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 .
Response to Amendment
A Preliminary Amendment was made 11/18/2024 to amend the specification, abstract, and claims. Claims 1-20 are pending, including new claims 16-20.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on November 18, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is considered by examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such limitations with associated identified structure-function are:
Claim 1: “image inspection apparatus” defined as a charged-particle beam inspection system, described as a scanning electron microscope and associated imaging device, such as a camera, to generate an image of the sample (Fig 1 and described in the specification in at least ¶ [0024]-[0025], [0036]-[0038]).
Claim 2: “image inspection apparatus” defined as a charged-particle beam inspection system, described as a scanning electron microscope and associated imaging device, such as a camera, to generate an image of the sample (Fig 1 and described in the specification in at least ¶ [0024]-[0025], [0036]-[0038]).
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, each are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 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 1-9, 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Huisman et al (WO 2021/032652, cited in IDS 11/18/2024) in view of Fang et al (US 2020/0018944).
Regarding Claim 1, Huisman et al teach a system (imaging system 200; Fig 2 and ¶ [0041]), comprising:
an image inspection apparatus (EBI system 100 of imaging system 200; Fig 1, 2 and ¶ [0036], [0041]) configured to scan a sample and generate an inspection image of an integrated circuit fabricated on the sample (the electron beam tool 104 is used to scan a semiconductor wafer sample to be inspected and generate an SEM image; Fig 1, 2 and ¶ [0036]-[0037], [0042]-[0047]); and
a controller including circuitry (controller 109 with electrical configurations, including image acquirer 260, storage 270 integrated as one unit; Fig 1, 2 and ¶ [0038]-[0040], [0045]), configured to:
generate a set of simulated inspection images (images may be generated of the wafer 203; Fig 2 and ¶ [0045]-[0046]),
wherein each of the set of simulated inspection images comprises no charging artifact (the image sample (of a plurality of images) may be generated to not contain backscattered electrons or secondary electrons causing artifacts (thereby no electric charge); Fig 2, 3 and ¶ [0049]-[0050]); and
generate a set of inspection images by applying a physics-based model to the set of simulated inspection images (a physics-based charge diffusion modeling technique can be used to simulate an SEM-induced charging effect to generate (a set of) distorted SEM images; Fig 3 and ¶ [0051]-[0052]).
Huisman et al does not teach to train a machine learning model using the set of inspection images as input, wherein the machine learning model outputs a set of decoupled features of the set of inspection images; and apply the trained machine learning model on the inspection image to generate an output inspection image, wherein the output inspection image comprises fewer charging artifacts than the inspection image.
Fang et al is analogous art pertinent to the technological problem addressed in the current application and teaches to train a machine learning model using the set of inspection images as input (machine learning network 320 is trained with SEM training images of a wafer 203, which may be part of training data (inspection image acquired with distortion and used for training); Fig 3 and ¶ [0050]-[0054]),
wherein the machine learning model outputs a set of decoupled features of the set of inspection images (the machine learning network 320 is trained to extract one or more trained features from high-resolution images 310, which may be based on pattern information as identified by a pattern extractor 340 (associated with a charge ¶ [0038], [0090]), and used by comparator 370 to generate an output file (decoupled features described as data stored in a binary file format ¶ [0047]); Fig 2-5 and ¶ [0051]-[0064], [0074]-[0076]); and
apply the trained machine learning model on the inspection image to generate an output inspection image (the trained machine learning network 320 receives an inspection image 330 to extract features, used by pattern extractor 340 to extract patterns and is compared to the stored trained pattern features by the comparator 370 to determine matching relevant features/pattern information, followed by an image enhancer 380 to generate an enhanced inspection image; Fig 2-5 and ¶ [0053]-[0066], [0084]-[0086], [0090]),
wherein the output inspection image comprises fewer charging artifacts than the inspection image (the enhanced image of inspection image 330 is generated based on enhancing the spatial-spectral characteristics of the inspection image to improve the image quality (such as fewer charging artifacts causing a defect); Fig 2-5 and ¶ [0066]-[0069], [0084]-[0086], [0090]).
