DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Election/Restriction
This application contains claims directed to the following patentably distinct species:
Species I: claims 1-20, Figures 4A and 4B, 5, 10, and paragraphs 6-26 and 80-104; and
Species II: claims 21-31, Figures 14A, 14B, 16, and paragraphs 27-37, 61, 62, and 175-201.
The species are independent or distinct because they include mutually exclusive features. See MPEP 806.04(f). In addition, these species are not obvious variants of each other based on the current record.
Species I includes the mutually exclusive features: “oxide semiconductor” (claim 1), “oxygen vacancy distribution” (claim 1), “the plurality of reference images are acquired using samples each having a predetermined oxygen vacancy concentration” (claim 12), and “the comparison data is generated by a light source disposed to emit light having a predetermined angle with respect to a surface of the comparison target” (claim 18).
Species II includes the mutually exclusive features: “plurality of inspection organic materials” (claim 21), “organic material distribution” (claim 21), “the plurality of reference images are acquired based on the bonding type of the plurality of organic materials” (claim 26) and determining “the type of inspection organic materials included in the inspection target based on the organic material distribution image” (claims 26 and 27).
Species I and II are independent or distinct because Species I concerns inspecting an oxide semiconductor, outputting an oxygen vacancy distribution image, and detecting whether the oxide semiconductor is defective, and Species II concerns inspecting organic materials, outputting an organic material distribution image, and determining the type of the organic materials. The claims of Species I require an inspection target including “an oxide semiconductor” and “reference data indicating oxygen vacancy distribution”, but the claims of Species II do not. The claims of Species II require an inspection target including “plurality of inspection organic materials” and “reference images … corresponding to each of the plurality of organic materials”, but the claims of Species I do not.
Applicant is required under 35 U.S.C. 121 to elect a single disclosed species, or a single grouping of patentably indistinct species, for prosecution on the merits to which the claims shall be restricted if no generic claim is finally held to be allowable. Currently, no claims are generic.
There is a serious search and/or examination burden for the patentably distinct species as set forth above because at least the following reasons apply: the species or groupings of patentably indistinct species require a different field of search (e.g., searching different classes/subclasses or electronic resources, or employing different search strategies or search queries). Species I requires a search of G06T7/004, G06T2207/30148, G01N21/9501, H10D30/6755, and H10D62/875. Species II requires a search of G01N25/72, H10D62/60, H10D62/8325, H10D62/50.
Applicant is advised that the reply to this requirement to be complete must include (i) an election of a species to be examined even though the requirement may be traversed (37 CFR 1.143) and (ii) identification of the claims encompassing the elected species or grouping of patentably indistinct species, including any claims subsequently added. An argument that a claim is allowable or that all claims are generic is considered nonresponsive unless accompanied by an election.
The election may be made with or without traverse. To preserve a right to petition, the election must be made with traverse. If the reply does not distinctly and specifically point out supposed errors in the election of species requirement, the election shall be treated as an election without traverse. Traversal must be presented at the time of election in order to be considered timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are added after the election, applicant must indicate which of these claims are readable on the elected species or grouping of patentably indistinct species.
Should applicant traverse on the ground that the species, or groupings of patentably indistinct species from which election is required, are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing them to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the species unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other species.
Upon the allowance of a generic claim, applicant will be entitled to consideration of claims to additional species which depend from or otherwise require all the limitations of an allowable generic claim as provided by 37 CFR 1.141.
During a telephone conversation with Kyu Wan Ryu (reg. no. 74,525) on 23 December 2025 a provisional election was made without traverse to prosecute the invention of Species I, claims 1-20. Affirmation of this election must be made by applicant in replying to this Office action. Claims 21-31 are withdrawn from further consideration by the examiner as being drawn to a non-elected invention. See 37 CFR 1.142(b).
Applicant is reminded that upon the cancelation of claims to a non-elected invention, the inventorship must be corrected in compliance with 37 CFR 1.48(a) if one or more of the currently named inventors is no longer an inventor of at least one claim remaining in the application. A request to correct inventorship under 37 CFR 1.48(a) must be accompanied by an application data sheet in accordance with 37 CFR 1.76 that identifies each inventor by his or her legal name and by the processing fee required under 37 CFR 1.17(i).
