal to produce a fourth image of the object including the defect on the object, as taught by Aiyer.
The suggestion/motivation for doing so would have been to provide images of a substrate at numerous angles, which allows for more morphological information of the substrate to be known, such as height and depth of a wafer; more morphological information of the substrate leads to stronger image analysis of the substrate to find defects in the substrate.
Kolchin, in view of Aiyer, fails to teach
wherein the first, second, and third signals include signals from secondary electrons radiated from the object upon light signal emitted from the signal source opposite of the object contacts the object.
Bauer teaches
wherein the first, second, and third signals include signals from secondary electrons radiated from the object upon light signal emitted from the signal source opposite of the object contacts the object (Bauer, para. [0201]; para. [0203]; FIG. 24: “The backscattered electrons and secondary electrons generated in an interaction region or a scattering cone of the sample 2425 by the electron beam 2415 are registered by the detector 2417. The detector 2417 that is arranged in the electron column 2420 is referred to as an “in lens detector.” The detector 2417 can be installed in the column 2420 in various embodiments. The detector 2417 converts the secondary electrons generated by the electron beam 2415 at the measurement point 2422 and/or the electrons backscattered from the sample 2425 into an electrical measurement signal and transmits the latter to an evaluation unit 2480 of the apparatus 2400. The evaluation unit 2480 analyzes the measurement signals from the detectors 2417 and 2419 and generates an image of the sample 2425 therefrom, said image being displayed on the display 2495 of the evaluation unit 2480. The detector 2417 can additionally contain a filter or a filter system in order to discriminate the electrons in terms of energy and/or solid angle (not represented in FIG. 24”; “Further, the apparatus 2400 can comprise a third detector (not illustrated in FIG. 24). The third detector can be embodied in the form of an Everhart-Thornley detector and is typically arranged outside the column 2420. In general, it is used to detect secondary electrons.”;
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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, second, and third signals, as taught by Kolchin, in view of Aiyer, to include signals from secondary electrons radiated from the object upon signals emitted from a signal source opposite of the object contacts the object, as taught by Bauer.
The suggestion/motivation for doing so would have been that “the second parameter that determines the resolution limit when producing a repair element is the interaction region or the scattering cone of the secondary electrons generated by a particle beam having mass.” (Bauer, para. [0081]; therefore, detecting secondary electrons scattering off the object on a substrate leads to generating a more accurate image of the object at a more easily viewable resolution.
Therefore, it would have been obvious to combine Kolchin, with Aiyer and Bauer, to obtain the invention as specified in claim 1.
Regarding claim 2, Kolchin, in view of Aiyer, and in view of Bauer teaches the apparatus of claim 1, wherein the fourth signal includes signal from backscattered electrons radiated from the object upon light signal emitted from the signal source opposite of the object contacts the object (Bauer, para. [0201]; para. [0203]; FIG. 24; Aiyer, col. 5, lines 7-13; col. 2, lines 22-25; col. 5, lines 24-33; FIG. 7; see rejection of claim 1 above; the fourth detector taught by Aiyer is modified to be an additional Everhart-Thornley detector, as taught by Bauer in the rejection of claim 1 above that includes signal from backscattered electrons radiated from the object upon light signal emitted from the signal source opposite of the object contacts the object).
Regarding claim 3, Kolchin, in view Aiyer, and in view of Bauer, teaches the apparatus of claim 2, wherein the fourth signal includes composition information of the object (Aiyer, col. 5, lines 7-13; col. 2, lines 22-25; FIG. 7; see rejection of claim 1 above) and
the defect and the first, second, and third signals include morphology information of the object and the defect (Kolchin, col. 9, lines 13-27; FIG. 2; FIG. 3; see rejection of claim 1 above; Kolchin, col. 8, lines 41-53; col. 10, lines 11-13; FIG. 4: “In general, the optical subsystem, including both the illumination and collection subsystems, generates a focused optical image at each of a plurality of focus planes located at a plurality of different depths of a structure under measurement (e.g., a vertically stacked structure). The alignment of the focus plane of the optical subsystem at each different depth is achieved by optical adjustment that moves the focus plane in the z-direction, specimen positioning in the z-direction, or both. One or more detectors detect the light collected at each of the plurality of different depths and generate a plurality of output signals indicative of the amount of light collected at each of the plurality of different depths.”; morphological information are aspects like size, weight, height, etc. which are seen in the 3D image generated; “For example, FIG. 4 depicts a three dimensional contour map 152 of a measured signal through an inspected volume that can be presented to an operator for analysis.”;
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Regarding claim 4, Kolchin, in view Aiyer, and in view of Bauer, teaches the apparatus of claim 3, wherein the controller is further configured to: determine morphology information of the defect based on the first, second, third images of the object (Kolchin, col. 10, lines 37-60; col. 14, lines 11-27: “In another aspect, the three-dimensional location of a defect of interest is determined based on an analysis of the three-dimensional image of a thick semiconductor structure. In this manner, the actual position of a defect within a wafer is measured (e.g., {x, y, z} coordinates of the defect). The actual defect position can be used to locate the defect later for further analysis (e.g., analysis by a focused ion beam system, EBI system, etc. In many dark field measurement applications, the diffraction orders are suppressed and the actual defect location in the z-direction (e.g., depth) is linearly related to the focus offset associated with the peak signal. For many cases of incoherent BF illumination, the defect location in the z-direction is linearly related to the focus offset associated with the peak signal”; the defect images take into account morphological information of the wafer/substrate such as depth of the defect found by certain light reflected off the substrate detected by the detectors, as well as the structural makeup the defect itself; “In another embodiment, defects can be detected at stack depths that are as large as about eight micrometers. The thickness of a vertical ONON or OPOP stack under inspection is limited only by the depth of penetration of the illumination light. Transmission through an oxide-nitride-oxide-nitrite (ONON) or oxide-polysilicon-oxide-polysilicon (OPOP) stack is limited less by absorption at longer wavelengths. Thus, longer illumination wavelengths may be employed to effectively inspect very deep structures.”; this means that composition information is known in the image of the substrate and the defect because different wavelengths are chosen for the illumination light onto the substrate depending on the type of material the substrate is made of; different wavelength light will be detected by the detectors reflecting off the substrate to generate a variety of images depending on the composition of the material of the substrate and the defect in the substrate);
determine composition information of the defect based on the fourth image of the object (Aiyer, col. 5, lines 7-13; col. 2, lines 22-25; FIG. 7; see rejection of claim 1 above; see rejection of claim 2 reciting wherein the fourth signal includes composition information of the object; Kolchin, col. 10, lines 37-60; col. 14, lines 11-27; see rejection above; Kolchin teaches finding both composition and morphology information of the defects in the semiconductor substrate from images which is extended to the fourth image taken by the fourth detector taught by Aiyer); and
identify a defect type based on the first, second, third, and fourth images of the object (Kolchin, col. 10, lines 24-36: “In some embodiments, a three-dimensional image is processed algorithmically to identify and classify defects of interest. In some examples, processor 131 is configured to detect and classify defects from a three-dimensional image. The processor may include any appropriate processor known in the art. In addition, the processor may be configured to use any appropriate defect detection and classification algorithm or method known in the art. For example, the processor may use a die-to-database comparison, a three-dimensional filter, a clustering algorithm such as a principal component analysis or spectral clustering, a thresholding algorithm, a deep learning algorithm, or any other suitable algorithm to detect and classify defects on the specimen.”).
Regarding claim 7, Kolchin, in view of Aiyer, and in view of Bauer, teaches the apparatus of claim 1, wherein the first, second, third, and fourth locations are different from each other (Kolchin, col. 5, lines 15-26; col. 6, lines 48-51; FIG 1; Aiyer, col. 5, lines 7-13; col. 2, lines 22-25; col. 5, lines 24-33; FIG. 7; see rejection of claim 1 above; FIG. 1 of Kolchin shows three detectors in different locations; Fig. 7 of Aiyer shows four detectors at different locations and angles).
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Kolchin, in view of Aiyer, Bauer, and in view of Chinese Patent Application Publication No.: CN 109142371 A (Luo et al.) (hereinafter Luo).