It would have been obvious to one of ordinary skill in art to combine the teachings of Huisman et al with Fang et al including to train a machine learning model using the set of inspection images as input, wherein the machine learning model outputs a set of decoupled features of the set of inspection images; and apply the trained machine learning model on the inspection image to generate an output inspection image, wherein the output inspection image comprises fewer charging artifacts than the inspection image. Huisman et al discloses the use of the machine learning techniques as it may be applied to images with SEM charging artifacts (¶ [0004]). Fang et al teaches the use of the inspection system as applied to charged particle imaging and applicable for SEM imaging inspections for detecting defects (artifacts) (¶ [0026]). By using a trained machine learning model to perform image inspection of SEM images, the inspection tool may be configured to identify pattern abnormalities, thereby quickly improving quality and improving throughput of manufactured wafers, as recognized by Fang et al (¶ [0018]-[0020]).
Regarding Claim 2, Huisman et al teach a system (imaging system 200; Fig 2 and ¶ [0041]), comprising:
an image inspection apparatus (EBI system 100 of imaging system 200; Fig 1, 2 and ¶ [0036], [0041]) configured to scan a sample and generate an inspection image of an integrated circuit fabricated on the sample (the electron beam tool 104 is used to scan a semiconductor wafer sample to be inspected and generate an SEM image; Fig 1, 2 and ¶ [0036]-[0037], [0042]-[0047]); and
a controller including circuitry (controller 109 with electrical configurations, including image acquirer 260, storage 270 integrated as one unit; Fig 1, 2 and ¶ [0038]-[0040], [0045]), configured to:
obtain a set of inspection images (image acquirer 260 acquires images (set) of a semiconductor sample (wafer 203); Fig 2 and ¶ [0046]-[0047]), and
wherein each of the set of inspection images comprises a charging artifact (the images may include SEM-induced charging artifacts; Fig 2, 3 and ¶ [0047]-[0049]).
Huisman et al does not teach to train a machine learning model using the set of inspection images as input, wherein the machine learning model outputs a set of decoupled features of the set of inspection images.
Fang et al is analogous art pertinent to the technological problem addressed in the current application and teaches to train a machine learning model using the set of inspection images as input (machine learning network 320 is trained with SEM training images of a wafer 203, which may be part of training data (inspection image acquired with distortion and used for training); Fig 3 and ¶ [0050]-[0054]),
wherein the machine learning model outputs a set of decoupled features of the set of inspection images (the machine learning network 320 is trained to extract one or more trained features from high-resolution images 310, which may be based on pattern information as identified by a pattern extractor 340 (associated with a charge ¶ [0038], [0090]), and used by comparator 370 to generate an output file (decoupled features described as data stored in a binary file format ¶ [0047]); Fig 2-5 and ¶ [0051]-[0064], [0074]-[0076]).
It would have been obvious to one of ordinary skill in art to combine the teachings of Huisman et al with Fang et al including to train a machine learning model using the set of inspection images as input, wherein the machine learning model outputs a set of decoupled features of the set of inspection images. Huisman et al discloses the use of the machine learning techniques as it may be applied to images with SEM charging artifacts (¶ [0004]). Fang et al teaches the use of the inspection system as applied to charged particle imaging and applicable for SEM imaging inspections for detecting defects (artifacts) (¶ [0026]). By training a machine learning model to perform image inspection of SEM images, the inspection tool may be configured to identify pattern abnormalities, thereby quickly improving quality and improving throughput of manufactured wafers, as recognized by Fang et al (¶ [0018]-[0020]).