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Drawings
The drawings are objected to because FIG. 13C contains text that is difficult to read and the detailed description does not explain what the specific text would be. The lower left corner of each COMa image appears to depict the term “HAADF” (High-angle Annular Dark-Field Imaging), but the text above it to the left of the listed elements is unreadable and the number in the lower right corner is also unreadable. Because HAADF is not explicitly described in the specification, the unreadable text is needed to fully understand the comparison image in FIG. 13C, though this does not preclude examination on the merits.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The abstract of the disclosure is objected to because “Provided is” in line 1 can be implied. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The title of the invention is not descriptive. The title is too broad to assist a reader of the instant application in knowing what type of inspection is involved. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: OXIDE SEMICONDUCTOR INSPECTION DEVICE AND OXYGEN VACANCY INSPECTION METHOD USING THE SAME
Claim Objections
Claim 15 is objected to because of the following informalities: “an oxygen vacancy distributions” (emphasis added) should be written as “an oxygen vacancy distribution” for clarity. Appropriate correction is required.
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 limitations use 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 limitations are:
“an image output unit configured to output an inspection image for an inspection target including an oxide semiconductor” in claims 1-13;
“a storage unit configured to store a plurality of reference images and a plurality of reference data indicating oxygen vacancy distribution which are generated through an artificial neural network” in claims 1-13;
“a neural network processing unit configured to compare the inspection image with the reference images and select a selection reference image corresponding to the inspection image, and output an oxygen vacancy distribution image based on selection reference data corresponding to the selection reference image” in claims 1-13; and
“a detection unit configured to detect whether the inspection target is defective based on the oxygen vacancy distribution image” in claim 13.
Because these claim limitations 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations 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 3-8, 10, and 15-19 are 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.
Claims 3, 10, and 16 recite in part, “the comparison image” (emphasis added), however their parent claims recite, in part, “comparison images”. Thus, it is unclear which “comparison image” is being referenced in claims 3, 10 and 16. For purposes of applying prior art, the Examiner interprets “the comparison image” as referring to one of the “comparison images” of claim 2. Dependent claims 4-7 and 17-19 are rejected for inheriting and not curing the deficiencies of claims 3 and 16 respectively.
Claim 7 recites in part, “wherein an area of the first region is about 900 nm2 to about 1600 nm2” (emphasis added). The specific range being claimed is ambiguous because the term “about” that describes each value of the range is vague. The Specification does not provide an example of a region that is “about 900 nm2 to about 1600 nm2” in size. Thus, it is unclear what values are included in the range of first region sizes in claim 7 and what values are not included. For example, would 890 nm2 be “about 900 nm2”? Would 850 nm2 be “about 900 nm2”? For purposes of applying prior art, the examiner interprets “about 900 nm2 to about 1600 nm2” to be an inclusive range between 900 nm2 and 1600 nm2.
Claim 8 recites in part, “wherein the comparison target includes a plurality of comparison targets and oxide semiconductors included in the plurality of comparison targets are different in concentration of oxygen vacancy” (emphasis added). However, claim 2 recites, in part, “wherein the reference images and the reference data are generated by learning comparison images for a plurality of comparison targets comprising an oxide semiconductor, and comparison data indicating oxygen vacancy distributions of the comparison targets through the artificial neural network” (emphasis added). The limitation “the comparison target” in claim 8 lacks antecedent basis because it is unclear which of the “plurality of comparison targets” from claim 2 are being referenced. The limitation “the plurality of comparison targets” in claim 8 lacks antecedent basis because it is unclear which of the “plurality of comparison targets” from claim 2 or claim 8 are being referenced. It is also unclear whether “a plurality of comparison targets and oxide semiconductors included in the plurality of comparison targets” means a plurality of oxide semiconductors are included in each comparison target, whether each comparison target includes one oxide semiconductor, or something else. For purposes of applying prior art, claim 8 is interpreted to include: “wherein the comprising the [[and]] oxide semiconductor
Claim 15 recites in part, “wherein the generating of the reference images and the reference data comprises learning comparison image for comparison target including oxide semiconductors, and comparison data indicating an oxygen vacancy distributions of the comparison target through the artificial neural network” (emphasis added). This language is unclear as to whether it means the “comparison target” includes a plurality of semiconductors, whether it was intended to correspond to substantially similar language in claim 2, or something else. For purposes of applying prior art, the Examiner interprets claim 15 to correspond to claim 2. Dependent claims 16-19 are rejected for inheriting and not curing the deficiencies of claim 15.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed inventions absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 8, 10-16, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over STEM Image Analysis Based on Deep Learning: Identification of Vacancy Defects and Polymorphs of MoS2 to Lee et al. (hereinafter “Lee”) in view of U.S. Pat. Appl. Pub. No. 20180337238 to Eom et al. (hereinafter “Eom”) and in further view of Wafer Map Defect Pattern Classification and Image Retrieval Using Convolutional Neural Network to Nakazawa et al. (hereinafter “Nakazawa”).