Regarding claim 5, Kolchin, in view Aiyer, and in view of Bauer, teaches the apparatus of claim 4, wherein the controller is further configured to: determine morphology information of the defect based on the first, second, third images of the object (Kolchin, col. 10, lines 37-60; col. 14, lines 11-27: “In another aspect, the three-dimensional location of a defect of interest is determined based on an analysis of the three-dimensional image of a thick semiconductor structure. In this manner, the actual position of a defect within a wafer is measured (e.g., {x, y, z} coordinates of the defect). The actual defect position can be used to locate the defect later for further analysis (e.g., analysis by a focused ion beam system, EBI system, etc. In many dark field measurement applications, the diffraction orders are suppressed and the actual defect location in the z-direction (e.g., depth) is linearly related to the focus offset associated with the peak signal. For many cases of incoherent BF illumination, the defect location in the z-direction is linearly related to the focus offset associated with the peak signal”; the defect images take into account morphological information of the wafer/substrate such as depth of the defect found by certain light reflected off the substrate detected by the detectors, as well as the structural makeup the defect itself; “In another embodiment, defects can be detected at stack depths that are as large as about eight micrometers. The thickness of a vertical ONON or OPOP stack under inspection is limited only by the depth of penetration of the illumination light. Transmission through an oxide-nitride-oxide-nitrite (ONON) or oxide-polysilicon-oxide-polysilicon (OPOP) stack is limited less by absorption at longer wavelengths. Thus, longer illumination wavelengths may be employed to effectively inspect very deep structures.”; this means that composition information is known in the image of the substrate and the defect because different wavelengths are chosen for the illumination light onto the substrate depending on the type of material the substrate is made of; different wavelength light will be detected by the detectors reflecting off the substrate to generate a variety of images depending on the composition of the material of the substrate and the defect in the substrate);
determine composition information of the defect based on the fourth image of the object (Aiyer, col. 5, lines 7-13; col. 2, lines 22-25; FIG. 7; see rejection of claim 1 above; see rejection of claim 2 reciting wherein the fourth signal includes composition information of the object; Kolchin, col. 10, lines 37-60; col. 14, lines 11-27; see rejection above; Kolchin teaches finding both composition and morphology information of the defects in the semiconductor substrate from images which is extended to the fourth image taken by the fourth detector taught by Aiyer); and
identify a defect type based on the first, second, third, and fourth images of the object (Kolchin, col. 10, lines 24-36: “In some embodiments, a three-dimensional image is processed algorithmically to identify and classify defects of interest. In some examples, processor 131 is configured to detect and classify defects from a three-dimensional image. The processor may include any appropriate processor known in the art. In addition, the processor may be configured to use any appropriate defect detection and classification algorithm or method known in the art. For example, the processor may use a die-to-database comparison, a three-dimensional filter, a clustering algorithm such as a principal component analysis or spectral clustering, a thresholding algorithm, a deep learning algorithm, or any other suitable algorithm to detect and classify defects on the specimen.”).
Kolchin, in view of Aiyer, and in view of Bauer, fails to teach
wherein the artificial intelligence assisted object detection algorithms include a YOLO (You Only Look Once) model.
Luo teaches
wherein the artificial intelligence assisted object detection algorithms include a YOLO (You Only Look Once) model (Luo, abstract: “The invention discloses high density flexible exterior substrate defect detecting system and method based on deep learning, system includes hardware platform and software detection platform. Detection method includes the following steps: collecting the FICS image for containing different defects as training sample; Sample image is pre-processed, including is unified into standard size and handmarking is carried out to the defects of sample position and classification; Sample image input is trained based on the deep learning model for improving YOLO convolutional neural networks, obtains the model parameter that output is defective locations and classification; It inputs in trained deep learning model and detects after being standardized to the picture size of acquisition, obtain the defects of acquired image position and classification information.”).
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 artificial intelligence assisted object detection algorithm, as taught by Kolchin, in view Aiyer, and in view of Bauer, to include a YOLO (You Only Look Once) model, as taught by Luo.
The suggestion/motivation for doing so would have been so “quick positioning and type identification of high density flexible exterior substrate defect can be achieved” (Luo, abstract) which “solves traditional shortcoming detection system where method speed is slow, it is difficult to realize the quick detection problem of high density FICS open defect” (Luo, abstract).
Therefore, it would have been obvious to combine Kolchin, Aiyer, and Bauer, with Luo, to obtain the invention as specified in claim 5.
Regarding claim 6, Kolchin, in view of Aiyer, in view of Bauer, and in view of Luo, teaches the apparatus of claim 5.
Kolchin, in view of Aiyer, in view of Bauer, and in view of Luo, fails to teach
wherein the controller is further configured to: determine a type of material for repairing the defect type based on the morphology information and the composition information; and request repairing the defect of the object using the determined material.
Bauer further teaches
wherein the controller is further configured to: determine a type of material for repairing the defect type (Bauer, para. [0066]-[0067]; para. [0190]: “The at least one repair element produced can at least partly overlap the at least one defect. A deposited repair element can comprise a material of the lithographic mask. The deposited repair element can comprise: a metal, for instance chromium (Cr), a metal compound, for instance tantalum nitride (TaN), silicon (Si), silicon dioxide (SiO2) and molybdenum silicon oxynitride (MoxSiOyNz), wherein 0<x≤0.5, 0≤y≤2 and 0≤z≤4/3. An etched repair element can etch a material of the photolithographic mask. The etched repair element can comprise the mask materials mentioned above. The method defined above can comprise the steps of: (a) producing at least one repair element by use of at least one repair shape for which the parameters are defined by the at least one defect; and (b) ascertaining parameters of a repair shape for a remaining defect residue, wherein ascertaining parameters for the repair shape for the remaining defect residue comprises: allocating at least one numerical value to a parameter which deviates from the numerical value predefined by the remaining defect residue for said parameter.; “Alternatively or additionally, a machine learning model, for a repair shape that has already been parametrized, can allocate a different numerical value to one or more parameters for the purpose of ascertaining the above-described repair elements 410, 510, 610 according to the invention. The currently preferred embodiment, however, is that, from the input data indicated above, a machine learning model directly predicts the parameters of a repair shape for the purpose of forming one of the repair elements 410, 510, 610 described in this application. The process of training a machine learning model will not be discussed in this application.”; a photolithographic mask or a “reticle” is a substrate) based on the morphology information and the composition information (Bauer, para. [0213]; para. [0070]: “As already explained above, an electron beam 2415 can be focused to a spot diameter in the range of a few nanometers. The interaction region or the scattering cone in which an electron beam 2415 generates secondary electrons depends firstly on the energy of the electron beam 2415 and secondly on the material composition on which the electron beam 2415 impinges. The diameters of interaction regions attain values in the low single-digit nanometer range. The diameter of a scattering cone of an electron beam 2415 thus limits the achievable resolution limit during the generation of a repair element 410, 510, 610 by implementing the corresponding repair shape. Said resolution limit at the present time is in the single-digit nanometer range.”; “The at least one repair element produced can have in at least one dimension a dimensional size which is smaller than the resolution limit R of the photomask. As already explained above, the averaging of the actinic radiation over structures with dimensional sizes below the resolution capability of the mask results in a reduced effect of a placement error of a repair element. This circumstance significantly facilitates the positioning of the repair element(s) in relation to the position of a defect to be repaired. Furthermore, by virtue of the non-imaging of the repair element(s), the geometric shape(s) thereof can deviate from the shape of the defect in a significant way, without the compensation of the defect being adversely influenced in an appreciable way. This fact considerably simplifies the repair or the compensation of small defects.”); and
request repairing the defect of the object using the determined material (Bauer, para. [0066]-[0067]; see above; para. [0054]; para. [0210]; FIG. 24: “Producing the at least one repair element can comprise: carrying out at least one local etching process and/or carrying out at least one local deposition process by use of at least one focused particle beam and at least one precursor gas.”; “The gas providing system 2390 realized by the apparatus 2400 is discussed below. As already explained above, the sample 2425 is arranged on a sample stage 2430. The imaging elements of the column 2420 of the SEM 2410 can focus the electron beam 2415 and scan the latter over the sample 2525. The electron beam 2415 of the SEM 2410 can be used to induce a particle beam-induced deposition process (EBID, electron beam induced deposition) and/or a particle beam-induced etching process (EBIE, electron beam induced etching). The exemplary apparatus 2400 in FIG. 24 has three different supply containers 2440, 2450 and 2460, for storing various precursor gases, for the purposes of carrying out these processes.”;
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particle beam 2350 shown in system FIG. 23 is combined with gas providing system shown in FIG. 24 above).
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 controller, as taught by Kolchin, in view of Aiyer, in view of Bauer, and in view of Luo, to be further configured to determine a type of material for repairing the defect type based on the morphology information and the composition information, and request repairing the defect of the object using the determined material, as further taught by Bauer.
The suggestion/motivation for doing so would have been that “furthermore, the repair of increasingly smaller defects is becoming more and more difficult. Firstly, the positioning of a repair tool relative to an identified defect is possible only with very complex metrology and, secondly, setting the repair tool to a specific small defect requires a high expenditure of time.” (Bauer, para. [0007]).
Therefore, it would have been obvious to combine Kolchin, Aiyer, Bauer, and Luo, with Bauer further, to obtain the invention as specified in claim 6.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Kolchin, in view of Aiyer, Bauer, and in view of U.S. Patent Application Publication No.: 2019/0131120 (Yamaguchi).