Regarding Claim 3, Huisman et al in view of Fang et al teach the system of claim 2 (as described above), wherein the set of inspection images (Huisman et al, image acquirer 260 acquires images (set) of a semiconductor sample (wafer 203); Fig 2 and ¶ [0046]-[0047]) are measured inspection images (Huisman et al, the parameters of the charging artifacts are based on calibrations to theoretical calculations, thereby the simulated SEM-induced charging effect images are used as measured inspection images; Fig 3 and ¶ [0052]), and
wherein the controller includes circuitry (Huisman et al, controller 109 with electrical configurations, including image acquirer 260, storage 270 integrated as one unit; Fig 1, 2 and ¶ [0038]-[0040], [0045]) further configured to: obtain the set of inspection images by performing multiple scans on a test sample (Huisman et al, multiple SEM images may be generated; ¶ [0047]),
wherein each of the multiple scans is configured to use a different acquisition setting, and the acquisition setting comprises at least one of a beam current, a scan direction, or a landing energy of a beam (Huisman et al, scanning can be performed to generate multiple SEM images with primary electron beam 220 scanning wafer 203 along the same scan-direction; ¶ [0047]).
Regarding Claim 4, Huisman et al in view of Fang et al teach the system of claim 2 (as described above), wherein the set of inspection images are simulated inspection images (Huisman et al, a physics-based charge diffusion modeling technique can be used to simulate an SEM-induced charging effect to generate (a set of) distorted SEM images; Fig 3 and ¶ [0051]-[0052]), and
wherein the controller includes circuitry (Huisman et al, controller 109 with electrical configurations, including image acquirer 260, storage 270 integrated as one unit; Fig 1, 2 and ¶ [0038]-[0040], [0045]) further configured to: generate a set of simulated inspection images (Huisman et al, images may be generated of the wafer 203; Fig 2 and ¶ [0045]-[0046]),
wherein each of the set of simulated inspection images comprises no charging artifact (Huisman et al, the image sample (of a plurality of images) may be generated to not contain backscattered electrons or secondary electrons causing artifacts (thereby no electric charge); Fig 2, 3 and ¶ [0049]-[0050]); and
generate the set of inspection images by applying a physics-based model to the set of simulated inspection images (Huisman et al, a physics-based charge diffusion modeling technique can be used to simulate an SEM-induced charging effect to generate (a set of) distorted SEM images; Fig 3 and ¶ [0051]-[0052]).
Regarding Claim 5, Huisman et al in view of Fang et al teach the system of claim 4 (as described above), wherein the controller includes circuitry further configured to generate the set of simulated inspection images using a Monte-Carlo based technique (Huisman et al, a physics-based charge diffusion modeling technique is based on Monte-Carlo simulations; Fig 3 and ¶ [0052]).
Regarding Claim 6, Huisman et al in view of Fang et al teach the system of claim 2 (as described above), wherein the set of inspection images are simulated inspection images (Huisman et al, a physics-based charge diffusion modeling technique can be used to simulate an SEM-induced charging effect to generate (a set of) distorted SEM images; Fig 3 and ¶ [0051]-[0052]), and
wherein the controller includes circuitry (Huisman et al, controller 109 with electrical configurations, including image acquirer 260, storage 270 integrated as one unit; Fig 1, 2 and ¶ [0038]-[0040], [0045]) further configured to: generate a set of simulated inspection images as the set of inspection images (Huisman et al, a physics-based charge diffusion modeling technique can be used to simulate an SEM-induced charging effect to generate (a set of) distorted SEM images; Fig 3 and ¶ [0051]-[0052]),
wherein each of the set of simulated inspection images comprises a charging artifact (Huisman et al, a physics-based charge diffusion modeling technique can be used to simulate an SEM-induced charging effect to generate (a set of) distorted SEM images; Fig 3 and ¶ [0051]-[0052]).
Regarding Claim 7, Huisman et al in view of Fang et al teach the system of claim 2 (as described above), wherein the set of decoupled features comprise at least one of a feature representing a scan direction, a feature representing a pattern, or a feature representing a dose of charged particles (Fang et al, the machine learning network 320 is trained to extract one or more trained features from high-resolution images 310, which may be based on pattern information as identified by a pattern extractor 340 (associated with a charge ¶ [0038], [0090]), and used by comparator 370 to generate an output file (decoupled features described as data stored in a binary file format ¶ [0047]); Fig 2-5 and ¶ [0051]-[0064], [0074]-[0076]).