Regarding claim 1, Lee teaches an inspection device comprising:
an image output unit (Lee, pg. 4683, “Experimental HAADF-STEM imaging was performed using an aberration-corrected TEM (ARM-200F; JEOL) operated at 80 kV with a probe current of 25 pA.”; The experimental STEM images are inspection images) configured to output an inspection image (experimental STEM images) for an inspection target including a semiconductor (Lee, pg. 4677, “We create an FCN to process MoS2, the archetype of TMDCs”; Molybdenum disulfide is an example of a transition metal dichalcogenide semiconductor.);
a storage unit (FCN image-based training requires a storage to retain the image and learning data. See Lee, pg. 4683, “load the training sets”.) configured to store a plurality of reference images (STEM images are generated and then stored for training the FCN. See Lee, pg. 4678, “we mainly trained our ResUNet models with simulated STEM images (256 × 256 pixels) generated by Computem.”; pg. 4683, “The vacancy-detection model used a kernel size of 7 × 7 pixels and 10 000 images for training and 5000 images for validation.”; The simulated STEM images used for training are reference images.) and a plurality of reference data indicating a vacancy distribution (The image features learned to be recognized by the model and the learned parameters of the model are reference data that is generated, stored and used by the model to generate new output, the reference data relating input data to sulfur vacancy distribution image output. See Lee, pg. 4679, “the model started producing meaningful predictions of vacancy locations”; Figure 3(g) in comparison to Figure (h) shows how vacancy defects or missing atoms are indicated by the intensities of dark spots that are learnable features of the FCN.) through an artificial neural network (Lee, Figure 2, “Fully convolutional network (FCN)”); and
a neural network processing unit (Lee - The neural network training requires a general purpose processor to execute the program instructions that carry out the training. See Lee, Methods section, pg. 4683) configured to compare the inspection image with the reference images (Experimental inspection images are compared with simulated STEM images. See Lee at pg. 4681 and Figure 4. An input image provided to the FCN is compared with the reference STEM images via the model, which is trained on the reference images.) and output a vacancy distribution image (Lee, pg. 4679, Figure 2(b), output 1) based on selection reference data (Lee, pg. 4688, “The locations of vacancies were predicted by the vacancy model as shown in Figure 4c,h, where the green dots represent single sulfur vacancies (VS1) and blue dots represent double sulfur vacancies (VS2).”; Single vacancies and double vacancies correspond to different image intensities. The vacancy prediction is based on the FCN’s internal selections of outputs corresponding to inputs corresponding to vacancies or a lack thereof; pg. 4683, “pg. 4683, “We believe that the current models can be extended to investigate similar structural features in various 2D crystals beyond MoS2 and therefore provide a guideline on best practices to train a deep learning model for STEM image analysis.”), but does not teach that which is explicitly taught by Eom.
Eom teaches an oxide semiconductor (Strontium titanate, SrTiO3, is an oxide semiconductor. See Eom at Abstract) and reference data indicating oxygen vacancy distribution (An oxygen vacancy index is determined and used to confirm that “the density of oxygen vacancies is expected to increase with distance away from the interface into the bulk region”. See Eom at par. 43; par.15, “Scanning transmission electron microscopy-annular dark-field (STEM-ADF) images taken from the STO (10 unit-cells)/LAO (40 unit-cells)/STO heterostructure are given in FIG. 3B. … Atomic-scale energy dispersive X-ray spectroscopy (EDS) elemental mapping was performed on the same sample (FIG. 3C). The EDS maps show the chemically-abrupt top interface with atomic intermixing limited to ~1 unit-cell.”; STEM-ADF images are provided in FIG. 3B to provide structural inspection images. EDS elemental mapping is provided in FIG. 3C to provide a spatial distribution of elements. EELS spectra are provided in FIGs. 11A-11E, where EELS produces O-K edge maps as provided in FIGs. 11C and 11E which are used to assess oxygen distribution. Since the sensitivity of EELS was found to be too low to detect oxygen vacancies, the DR-CLS technique is used to determine the distribution of oxygen vacancies. See pars. 20 and 21. Oxygen distribution is assessed by calculating the Oxygen Vacancy Index shown in FIG. 6B, which represents a profile of oxygen vacancies across multiple layers or regions of the heterostructure.).