Regarding claim 8, Kolchin, in view of Aiyer, and in view of Bauer, teaches the apparatus of claim 1, wherein the fourth signal includes composition information of the object ((Aiyer, col. 5, lines 7-13; col. 2, lines 22-25; FIG. 7; see rejection of claim 1 above; Kolchin, col. 10, lines 37-60; col. 14, lines 11-27; see rejection above; Kolchin teaches finding both composition and morphology information of the defects in the semiconductor substrate from images which is extended to the fourth image taken by the fourth detector taught by Aiyer) and
the defect and the first, second, and third signals include morphology information of the object and the defect (Kolchin, col. 9, lines 13-27; FIG. 2; FIG. 3; see rejection of claim 1 above; Kolchin, col. 8, lines 41-53; col. 10, lines 11-13; FIG. 4: “In general, the optical subsystem, including both the illumination and collection subsystems, generates a focused optical image at each of a plurality of focus planes located at a plurality of different depths of a structure under measurement (e.g., a vertically stacked structure). The alignment of the focus plane of the optical subsystem at each different depth is achieved by optical adjustment that moves the focus plane in the z-direction, specimen positioning in the z-direction, or both. One or more detectors detect the light collected at each of the plurality of different depths and generate a plurality of output signals indicative of the amount of light collected at each of the plurality of different depths.”; morphological information are aspects like size, weight, height, etc. which are seen in the 3D image generated; “For example, FIG. 4 depicts a three dimensional contour map 152 of a measured signal through an inspected volume that can be presented to an operator for analysis.”;
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Kolchin, in view of Aiyer, and in view of Bauer, fails to teach
an etching gas supplier configured to supply an etchant gas, wherein the etching gas supplier is operatively coupled to the controller.
Yamaguchi teaches
an etching gas supplier configured to supply an etchant gas, wherein the etching gas supplier is operatively coupled to the controller (Yamaguchi, para. [0044]-[0045]; FIG. 2: "Here, in the case where the mixed etching gas of the complex forming gas and the complex stabilizing material gas is introduced into the processing chamber 11 in the state in which the temperature of the semiconductor substrate 1 exceeds the predetermined gas introduction upper limit temperature T1, the lanthanum silicate film which is close to a mixed etching gas supply port and thus the thickness of the lanthanum silicate film at a place where the gas concentration is high is quickly reduced, whereas the defect that the thickness of the lanthanum silicate film at a place where the gas concentration is low such as a place away from the etching gas supply port or a bottom of a deep hole is not greatly reduced is apt to occur. To minimize the occurrence of the defects, the mixed etching gas is introduced into the processing chamber 11 after waiting until the temperature of the semiconductor substrate 1 falls below the predetermined gas introduction upper limit temperature T1 .The gas introduction upper limit temperature T1 is affected by various factors such as the dimension of the wafer 1, the material of the wafer, the film structure and film composition of the high-k insulating film, the composition of the mixed etching gas, the film thickness or the opening dimension of the resist film or the hard mask film, and the like. For this reason, there is a need to check and set the gas introduction upper limit temperature T1 beforehand for each semiconductor device to be processed."; as shown in FIG. 2, the etching gas supply is connected to a controller/processor;
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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 apparatus, as taught by Kolchin, in view of Aiyer, and in view of Bauer, to include an etching gas supplier configured to supply an etchant gas, wherein the etching gas supplier is operatively coupled to the controller, as taught by Yamaguchi.
The suggestion/motivation for doing so would have been that precise control of etching gas composition is crucial in semiconductor manufacturing because it directly impacts the etch rate, selectivity, and profile of the etched features, which ultimately determine the performance and functionality of the devices; adjusting the gas mixture and flow rates ensures the correct etching of specific materials while protecting others, leading to the desired 3D structure of the chip.
Therefore, it would have been obvious to combine Kolchin, Aiyer, and Bauer, with Yamaguchi, to obtain the invention as specified in claim 8.
Regarding claim 9, Kolchin, in view of Aiyer, in view of Bauer, and in view of Yamaguchi, teaches the apparatus of claim 8, wherein the controller is further configured to: identify a defect type based on the first, second, third, and fourth images of the object (Kolchin, col. 10, lines 24-36: “In some embodiments, a three-dimensional image is processed algorithmically to identify and classify defects of interest. In some examples, processor 131 is configured to detect and classify defects from a three-dimensional image. The processor may include any appropriate processor known in the art. In addition, the processor may be configured to use any appropriate defect detection and classification algorithm or method known in the art. For example, the processor may use a die-to-database comparison, a three-dimensional filter, a clustering algorithm such as a principal component analysis or spectral clustering, a thresholding algorithm, a deep learning algorithm, or any other suitable algorithm to detect and classify defects on the specimen.”)
Kolchin, in view of Aiyer, in view of Bauer, and in view of Yamaguchi, fails to teach
determine a type of etchant gas for repairing the defect type based on the morphology information and the composition information.
Yamaguchi further teaches
determine a type of etchant gas for repairing the defect type based on the morphology information and the composition information (Yamaguchi, para. [0026]-[0027]: “(1) It is possible to easily adjust the mixing ratio of the mixed etching gas by vaporizing the single chemical stock liquid in the vaporizers of each of the plurality of systems or the vaporizer of at least one system. For example, in the case where first mixed etching gas of gas A and gas B is generated from the vaporizer of the first system, etching gas of gas C is generated from the vaporizer of the second system, the first mixed etching gas acts on a first high-k insulating film material, and second mixed etching gas in which the gas C is also mixed with the first mixed etching gas acts on a second high-k insulating film material (which has an element composition different from that of the first high-k insulating film material), the vaporizer of the first system is used when the first high-k insulating film layer of the semiconductor device is etched and the vaporizers of the first system and the second system are used together when the second high-k insulating film layer is etched, such that it is possible to easily etch the high-k insulating film layer having a plurality of different materials included in the semiconductor device. 2) In the case where the mixed chemical liquid is vaporized by each of the vaporizers of the plurality of systems, for example, the vaporizer of the first system can generate the first mixed etching gas of the gas A and the gas B and the vaporizer of the second system can generate third mixed etching gas of the gas C and the gas B. In this case, if both the first mixed etching gas and the third mixed etching gas act on the same high-k insulating film material, it may be considered that the vaporizers of the first system and the second system are used together to etch the predetermined high-k insulating film layer of the semiconductor device. In addition, when the first mixed etching gas acts on the first high-k insulating film material and the third mixed etching gas acts on the second high-k insulating film material (which has the element composition different from that of the first high-k insulating film material), the vaporizer of the first system is used when the first high-k insulating film layer of the semiconductor device is etched and the vaporizer of the second system are switched and used when the second high-k insulating film layer is etched, such that it is possible to easily etch the high-k insulating film layer having the plurality of different materials included in the semiconductor device.”).
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 controller, as taught by Kolchin, in view of Aiyer, in view of Bauer, and in view of Yamaguchi, to be configured to determine a type of etchant gas for repairing the defect type based on the morphology information and the composition information, as further taught by Yamaguchi.
The suggestion/motivation for doing so would have been that choosing the optimal etching gas is critical in semiconductor fabrication to achieve precise pattern transfer; the primary benefits of matching the gas to the substrate’s morphology and composition include achieving vertical sidewalls, ensuring high selectivity, and preventing underlying layer damage.
Therefore, it would have been obvious to combine Kolchin, Aiyer, Bauer, and Yamaguchi, with Yamaguchi further, to obtain the invention as specified in claim 9.
Regarding claim 10, Kolchin, in view of Aiyer, in view of Bauer, and in view of Yamaguchi, teaches the apparatus of claim 9, wherein the controller is further configured to: request the etching gas supplier to provide the determined type of etchant gas; and repairing the defect of the object using the determined type of etchant gas (Yamaguchi, para. [0044]-[0045]; FIG. 2; see rejection of claim 8 above; the etching gas is released to fix the defects in the semiconductor wafer; see FIG. 2 showing the control device 100 controlling adding the gas to the container 10 having the processing chamber 11 with the wafer state 12 holding the semiconductor wafer).
Claims 11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Kolchin, in view of Bauer.