Regarding Claim 8, Huisman et al in view of Fang et al teach the system of claim 2 (as described above), wherein the controller includes circuitry (Fang et al, controller 109; Fig 1-3 and ¶ [0032], [0037]-[0039]) further configured to: apply the trained machine learning model on the inspection image to generate an output inspection image (Fang et al, the trained machine learning network 320 receives an inspection image 330 to extract features, used by pattern extractor 340 to extract patterns and is compared to the stored trained pattern features by the comparator 370 to determine matching relevant features/pattern information, followed by an image enhancer 380 to generate an enhanced inspection image; Fig 2-5 and ¶ [0053]-[0066], [0084]-[0086], [0090]),
wherein the output inspection image comprises fewer charging artifacts than the inspection image (Fang et al, the enhanced image of inspection image 330 is generated based on enhancing the spatial-spectral characteristics of the inspection image to improve the image quality (such as fewer charging artifacts causing a defect); Fig 2-5 and ¶ [0066]-[0069], [0084]-[0086], [0090]).
Regarding Claim 9, Huisman et al in view of Fang et al teach the system of claim 8 (as described above), wherein the output inspection image comprises a patten having a surrounding edge blooming (Huisman et al, the surface pattern may be presented as an “edge bloom”; Fig 6B and ¶ [0053], [0071]).
Regarding Claim 16, Huisman et al teach a non-transitory computer-readable medium (memory of controller 109; Fig 1 and ¶ [0040]) that stores a set of instructions that is executable by at least one processor of an apparatus to cause the apparatus to (memory stores instructions accessible and executed by processor of controller 109; Fig 1 and ¶ [0040]) perform operations comprising: steps identical to claim 2 (as described above).
Regarding Claim 17, Huisman et al in view of Fang et al teach the non-transitory computer-readable medium of claim 16 (as described above), with further steps claimed identical to claim 3 (as described above).
Regarding Claim 18, Huisman et al in view of Fang et al teach the non-transitory computer-readable medium of claim 16 (as described above), with further steps claimed identical to claim 4 (as described above).
Regarding Claim 19, Huisman et al in view of Fang et al teach the non-transitory computer-readable medium of claim 16 (as described above), with further steps claimed identical to claim 5 (as described above).
Regarding Claim 20, Huisman et al in view of Fang et al teach the non-transitory computer-readable medium of claim 16 (as described above), with further steps claimed identical to claim 6 (as described above).
Claims 10, 12 are rejected under 35 U.S.C. 103 as being unpatentable over Huisman et al (WO 2021/032652, cited in IDS 11/18/2024) in view of Fang et al (US 2020/0018944) and Zhang et al (US 2017/0345140).
Regarding Claim 10, Huisman et al in view of Fang et al teach the system of claim 8 (as described above).
Huisman et al in view of Fang et al does not teach wherein the machine learning model comprises an autoencoder, the set of decoupled features comprise a set of codes of the autoencoder, and the set of codes comprise at least one of a code representing a scan direction, a code representing a pattern feature, or a code representing a dose of charged particles.
Zhang et al is analogous art pertinent to the technological problem addressed in the current application and teaches wherein the machine learning model comprises an autoencoder (the machine learning model may be a variational autoencoder; ¶ [0060], [0066], [0072], [0075]), the set of decoupled features comprise a set of codes of the autoencoder (the output from the encoder layers may include a defect classification code; ¶ [0113]-[0115]), and the set of codes comprise at least one of a code representing a scan direction, a code representing a pattern feature, or a code representing a dose of charged particles (the output from the encoder layers may include classifying a defect of the pattern features of the SEM image, with defect classification results output as a defect classification code; ¶ [0113]-[0115]).