Lee discloses a neural network model that identifies distributions of semiconductor vacancy point defects from scanning transmission electron microscopy (STEM) images. The model is trained to accurately represent the “expected linear correlation” between different vacancy concentrations (vacancy ratios) generated by the FCN and those of labeled ground truth images. See Lee at pg. 4681 and Figure 4(o). Thus, Lee shows that it was known in the art before the effective filing date of the claimed invention to identify different vacancy concentrations in semiconductors using a machine learning model and electron microscopy images, which is analogous to the claimed invention in that it is pertinent to the problem being solved by the claimed invention, improving the detection of semiconductor defects. Eom discloses an oxide heterostructure including two SrTiO3 oxide semiconductor layers and generating an oxygen vacancy index that spans across multiple regions of the heterostructure based on acquired STEM-ADF images. See Eom at Abstract and FIG. 6B. STEM-ADF images are generated to obtain structural lattice information and EDS is performed to generate elemental distribution information of oxygen to corroborate findings from other generated maps and confirm intermixing at the interface is limited. See id. at pars. 20 and 36. Thus, Eom shows that it was known in the art before the effective filing date of the claimed invention to use EDS to generate an element map to corroborate the lattice structure obtained by STEM images, which is analogous to the claimed invention in that it is pertinent to the problem being solved by the claimed invention, improving the detection of semiconductor defects.
A person of ordinary skill in the art would have been motivated to reconfigure Lee’s neural network to detect oxygen vacancies in an oxide semiconductor image of multiple layers (semiconductor regions or groups of regions) with different vacancy concentrations as disclosed by Eom (see FIG. 6B), by using EDS to generate reference data including elemental mapping for training and verifying the model, to thereby detect oxygen vacancy point defects and generate oxygen vacancy profiles and oxygen vacancy distribution images from input images of oxide semiconductors instead of or in addition to sulfur vacancies in MoS2. Based on the foregoing, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have made such modification according to known methods to yield the predictable results to have the benefit of extending the model’s application to other manufacturing processes.
Lee in view of Eom does not teach that which is explicitly taught by Nakazawa.
Nakazawa teaches select a selection reference image (stored image that is to be retrieved. See Nakazawa at pg. 309) corresponding to the inspection image (input wafer map image. See Nakagawa at section II.B; Nakazawa, pg. 309, “In order to have an effective knowledge base, two components are required: 1) wafer map defect pattern classification and 2) wafer map image retrieval from historical wafer map libraries. The wafer map defect pattern classification can provide information about a defect occurrence rate for each defect class and engineers focus on the most important issue using this data. The wafer map image retrieval is helpful to identify a root cause by querying historical wafer maps with the known root cause.”) and output a distribution image (wafer map. See Nakagawa at section II.C) based on reference data (neural network parameters or weights) corresponding to the selection reference image (Nakagawa, pg. 310, section I, “The performance of trained CNN is also validated using data from the real wafers. Then the image retrieval result is shown by comparing a query image and the top three retrieved wafer maps from the 18,000 wafer library”. Different classes of defects are shown in Table II.).
Lee in view of Eom is analogous to the claimed invention for the reasons provided above. Nakazawa discloses a neural network that takes a query wafer map image from a semiconductor manufacturing process as input, classifies the defects, and retrieves the top three matching wafer map images. See Nakazawa at Fig. 8. Thus, Nakazawa shows that it was known in the art before the effective filing date of the claimed invention to output a defect distribution image based on selected reference images, which is analogous to the claimed invention in that it is pertinent to the problem being solved by the claimed invention, improving the detection of semiconductor defects.
A person of ordinary skill in the art would have been motivated to modify the neural network of Lee in view of Eom to include the functionality of performing classification and image retrieval disclosed by Nakazawa, or use a combination of the two neural networks, to thereby classify the type of defect distribution in an inspection image and output the top three matching reference images of oxygen vacancy distribution based on similar reference data used to train the network. Based on the foregoing, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have made such modification according to known methods to yield the predictable results to have the benefit of facilitating the identification of root causes of semiconductor defects in a manufacturing process. See Nakazawa at Abstract.