Regarding claim 11, Kolchin teaches an apparatus comprising: a signal source configured to emit light signal to an object on a substrate; a first detector positioned at a first location configured to receive a first signal reflected from the object; a second detector positioned at a second location configured to receive a second signal reflected from the object; a third detector positioned at a third location configured to receive a third signal reflected from the object (Kolchin, col. 5, lines 15-26; col. 6, lines 48-51; FIG 1: “FIG. 1 is a simplified schematic view of one embodiment of an optical inspection system 100 configured to perform detection and classification of defects of interest (DOI) on semiconductor wafers based on three-dimensional images. Optical inspection system 100 includes an illumination subsystem, a collection subsystem, one or more detectors, and a computing system. The illumination subsystem includes an illumination source 101 and all optical elements in the illumination optical path from the illumination source to the wafer. The collection subsystem includes all optical elements in the collection optical path from the specimen to each detector.; “Each of detectors 115, 120, and 125 generally function to convert the reflected and scattered light into an electrical signal, and therefore, may include substantially any photodetector known in the art.”;
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a controller configured to: (Kolchin, col. 7, lines 35-49: “System 100 also includes various electronic components (not shown) needed for processing the reflected and/or scattered signals detected by any of detectors 115, 120, and 125. For example, system 100 may include amplifier circuitry to receive output signals from any of detectors 115, 120, and 125 and to amplify those output signals by a predetermined amount and an analog-to-digital converter (ADC) to convert the amplified signals into a digital format suitable for use within processor 131.”)
analyze the first signal to produce a first image of the object including a defect on the object; analyze the second signal to produce a second image of the object including the defect on the object; and analyze the third signal to produce a third image of the object including the defect on the object (Kolchin, col. 6, lines 31-37; col. 9, lines 13-27; col. 14, lines 54-65; FIG. 2; FIG. 3: “System 100 includes collection optics 116, 117, and 118 to collect the light scattered and/or reflected by wafer 103 and focus that light onto detector arrays 115, 120, and 125, respectively. The outputs of detectors 115, 120, and 125 are communicated to computing system 130 for processing the signals and determining the presence of defects and their locations.”; “In one aspect, a three-dimensional image of a thick semiconductor structure is generated from a volume measured in two lateral dimensions (e.g., parallel to the wafer surface) and a depth dimension (e.g., normal to the wafer surface). In the embodiment depicted in FIG. 1, computing system 130 arranges the outputs from one or more of the measurement channels (e.g., from one or more of detectors 115, 120, and 125) into a volumetric data set that corresponds to the measured volume. FIG. 2 depicts a plot 150 of a cross-sectional view (y=0) of a measured three-dimensional image illustrating a peak signal near a focus offset of −0.5 micrometers. FIG. 3 depicts a plot 151 of another cross-sectional view (x=0) of the measured three-dimensional image also illustrating a peak signal near a focus offset of −0.5 micrometers.”; “As used herein, the term “wafer” generally refers to substrates formed of a semiconductor or non-semiconductor material … a “reticle” may be a reticle at any stage of a reticle fabrication process, or a completed reticle that may or may not be released for use in a semiconductor fabrication facility. A reticle, or a “mask,” is generally defined as a substantially transparent substrate having substantially opaque regions formed thereon and configured in a pattern. The substrate may include, for example, a glass material such as quartz.”;
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wherein the controller includes artificial intelligence assisted object detection algorithms, and wherein the first, second, and third locations are different from each other (Kolchin, col. 10, lines 24-36: “In some embodiments, a three-dimensional image is processed algorithmically to identify and classify defects of interest. In some examples, processor 131 is configured to detect and classify defects from a three-dimensional image. The processor may include any appropriate processor known in the art. In addition, the processor may be configured to use any appropriate defect detection and classification algorithm or method known in the art. For example, the processor may use a die-to-database comparison, a three-dimensional filter, a clustering algorithm such as a principal component analysis or spectral clustering, a thresholding algorithm, a deep learning algorithm, or any other suitable algorithm to detect and classify defects on the specimen.”; Kolchin teaches using deep learning for identification and classification of defects in semiconductor wafer/substrates; the term “deep learning” meets the broadest reasonable interpretation of the claim term “artificial intelligence assisted object detection algorithm”; the 3D visualization of the wafer/substrate).
Kolchin fails to teach
wherein the first, second, and third signals include signals from secondary electrons radiated from the object upon light signal emitted from the signal source opposite of the object contacts the object.
Bauer teaches
wherein the first, second, and third signals include signals from secondary electrons radiated from the object upon light signal emitted from the signal source opposite of the object contacts the object (Bauer, para. [0201]; para. [0203]; FIG. 24: “The backscattered electrons and secondary electrons generated in an interaction region or a scattering cone of the sample 2425 by the electron beam 2415 are registered by the detector 2417. The detector 2417 that is arranged in the electron column 2420 is referred to as an “in lens detector.” The detector 2417 can be installed in the column 2420 in various embodiments. The detector 2417 converts the secondary electrons generated by the electron beam 2415 at the measurement point 2422 and/or the electrons backscattered from the sample 2425 into an electrical measurement signal and transmits the latter to an evaluation unit 2480 of the apparatus 2400. The evaluation unit 2480 analyzes the measurement signals from the detectors 2417 and 2419 and generates an image of the sample 2425 therefrom, said image being displayed on the display 2495 of the evaluation unit 2480. The detector 2417 can additionally contain a filter or a filter system in order to discriminate the electrons in terms of energy and/or solid angle (not represented in FIG. 24”; “Further, the apparatus 2400 can comprise a third detector (not illustrated in FIG. 24). The third detector can be embodied in the form of an Everhart-Thornley detector and is typically arranged outside the column 2420. In general, it is used to detect secondary electrons.”;
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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, second, and third signals, as taught by Kolchin, to include signals from secondary electrons radiated from the object upon signals emitted from a signal source opposite of the object contacts the object, as further taught by Bauer.
The suggestion/motivation for doing so would have been that “the second parameter that determines the resolution limit when producing a repair element is the interaction region or the scattering cone of the secondary electrons generated by a particle beam having mass.” (Bauer, para. [0081]; therefore, detecting secondary electrons scattering off the object on a substrate leads to generating a more accurate image of the object at a more easily viewable resolution.
Therefore, it would have been obvious to combine Kolchin, with Bauer, to obtain the invention as specified in claim 11.
Regarding claim 13, Kolchin, in view of Bauer, teaches the apparatus of claim 11, wherein the first, second, and third signals include morphology information of the object and the defect (Kolchin, col. 9, lines 13-27; FIG. 2; FIG. 3; see rejection of claim 1 above; Kolchin, col. 8, lines 41-53; col. 10, lines 11-13; FIG. 4: “In general, the optical subsystem, including both the illumination and collection subsystems, generates a focused optical image at each of a plurality of focus planes located at a plurality of different depths of a structure under measurement (e.g., a vertically stacked structure). The alignment of the focus plane of the optical subsystem at each different depth is achieved by optical adjustment that moves the focus plane in the z-direction, specimen positioning in the z-direction, or both. One or more detectors detect the light collected at each of the plurality of different depths and generate a plurality of output signals indicative of the amount of light collected at each of the plurality of different depths.”; morphological information are aspects like size, weight, height, etc. which are seen in the 3D image generated; “For example, FIG. 4 depicts a three dimensional contour map 152 of a measured signal through an inspected volume that can be presented to an operator for analysis.”;
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Claims 12 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Kolchin, in view of Bauer, and in view of Aiyer.
Regarding claim 12, Kolchin, in view of Bauer, teaches the apparatus of claim 11.
Kolchin, in view of Bauer, fails to teach
a fourth detector positioned at a fourth location configured to receive a fourth signal reflected from the object, wherein the controller is further configured to analyze the fourth signal to produce a fourth image of the object including the defect on the object.
Aiyer teaches
a fourth detector positioned at a fourth location configured to receive a fourth signal reflected from the object, wherein the controller is further configured to analyze the fourth signal to produce a fourth image of the object including the defect on the object (Aiyer, col. 5, lines 7-13; col. 2, lines 22-25; col. 5, lines 24-33; FIG. 7: “Further, in one embodiment of this apparatus as shown in more detail in FIG. 7, several cameras (detectors) are used. The multiple CCD (charge coupled device) video cameras 48A, 48B, 48C, 48D in this embodiment are positioned such that each camera receives the designated first order beam 12 from one grating type. The use of CCD cameras as the detectors is illustrative, not limiting … Multiple CCD cameras 48A, 48B, 48C, 48D (the detectors) are mounted on a guide beam 52 which in turn is held by vertical support 54. The number of cameras needed for semiconductor industry standard Class 2 defect inspection depends on the number of grating types that are to be inspected.”; “In another embodiment where a particular grating includes features of several different pitches (patterns), the detector is moved to several predetermined angles to inspect each of the different patterns. In another embodiment, several different detectors are located at different angles of reflective diffraction to receive light diffracted from the various patterns.”; “Cameras 48A, . . . , 48D provide their output signals (via camera multiplexer 58) to a commercially available type image processor unit 60. Through camera multiplexing, automatic inspection of different grating types is achieved with a single image processor unit 60. The position of each camera 48A, . . . , 48D in the vertical plane varies in this example from 500 mm to 600 mm from the plane of wafer 10. (For clarity, only four of the five cameras used in one embodiment are shown in FIG. 7. The X-Y-Z axes are shown for orientation purposes.)”;
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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 1) the apparatus, as taught by Kolchin, in view of Bauer, to include a fourth detector positioned at a fourth location configured to receive a fourth signal reflected from the object, as taught by Aiyer, and 2) the controller, as taught by Kolchin, in view of Bauer, to be configured to analyze the fourth signal to produce a fourth image of the object including the defect on the object, as taught by Aiyer.
The suggestion/motivation for doing so would have been to provide images of a substrate at numerous angles, which allows for more morphological information of the substrate to be known, such as height and depth of a wafer; more morphological information of the substrate leads to stronger image analysis of the substrate to find defects in the substrate.