It would have been obvious to one of ordinary skill in art to combine the teachings of Huisman et al in view of Fang et al with Zhang et al including wherein the machine learning model comprises an autoencoder, the set of decoupled features comprise a set of codes of the autoencoder, and the set of codes comprise at least one of a code representing a scan direction, a code representing a pattern feature, or a code representing a dose of charged particles. By perform defect inspection of semiconductor wafers during manufacture allow for precise and efficient inspection for attributes of defects, thereby improving the process for acceptable characteristics, as recognized by Zhang et al (¶ [0004]-[0008]).
Regarding Claim 12, Huisman et al in view of Fang et al teach the system of claim 2 (as described above).
Huisman et al in view of Fang et al does not teach wherein the machine learning model comprises an autoencoder, and the set of decoupled features comprise a set of codes of the autoencoder.
Zhang et al is analogous art pertinent to the technological problem addressed in the current application and teaches wherein the machine learning model comprises an autoencoder (the machine learning model may be a variational autoencoder; ¶ [0060], [0066], [0072], [0075]), and the set of decoupled features comprise a set of codes of the autoencoder (the output from the encoder layers may include classifying a defect of the pattern features of the SEM image, with defect classification results output as a defect classification code; ¶ [0113]-[0115]).
It would have been obvious to one of ordinary skill in art to combine the teachings of Huisman et al in view of Fang et al with Zhang et al including wherein the machine learning model comprises an autoencoder, and the set of decoupled features comprise a set of codes of the autoencoder. By perform defect inspection of semiconductor wafers during manufacture allow for precise and efficient inspection for attributes of defects, thereby improving the process for acceptable characteristics, as recognized by Zhang et al (¶ [0004]-[0008]).
Allowable Subject Matter
Claims 11, 13-15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding Claim 11, the prior art was not readily identified to teach, suggest or provide motivation to combine with the above cited prior art to teach the entirety of the following limitations in combination with all of the limitations in which it depends:
The system of claim 10, wherein the controller includes circuitry further configured to: apply an encoder of the trained autoencoder on the inspection image to generate the set of codes; set the code representing the dose of charged particles to a value of zero; and apply a decoder of the trained autoencoder to the set of codes to generate the output inspection image, wherein the set of codes comprise the changed code representing the dose of charged particles.
Regarding Claim 13, the prior art was not readily identified to teach, suggest or provide motivation to combine with the above cited prior art to teach the entirety of the following limitations in combination with all of the limitations in which it depends:
The system of claim 12, wherein the set of inspection images comprise a first simulated inspection image, a second simulated inspection image, a third simulated inspection image, and a fourth simulated inspection image, wherein the first simulated inspection image and the second simulated inspection image have a same pattern feature and different scan directions, wherein the third simulated inspection image and the fourth simulated inspection image have different pattern features, and wherein the controller includes circuitry further configured to: in response to obtaining the first simulated inspection image and the second simulated inspection image, apply an encoder of the autoencoder on the first simulated inspection image to generate a first set of codes and on the second simulated inspection image to generate a second set of codes; in response to obtaining the third simulated inspection image and the fourth simulated inspection image, apply the encoder on the third simulated inspection image to generate a third set of codes and on the fourth simulated inspection image to generate a fourth set of codes; and train the autoencoder based on the first set of codes, the second set of codes, the third set of codes, and the fourth set of codes.
Claims 14-15 are dependent on claim 13 and therefore objected to for similar reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Baruch et al (US 2021/0407072) teach a system and method for generating a training set of images for DNN to learn the inspection of semiconductor wafers during fabrication processes and identify defects associated with different tooling operations including particle charging effects.
Sears et al (US 2016/0260576) teach a SEM apparatus for mitigating charging artifacts based on image analysis including analysis of the scan direction used to generate the image and using a composite of images in determining potential charging artifacts.
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/KATHLEEN M BROUGHTON/Primary Examiner, Art Unit 2661
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