Therefore, it would have been obvious to combine Kolchin and Bauer, with Aiyer, to obtain the invention as specified in claim 12.
Regarding claim 14, Kolchin, in view of Bauer, and in view of Aiyer, teaches the apparatus of claim 12, wherein the fourth signal includes composition information of the object and the defect (Aiyer, col. 5, lines 7-13; col. 2, lines 22-25; FIG. 7; see rejection of claim 1 above discussing the four detectors and detecting defects in the semiconductor wafer structure by taking an image from the optical signals of the detectors).
Regarding claim 15, Kolchin, in view of Bauer, and in view of Aiyer, teaches the apparatus of claim 14, wherein the fourth signal includes signal from backscattered electrons radiated from the object upon light signal emitted from the signal source opposite of the object contacts the object (Bauer, para. [0201]; para. [0203]; FIG. 24; Aiyer, col. 5, lines 7-13; col. 2, lines 22-25; col. 5, lines 24-33; FIG. 7; see rejection of claim 1 above; the fourth detector taught by Aiyer is modified to be an additional Everhart-Thornley detector, as taught by Bauer in the rejection of claim 1 above that includes signal from backscattered electrons radiated from the object upon light signal emitted from the signal source opposite of the object contacts the object).
Claims 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Kolchin, in view of Bauer, in view of Aiyer, and in view of Luo.
Regarding claim 16, Kolchin, in view of Bauer, and in view of Aiyer teaches the apparatus of claim 15, wherein the controller is further configured to: determine morphology information of the defect based on the first, second, third images of the object (Kolchin, col. 10, lines 37-60; col. 14, lines 11-27: “In another aspect, the three-dimensional location of a defect of interest is determined based on an analysis of the three-dimensional image of a thick semiconductor structure. In this manner, the actual position of a defect within a wafer is measured (e.g., {x, y, z} coordinates of the defect). The actual defect position can be used to locate the defect later for further analysis (e.g., analysis by a focused ion beam system, EBI system, etc. In many dark field measurement applications, the diffraction orders are suppressed and the actual defect location in the z-direction (e.g., depth) is linearly related to the focus offset associated with the peak signal. For many cases of incoherent BF illumination, the defect location in the z-direction is linearly related to the focus offset associated with the peak signal”; the defect images take into account morphological information of the wafer/substrate such as depth of the defect found by certain light reflected off the substrate detected by the detectors, as well as the structural makeup the defect itself; “In another embodiment, defects can be detected at stack depths that are as large as about eight micrometers. The thickness of a vertical ONON or OPOP stack under inspection is limited only by the depth of penetration of the illumination light. Transmission through an oxide-nitride-oxide-nitrite (ONON) or oxide-polysilicon-oxide-polysilicon (OPOP) stack is limited less by absorption at longer wavelengths. Thus, longer illumination wavelengths may be employed to effectively inspect very deep structures.”; this means that composition information is known in the image of the substrate and the defect because different wavelengths are chosen for the illumination light onto the substrate depending on the type of material the substrate is made of; different wavelength light will be detected by the detectors reflecting off the substrate to generate a variety of images depending on the composition of the material of the substrate and the defect in the substrate);
determine composition information of the defect based on the fourth image of the object (Aiyer, col. 5, lines 7-13; col. 2, lines 22-25; FIG. 7; see rejection of claim 1 above; see rejection of claim 2 reciting wherein the fourth signal includes composition information of the object; Kolchin, col. 10, lines 37-60; col. 14, lines 11-27; see rejection above; Kolchin teaches finding both composition and morphology information of the defects in the semiconductor substrate from images which is extended to the fourth image taken by the fourth detector taught by Aiyer);
identify a defect type based on the first, second, third, and fourth images of the object (Kolchin, col. 10, lines 24-36: “In some embodiments, a three-dimensional image is processed algorithmically to identify and classify defects of interest. In some examples, processor 131 is configured to detect and classify defects from a three-dimensional image. The processor may include any appropriate processor known in the art. In addition, the processor may be configured to use any appropriate defect detection and classification algorithm or method known in the art. For example, the processor may use a die-to-database comparison, a three-dimensional filter, a clustering algorithm such as a principal component analysis or spectral clustering, a thresholding algorithm, a deep learning algorithm, or any other suitable algorithm to detect and classify defects on the specimen.”);
determine morphology information of the defect based on the first, second, third images of the object (Kolchin, col. 10, lines 37-60; col. 14, lines 11-27: “In another aspect, the three-dimensional location of a defect of interest is determined based on an analysis of the three-dimensional image of a thick semiconductor structure. In this manner, the actual position of a defect within a wafer is measured (e.g., {x, y, z} coordinates of the defect). The actual defect position can be used to locate the defect later for further analysis (e.g., analysis by a focused ion beam system, EBI system, etc. In many dark field measurement applications, the diffraction orders are suppressed and the actual defect location in the z-direction (e.g., depth) is linearly related to the focus offset associated with the peak signal. For many cases of incoherent BF illumination, the defect location in the z-direction is linearly related to the focus offset associated with the peak signal”; the defect images take into account morphological information of the wafer/substrate such as depth of the defect found by certain light reflected off the substrate detected by the detectors, as well as the structural makeup the defect itself; “In another embodiment, defects can be detected at stack depths that are as large as about eight micrometers. The thickness of a vertical ONON or OPOP stack under inspection is limited only by the depth of penetration of the illumination light. Transmission through an oxide-nitride-oxide-nitrite (ONON) or oxide-polysilicon-oxide-polysilicon (OPOP) stack is limited less by absorption at longer wavelengths. Thus, longer illumination wavelengths may be employed to effectively inspect very deep structures.”; this means that composition information is known in the image of the substrate and the defect because different wavelengths are chosen for the illumination light onto the substrate depending on the type of material the substrate is made of; different wavelength light will be detected by the detectors reflecting off the substrate to generate a variety of images depending on the composition of the material of the substrate and the defect in the substrate);
determine composition information of the defect based on the fourth image of the object (Aiyer, col. 5, lines 7-13; col. 2, lines 22-25; FIG. 7; see rejection of claim 1 above; see rejection of claim 2 reciting wherein the fourth signal includes composition information of the object; Kolchin, col. 10, lines 37-60; col. 14, lines 11-27; see rejection above; Kolchin teaches finding both composition and morphology information of the defects in the semiconductor substrate from images which is extended to the fourth image taken by the fourth detector taught by Aiyer); and
identify a defect type based on the first, second, third, and fourth images of the object (Kolchin, col. 10, lines 24-36: “In some embodiments, a three-dimensional image is processed algorithmically to identify and classify defects of interest. In some examples, processor 131 is configured to detect and classify defects from a three-dimensional image. The processor may include any appropriate processor known in the art. In addition, the processor may be configured to use any appropriate defect detection and classification algorithm or method known in the art. For example, the processor may use a die-to-database comparison, a three-dimensional filter, a clustering algorithm such as a principal component analysis or spectral clustering, a thresholding algorithm, a deep learning algorithm, or any other suitable algorithm to detect and classify defects on the specimen.”).
Kolchin, in view of Bauer, and in view Aiyer, fails to teach
wherein the artificial intelligence assisted object detection algorithms include a YOLO (You Only Look Once) model.
Luo teaches
wherein the artificial intelligence assisted object detection algorithms include a YOLO (You Only Look Once) model (Luo, abstract: “The invention discloses high density flexible exterior substrate defect detecting system and method based on deep learning, system includes hardware platform and software detection platform. Detection method includes the following steps: collecting the FICS image for containing different defects as training sample; Sample image is pre-processed, including is unified into standard size and handmarking is carried out to the defects of sample position and classification; Sample image input is trained based on the deep learning model for improving YOLO convolutional neural networks, obtains the model parameter that output is defective locations and classification; It inputs in trained deep learning model and detects after being standardized to the picture size of acquisition, obtain the defects of acquired image position and classification information.”).
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 artificial intelligence assisted object detection algorithm, as taught by Kolchin, in view of Bauer, and in view of Aiyer, to include a YOLO (You Only Look Once) model, as taught by Luo.
The suggestion/motivation for doing so would have been so “quick positioning and type identification of high density flexible exterior substrate defect can be achieved” (Luo, abstract) which “solves traditional shortcoming detection system where method speed is slow, it is difficult to realize the quick detection problem of high density FICS open defect” (Luo, abstract).
Therefore, it would have been obvious to combine Kolchin, Bauer, and Aiyer, with Luo, to obtain the invention as specified in claim 16.
Regarding claim 17, Kolchin, in view of Bauer, in view of Aiyer, and in view of Luo, teaches the apparatus of claim 16.
Kolchin, in view of Bauer, in view of Aiyer, and in view of Luo, fails to teach
wherein the controller is further configured to: determine a type of material for repairing the defect type based on the morphology information and the composition information; and request repairing the defect of the object using the determined material.
Bauer further teaches
wherein the controller is further configured to: determine a type of material for repairing the defect type (Bauer, para. [0066]-[0067]; para. [0190]: “The at least one repair element produced can at least partly overlap the at least one defect. A deposited repair element can comprise a material of the lithographic mask. The deposited repair element can comprise: a metal, for instance chromium (Cr), a metal compound, for instance tantalum nitride (TaN), silicon (Si), silicon dioxide (SiO2) and molybdenum silicon oxynitride (MoxSiOyNz), wherein 0<x≤0.5, 0≤y≤2 and 0≤z≤4/3. An etched repair element can etch a material of the photolithographic mask. The etched repair element can comprise the mask materials mentioned above. The method defined above can comprise the steps of: (a) producing at least one repair element by use of at least one repair shape for which the parameters are defined by the at least one defect; and (b) ascertaining parameters of a repair shape for a remaining defect residue, wherein ascertaining parameters for the repair shape for the remaining defect residue comprises: allocating at least one numerical value to a parameter which deviates from the numerical value predefined by the remaining defect residue for said parameter.; “Alternatively or additionally, a machine learning model, for a repair shape that has already been parametrized, can allocate a different numerical value to one or more parameters for the purpose of ascertaining the above-described repair elements 410, 510, 610 according to the invention. The currently preferred embodiment, however, is that, from the input data indicated above, a machine learning model directly predicts the parameters of a repair shape for the purpose of forming one of the repair elements 410, 510, 610 described in this application. The process of training a machine learning model will not be discussed in this application.”; a photolithographic mask or a “reticle” is a substrate) based on the morphology information and the composition information (Bauer, para. [0213]; para. [0070]: “As already explained above, an electron beam 2415 can be focused to a spot diameter in the range of a few nanometers. The interaction region or the scattering cone in which an electron beam 2415 generates secondary electrons depends firstly on the energy of the electron beam 2415 and secondly on the material composition on which the electron beam 2415 impinges. The diameters of interaction regions attain values in the low single-digit nanometer range. The diameter of a scattering cone of an electron beam 2415 thus limits the achievable resolution limit during the generation of a repair element 410, 510, 610 by implementing the corresponding repair shape. Said resolution limit at the present time is in the single-digit nanometer range.”; “The at least one repair element produced can have in at least one dimension a dimensional size which is smaller than the resolution limit R of the photomask. As already explained above, the averaging of the actinic radiation over structures with dimensional sizes below the resolution capability of the mask results in a reduced effect of a placement error of a repair element. This circumstance significantly facilitates the positioning of the repair element(s) in relation to the position of a defect to be repaired. Furthermore, by virtue of the non-imaging of the repair element(s), the geometric shape(s) thereof can deviate from the shape of the defect in a significant way, without the compensation of the defect being adversely influenced in an appreciable way. This fact considerably simplifies the repair or the compensation of small defects.”); and
request repairing the defect of the object using the determined material (Bauer, para. [0066]-[0067]; see above; para. [0054]; para. [0210]; FIG. 24: “Producing the at least one repair element can comprise: carrying out at least one local etching process and/or carrying out at least one local deposition process by use of at least one focused particle beam and at least one precursor gas.”; “The gas providing system 2390 realized by the apparatus 2400 is discussed below. As already explained above, the sample 2425 is arranged on a sample stage 2430. The imaging elements of the column 2420 of the SEM 2410 can focus the electron beam 2415 and scan the latter over the sample 2525. The electron beam 2415 of the SEM 2410 can be used to induce a particle beam-induced deposition process (EBID, electron beam induced deposition) and/or a particle beam-induced etching process (EBIE, electron beam induced etching). The exemplary apparatus 2400 in FIG. 24 has three different supply containers 2440, 2450 and 2460, for storing various precursor gases, for the purposes of carrying out these processes.”;
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particle beam 2350 shown in system FIG. 23 is combined with gas providing system shown in FIG. 24 above).
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 controller, as taught by Kolchin, in view of Bauer, in view of Aiyer, and in view of Luo, to be further configured to determine a type of material for repairing the defect type based on the morphology information and the composition information, and request repairing the defect of the object using the determined material, as further taught by Bauer.
The suggestion/motivation for doing so would have been that “furthermore, the repair of increasingly smaller defects is becoming more and more difficult. Firstly, the positioning of a repair tool relative to an identified defect is possible only with very complex metrology and, secondly, setting the repair tool to a specific small defect requires a high expenditure of time.” (Bauer, para. [0007]).
Therefore, it would have been obvious to combine Kolchin, Bauer, Aiyer, and Luo, with Bauer further, to obtain the invention as specified in claim 17.
Claims 18 is rejected under 35 U.S.C. 103 as being unpatentable over Kolchin, in view of Bauer, and in view of Luo.
Regarding claim 18, Kolchin teaches an apparatus comprising: a signal source configured to emit light signal to an object; a first detector positioned at a first location configured to receive a first signal reflected from the object; a second detector positioned at a second location configured to receive a second signal reflected from the object; a fourth detector positioned at a fourth location configured to receive a fourth signal reflected from the object (Kolchin, col. 5, lines 15-26; col. 6, lines 48-51; FIG 1: “FIG. 1 is a simplified schematic view of one embodiment of an optical inspection system 100 configured to perform detection and classification of defects of interest (DOI) on semiconductor wafers based on three-dimensional images. Optical inspection system 100 includes an illumination subsystem, a collection subsystem, one or more detectors, and a computing system. The illumination subsystem includes an illumination source 101 and all optical elements in the illumination optical path from the illumination source to the wafer. The collection subsystem includes all optical elements in the collection optical path from the specimen to each detector.; “Each of detectors 115, 120, and 125 generally function to convert the reflected and scattered light into an electrical signal, and therefore, may include substantially any photodetector known in the art.”;
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a controller configured to: (Kolchin, col. 7, lines 35-49: “System 100 also includes various electronic components (not shown) needed for processing the reflected and/or scattered signals detected by any of detectors 115, 120, and 125. For example, system 100 may include amplifier circuitry to receive output signals from any of detectors 115, 120, and 125 and to amplify those output signals by a predetermined amount and an analog-to-digital converter (ADC) to convert the amplified signals into a digital format suitable for use within processor 131.”)
analyze the first signal to produce a first image of the object including a defect on the object; analyze the second signal to produce a second image of the object including the defect on the object; and analyze the fourth signal to produce a fourth image of the object including the defect on the object (Kolchin, col. 6, lines 31-37; col. 9, lines 13-27; col. 14, lines 54-65; FIG. 2; FIG. 3: “System 100 includes collection optics 116, 117, and 118 to collect the light scattered and/or reflected by wafer 103 and focus that light onto detector arrays 115, 120, and 125, respectively. The outputs of detectors 115, 120, and 125 are communicated to computing system 130 for processing the signals and determining the presence of defects and their locations.”; “In one aspect, a three-dimensional image of a thick semiconductor structure is generated from a volume measured in two lateral dimensions (e.g., parallel to the wafer surface) and a depth dimension (e.g., normal to the wafer surface). In the embodiment depicted in FIG. 1, computing system 130 arranges the outputs from one or more of the measurement channels (e.g., from one or more of detectors 115, 120, and 125) into a volumetric data set that corresponds to the measured volume. FIG. 2 depicts a plot 150 of a cross-sectional view (y=0) of a measured three-dimensional image illustrating a peak signal near a focus offset of −0.5 micrometers. FIG. 3 depicts a plot 151 of another cross-sectional view (x=0) of the measured three-dimensional image also illustrating a peak signal near a focus offset of −0.5 micrometers.”; “As used herein, the term “wafer” generally refers to substrates formed of a semiconductor or non-semiconductor material … a “reticle” may be a reticle at any stage of a reticle fabrication process, or a completed reticle that may or may not be released for use in a semiconductor fabrication facility. A reticle, or a “mask,” is generally defined as a substantially transparent substrate having substantially opaque regions formed thereon and configured in a pattern. The substrate may include, for example, a glass material such as quartz.”;
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wherein the controller includes artificial intelligence assisted object detection algorithms, (Kolchin, col. 10, lines 24-36: “In some embodiments, a three-dimensional image is processed algorithmically to identify and classify defects of interest. In some examples, processor 131 is configured to detect and classify defects from a three-dimensional image. The processor may include any appropriate processor known in the art. In addition, the processor may be configured to use any appropriate defect detection and classification algorithm or method known in the art. For example, the processor may use a die-to-database comparison, a three-dimensional filter, a clustering algorithm such as a principal component analysis or spectral clustering, a thresholding algorithm, a deep learning algorithm, or any other suitable algorithm to detect and classify defects on the specimen.”; Kolchin teaches using deep learning for identification and classification of defects in semiconductor wafer/substrates; the term “deep learning” meets the broadest reasonable interpretation of the claim term “artificial intelligence assisted object detection algorithm”; the 3D visualization of the wafer/substrate).
Kolchin fails to teach
wherein the first and second signals include signals from secondary electrons radiated from the object upon light signal emitted from the signal source opposite of the object contacts the object.
Bauer teaches
wherein the first and second signals include signals from secondary electrons radiated from the object upon light signal emitted from the signal source opposite of the object contacts the object (Bauer, para. [0201]; para. [0203]; FIG. 24: “The backscattered electrons and secondary electrons generated in an interaction region or a scattering cone of the sample 2425 by the electron beam 2415 are registered by the detector 2417. The detector 2417 that is arranged in the electron column 2420 is referred to as an “in lens detector.” The detector 2417 can be installed in the column 2420 in various embodiments. The detector 2417 converts the secondary electrons generated by the electron beam 2415 at the measurement point 2422 and/or the electrons backscattered from the sample 2425 into an electrical measurement signal and transmits the latter to an evaluation unit 2480 of the apparatus 2400. The evaluation unit 2480 analyzes the measurement signals from the detectors 2417 and 2419 and generates an image of the sample 2425 therefrom, said image being displayed on the display 2495 of the evaluation unit 2480. The detector 2417 can additionally contain a filter or a filter system in order to discriminate the electrons in terms of energy and/or solid angle (not represented in FIG. 24”; “Further, the apparatus 2400 can comprise a third detector (not illustrated in FIG. 24). The third detector can be embodied in the form of an Everhart-Thornley detector and is typically arranged outside the column 2420. In general, it is used to detect secondary electrons.”;
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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 signals, and as taught by Kolchin to include signals from secondary electrons radiated from the object upon signals emitted from a signal source opposite of the object contacts the object, as taught by Bauer.
The suggestion/motivation for doing so would have been that “the second parameter that determines the resolution limit when producing a repair element is the interaction region or the scattering cone of the secondary electrons generated by a particle beam having mass.” (Bauer, para. [0081]; therefore, detecting secondary electrons scattering off the object on a substrate leads to generating a more accurate image of the object at a more easily viewable resolution.
Kolchin, in view Bauer, fails to teach
wherein the controller includes artificial intelligence assisted object detection algorithms, and wherein the artificial intelligence assisted object detection algorithms include a YOLO (You Only Look Once) model.
Luo teaches
wherein the controller includes artificial intelligence assisted object detection algorithms, and wherein the artificial intelligence assisted object detection algorithms include a YOLO (You Only Look Once) model (Luo, abstract: “The invention discloses high density flexible exterior substrate defect detecting system and method based on deep learning, system includes hardware platform and software detection platform. Detection method includes the following steps: collecting the FICS image for containing different defects as training sample; Sample image is pre-processed, including is unified into standard size and handmarking is carried out to the defects of sample position and classification; Sample image input is trained based on the deep learning model for improving YOLO convolutional neural networks, obtains the model parameter that output is defective locations and classification; It inputs in trained deep learning model and detects after being standardized to the picture size of acquisition, obtain the defects of acquired image position and classification information.”).
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 artificial intelligence assisted object detection algorithm, as taught by Kolchin, in view of Bauer, to include a YOLO (You Only Look Once) model, as taught by Luo.
The suggestion/motivation for doing so would have been so “quick positioning and type identification of high density flexible exterior substrate defect can be achieved” (Luo, abstract) which “solves traditional shortcoming detection system where method speed is slow, it is difficult to realize the quick detection problem of high density FICS open defect” (Luo, abstract).
Therefore, it would have been obvious to combine Kolchin, with Bauer and Luo, to obtain the invention as specified in claim 18.
Claims 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kolchin, in view of Bauer, in view of Luo, and in view of Aiyer.
Regarding claim 19, Kolchin, in view of Bauer, and in view of Luo, teaches the apparatus of claim 18.
Kolchin, in view of Bauer, and in view of Luo, fails to teach
a third detector positioned at a third location configured to receive a third signal reflected from the object, wherein the controller is further configured to analyze the third signal to produce a third image of the object.
Aiyer teaches
a third detector positioned at a third location configured to receive a third signal reflected from the object, wherein the controller is further configured to analyze the third signal to produce a third image of the object (Aiyer, col. 5, lines 7-13; col. 2, lines 22-25; col. 5, lines 24-33; FIG. 7: “Further, in one embodiment of this apparatus as shown in more detail in FIG. 7, several cameras (detectors) are used. The multiple CCD (charge coupled device) video cameras 48A, 48B, 48C, 48D in this embodiment are positioned such that each camera receives the designated first order beam 12 from one grating type. The use of CCD cameras as the detectors is illustrative, not limiting … Multiple CCD cameras 48A, 48B, 48C, 48D (the detectors) are mounted on a guide beam 52 which in turn is held by vertical support 54. The number of cameras needed for semiconductor industry standard Class 2 defect inspection depends on the number of grating types that are to be inspected.”; “In another embodiment where a particular grating includes features of several different pitches (patterns), the detector is moved to several predetermined angles to inspect each of the different patterns. In another embodiment, several different detectors are located at different angles of reflective diffraction to receive light diffracted from the various patterns.”; “Cameras 48A, . . . , 48D provide their output signals (via camera multiplexer 58) to a commercially available type image processor unit 60. Through camera multiplexing, automatic inspection of different grating types is achieved with a single image processor unit 60. The position of each camera 48A, . . . , 48D in the vertical plane varies in this example from 500 mm to 600 mm from the plane of wafer 10. (For clarity, only four of the five cameras used in one embodiment are shown in FIG. 7. The X-Y-Z axes are shown for orientation purposes.)”;
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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 1) the apparatus, as taught by Kolchin, in view of Bauer, and in view of Luo, to include a third detector positioned at a third location configured to receive a third signal reflected from the object, as taught by Aiyer, and 2) the controller, as taught by Kolchin, in view of Bauer, and in view of Luo, to be configured to analyze the third signal to produce a third image of the object including the defect on the object, as taught by Aiyer.
The suggestion/motivation for doing so would have been to provide images of a substrate at numerous angles, which allows for more morphological information of the substrate to be known, such as height and depth of a wafer; more morphological information of the substrate leads to stronger image analysis of the substrate to find defects in the substrate.
Kolchin, in view of Bauer, in view of Luo, and in view of Aiyer teaches
wherein the fourth signal includes signal from backscattered electrons radiated from the object upon light signal emitted from the signal source opposite of the object contacts the object, and wherein the fourth signal includes composition information of the object and the defect and the first, second, and third signals include morphology information of the object and the defect (Bauer, para. [0201]; para. [0203]; FIG. 24; Aiyer, col. 5, lines 7-13; col. 2, lines 22-25; col. 5, lines 24-33; FIG. 7; see rejection above; see rejection of claim 18; the third detector taught by Aiyer is modified to be an additional Everhart-Thornley detector, as taught by Bauer that includes signal from backscattered electrons radiated from the object upon light signal emitted from the signal source opposite of the object contacts the object).
Therefore, it would have been obvious to combine Kolchin, Bauer, and Luo, with Aiyer, to obtain the invention as specified in claim 19.
Regarding claim 20, Kolchin, in view of Bauer, in view of Luo, and in view of Aiyer teaches the apparatus of claim 19, wherein the controller is further configured to: determine morphology information of the defect based on the first, second, third images of the object Kolchin, col. 10, lines 37-60; col. 14, lines 11-27: “In another aspect, the three-dimensional location of a defect of interest is determined based on an analysis of the three-dimensional image of a thick semiconductor structure. In this manner, the actual position of a defect within a wafer is measured (e.g., {x, y, z} coordinates of the defect). The actual defect position can be used to locate the defect later for further analysis (e.g., analysis by a focused ion beam system, EBI system, etc. In many dark field measurement applications, the diffraction orders are suppressed and the actual defect location in the z-direction (e.g., depth) is linearly related to the focus offset associated with the peak signal. For many cases of incoherent BF illumination, the defect location in the z-direction is linearly related to the focus offset associated with the peak signal”; the defect images take into account morphological information of the wafer/substrate such as depth of the defect found by certain light reflected off the substrate detected by the detectors, as well as the structural makeup the defect itself; “In another embodiment, defects can be detected at stack depths that are as large as about eight micrometers. The thickness of a vertical ONON or OPOP stack under inspection is limited only by the depth of penetration of the illumination light. Transmission through an oxide-nitride-oxide-nitrite (ONON) or oxide-polysilicon-oxide-polysilicon (OPOP) stack is limited less by absorption at longer wavelengths. Thus, longer illumination wavelengths may be employed to effectively inspect very deep structures.”; this means that composition information is known in the image of the substrate and the defect because different wavelengths are chosen for the illumination light onto the substrate depending on the type of material the substrate is made of; different wavelength light will be detected by the detectors reflecting off the substrate to generate a variety of images depending on the composition of the material of the substrate and the defect in the substrate);
determine composition information of the defect based on the fourth image of the object (Aiyer, col. 5, lines 7-13; col. 2, lines 22-25; FIG. 7; see rejection of claim 1 above; see rejection of claim 2 reciting wherein the fourth signal includes composition information of the object; Kolchin, col. 10, lines 37-60; col. 14, lines 11-27; see rejection above; Kolchin teaches finding both composition and morphology information of the defects in the semiconductor substrate from images which is extended to the fourth image taken by the fourth detector taught by Aiyer);
identify a defect type based on the first, second, third, and fourth images of the object (Kolchin, col. 10, lines 24-36: “In some embodiments, a three-dimensional image is processed algorithmically to identify and classify defects of interest. In some examples, processor 131 is configured to detect and classify defects from a three-dimensional image. The processor may include any appropriate processor known in the art. In addition, the processor may be configured to use any appropriate defect detection and classification algorithm or method known in the art. For example, the processor may use a die-to-database comparison, a three-dimensional filter, a clustering algorithm such as a principal component analysis or spectral clustering, a thresholding algorithm, a deep learning algorithm, or any other suitable algorithm to detect and classify defects on the specimen.”);
determine morphology information of the defect based on the first, second, third images of the object (Kolchin, col. 10, lines 37-60; col. 14, lines 11-27: “In another aspect, the three-dimensional location of a defect of interest is determined based on an analysis of the three-dimensional image of a thick semiconductor structure. In this manner, the actual position of a defect within a wafer is measured (e.g., {x, y, z} coordinates of the defect). The actual defect position can be used to locate the defect later for further analysis (e.g., analysis by a focused ion beam system, EBI system, etc. In many dark field measurement applications, the diffraction orders are suppressed and the actual defect location in the z-direction (e.g., depth) is linearly related to the focus offset associated with the peak signal. For many cases of incoherent BF illumination, the defect location in the z-direction is linearly related to the focus offset associated with the peak signal”; the defect images take into account morphological information of the wafer/substrate such as depth of the defect found by certain light reflected off the substrate detected by the detectors, as well as the structural makeup the defect itself; “In another embodiment, defects can be detected at stack depths that are as large as about eight micrometers. The thickness of a vertical ONON or OPOP stack under inspection is limited only by the depth of penetration of the illumination light. Transmission through an oxide-nitride-oxide-nitrite (ONON) or oxide-polysilicon-oxide-polysilicon (OPOP) stack is limited less by absorption at longer wavelengths. Thus, longer illumination wavelengths may be employed to effectively inspect very deep structures.”; this means that composition information is known in the image of the substrate and the defect because different wavelengths are chosen for the illumination light onto the substrate depending on the type of material the substrate is made of; different wavelength light will be detected by the detectors reflecting off the substrate to generate a variety of images depending on the composition of the material of the substrate and the defect in the substrate);
determine composition information of the defect based on the fourth image of the object (Aiyer, col. 5, lines 7-13; col. 2, lines 22-25; FIG. 7; see rejection of claim 1 above; see rejection of claim 2 reciting wherein the fourth signal includes composition information of the object; Kolchin, col. 10, lines 37-60; col. 14, lines 11-27; see rejection above; Kolchin teaches finding both composition and morphology information of the defects in the semiconductor substrate from images which is extended to the fourth image taken by the fourth detector taught by Aiyer); and
identify a defect type based on the first, second, third, and fourth images of the object (Kolchin, col. 10, lines 24-36: “In some embodiments, a three-dimensional image is processed algorithmically to identify and classify defects of interest. In some examples, processor 131 is configured to detect and classify defects from a three-dimensional image. The processor may include any appropriate processor known in the art. In addition, the processor may be configured to use any appropriate defect detection and classification algorithm or method known in the art. For example, the processor may use a die-to-database comparison, a three-dimensional filter, a clustering algorithm such as a principal component analysis or spectral clustering, a thresholding algorithm, a deep learning algorithm, or any other suitable algorithm to detect and classify defects on the specimen.”).
Kolchin, in view of Bauer, in view of Luo, and in view of Aiyer, fails to teach
wherein the controller is further configured to: determine a type of material for repairing the defect type based on the morphology information and the composition information; and request repairing the defect of the object using the determined material.
Bauer further teaches
wherein the controller is further configured to: determine a type of material for repairing the defect type (Bauer, para. [0066]-[0067]; para. [0190]: “The at least one repair element produced can at least partly overlap the at least one defect. A deposited repair element can comprise a material of the lithographic mask. The deposited repair element can comprise: a metal, for instance chromium (Cr), a metal compound, for instance tantalum nitride (TaN), silicon (Si), silicon dioxide (SiO2) and molybdenum silicon oxynitride (MoxSiOyNz), wherein 0<x≤0.5, 0≤y≤2 and 0≤z≤4/3. An etched repair element can etch a material of the photolithographic mask. The etched repair element can comprise the mask materials mentioned above. The method defined above can comprise the steps of: (a) producing at least one repair element by use of at least one repair shape for which the parameters are defined by the at least one defect; and (b) ascertaining parameters of a repair shape for a remaining defect residue, wherein ascertaining parameters for the repair shape for the remaining defect residue comprises: allocating at least one numerical value to a parameter which deviates from the numerical value predefined by the remaining defect residue for said parameter.; “Alternatively or additionally, a machine learning model, for a repair shape that has already been parametrized, can allocate a different numerical value to one or more parameters for the purpose of ascertaining the above-described repair elements 410, 510, 610 according to the invention. The currently preferred embodiment, however, is that, from the input data indicated above, a machine learning model directly predicts the parameters of a repair shape for the purpose of forming one of the repair elements 410, 510, 610 described in this application. The process of training a machine learning model will not be discussed in this application.”; a photolithographic mask or a “reticle” is a substrate) based on the morphology information and the composition information (Bauer, para. [0213]; para. [0070]: “As already explained above, an electron beam 2415 can be focused to a spot diameter in the range of a few nanometers. The interaction region or the scattering cone in which an electron beam 2415 generates secondary electrons depends firstly on the energy of the electron beam 2415 and secondly on the material composition on which the electron beam 2415 impinges. The diameters of interaction regions attain values in the low single-digit nanometer range. The diameter of a scattering cone of an electron beam 2415 thus limits the achievable resolution limit during the generation of a repair element 410, 510, 610 by implementing the corresponding repair shape. Said resolution limit at the present time is in the single-digit nanometer range.”; “The at least one repair element produced can have in at least one dimension a dimensional size which is smaller than the resolution limit R of the photomask. As already explained above, the averaging of the actinic radiation over structures with dimensional sizes below the resolution capability of the mask results in a reduced effect of a placement error of a repair element. This circumstance significantly facilitates the positioning of the repair element(s) in relation to the position of a defect to be repaired. Furthermore, by virtue of the non-imaging of the repair element(s), the geometric shape(s) thereof can deviate from the shape of the defect in a significant way, without the compensation of the defect being adversely influenced in an appreciable way. This fact considerably simplifies the repair or the compensation of small defects.”); and
request repairing the defect of the object using the determined material (Bauer, para. [0066]-[0067]; see above; para. [0054]; para. [0210]; FIG. 24: “Producing the at least one repair element can comprise: carrying out at least one local etching process and/or carrying out at least one local deposition process by use of at least one focused particle beam and at least one precursor gas.”; “The gas providing system 2390 realized by the apparatus 2400 is discussed below. As already explained above, the sample 2425 is arranged on a sample stage 2430. The imaging elements of the column 2420 of the SEM 2410 can focus the electron beam 2415 and scan the latter over the sample 2525. The electron beam 2415 of the SEM 2410 can be used to induce a particle beam-induced deposition process (EBID, electron beam induced deposition) and/or a particle beam-induced etching process (EBIE, electron beam induced etching). The exemplary apparatus 2400 in FIG. 24 has three different supply containers 2440, 2450 and 2460, for storing various precursor gases, for the purposes of carrying out these processes.”;
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particle beam 2350 shown in system FIG. 23 is combined with gas providing system shown in FIG. 24 above).
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 controller, as taught by Kolchin, in view of Bauer, in view of Luo, and in view of Aiyer, to be further configured to determine a type of material for repairing the defect type based on the morphology information and the composition information, and request repairing the defect of the object using the determined material, as further taught by Bauer.
The suggestion/motivation for doing so would have been that “furthermore, the repair of increasingly smaller defects is becoming more and more difficult. Firstly, the positioning of a repair tool relative to an identified defect is possible only with very complex metrology and, secondly, setting the repair tool to a specific small defect requires a high expenditure of time.” (Bauer, para. [0007]).
Therefore, it would have been obvious to combine Kolchin, Bauer, Luo, and Aiyer, with Bauer further, to obtain the invention as specified in claim 20.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL ADAM SHARIFF whose telephone number is 571-272-9741. The examiner can normally be reached M-F 8:30-5PM.
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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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/MICHAEL ADAM SHARIFF/
Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
Prosecution Insights