Regarding claim 2, Lee in view of Eom and in further view of Nakazawa teaches the inspection device of claim 1, wherein the reference images (Lee, pg. 4678, “we mainly trained our ResUNet models with simulated STEM images (256 × 256 pixels) generated by Computem.”; The images used to train, evaluate, or otherwise provide as input to the model are reference images, e.g., “simulated and experimental images”. See Lee at pg. 4679) and the reference data (The learned data indicative of vacancies or lack thereof from “simulated STEM images” and the experimental images used to confirm the model’s accuracy are examples of reference data. See Lee at Figure 3 and pgs. 4681 and 4678) are generated by learning comparison images for a plurality of comparison targets (Lee - a plurality of output images containing targets generated by the FCN are comparison targets. Also, a single output image comprising multiple regions contains multiple comparison targets.) comprising an oxide semiconductor (The “oxide heterostructure includes a base layer of SrTiO3, a polar layer of LaAlO2, and a non-polar layer of SrTiO3”. Eom at Abstract. SrTiO3 is an oxide semiconductor.), and comparison data (data of the comparison images) indicating oxygen vacancy distributions (Eom, par. 15, “FIGS. 6A-6D depict the oxygen vacancy distribution”) of the comparison targets through the artificial neural network (Lee, pgs. 4679-4680, “The training was terminated/stopped for both vacancy and polymorph models when the validation loss did not show any further decrease”).
The rationale for obviousness is the same as provided for claim 1.
Regarding claim 3, Lee in view of Eom and in further view of Nakazawa teaches the inspection device of claim 2, wherein the comparison image is generated from a first region (Eom, FIG. 6B, “Top STO film”) of the comparison target and the comparison data is generated from a second region (Eom, FIG. 6B, “Top STO film”, “LAO” and “STO substrate”; The oxygen vacancy profile spans all three layers (regions). All three layers together form a second region. The top layer is a first region included in the second region.) of the comparison target.
The rationale for obviousness is the same as provided for claim 1.
Regarding claim 4, Lee in view of Eom and in further view of Nakazawa teaches the inspection device of claim 3, wherein the second region is larger than the first region (180 nm is larger than the thickness of the Top STO film”. See Eom at FIG. 6B).
The rationale for obviousness is the same as provided for claim 1.
Regarding claim 5, Lee in view of Eom and in further view of Nakazawa teaches the inspection device of claim 4, wherein the first region is a portion of the second region (Eom, FIG. 6B, “Top STO film” is one portion of the three layers (portions)).
The rationale for obviousness is the same as provided for claim 1.
Regarding claim 6, Lee in view of Eom and in further view of Nakazawa teaches the inspection device of claim 3, wherein the first region corresponds to a portion of the oxide semiconductor (Eom, FIG. 6B, “Top STO film”).
The rationale for obviousness is the same as provided for claim 1.
Regarding claim 8, Lee in view of Eom and in further view of Nakazawa teaches the inspection device of claim 2, wherein the comparison target includes a plurality of comparison targets (Eom, FIG. 6B, “Top STO film”, “LAO” and “STO substrate”) comprising the oxide semiconductor are different in concentration of oxygen vacancy (Oxygen vacancy density generally increases with distance. See Eom at FIG. 6B; par. 43, “The small amount of oxygen vacancies in the top STO film showed no depth-dependency, while the amount of oxygen vacancy in the bulk STO increased with depth.”; One image depicting the different densities shows a plurality of comparison targets. Multiple output images depicting the same are also a plurality of comparison targets.).
The rationale for obviousness is the same as provided for claim 1.
Regarding claim 10, Lee in view of Eom and in further view of Nakazawa teaches the inspection device of claim 2, wherein each of the inspection image and the comparison image are generated using an energy dispersive spectroscopy (EDS) (Reference EDS elemental mapping data is used to generate the network output (comparison images). Using EDS to train a model and then using the trained model to generate a new output from an input inspection image is using EDS to generate that inspection image. See Eom at par. 15).
The rationale for obviousness is the same as provided for claim 1.
Regarding claim 11, Lee in view of Eom and in further view of Nakazawa teaches the inspection device of claim 1, wherein the artificial neural network is a convolutional neural network (Lee, Figure 2, “Fully convolutional network (FCN)”).
Regarding claim 12, Lee in view of Eom and in further view of Nakazawa teaches the inspection device of claim 1, wherein the plurality of reference images are acquired using samples each having a predetermined (Lee, pg. 4681, “The vacancy concentrations estimated by the FCN model and conventional analysis show the expected linear correlation as shown in Figures 4o,p.”; The images are still images, meaning their vacancy concentrations are predetermined once they are generated.) oxygen vacancy concentration (Oxygen vacancy density generally increases with distance. See Eom at FIG. 6B; par. 43).
The rationale for obviousness is the same as provided for claim 1.
Regarding claim 13, Lee in view of Eom and in further view of Nakazawa teaches the inspection device of claim 1, further comprising a detection unit (The neural network training requires a general purpose computer to carry out the training. See Lee, Methods section, pg. 4683) configured to detect whether the inspection target is defective (Nakazawa, section IV, pg. 313, “enable classification tasks”; Classifying different types of defect distributions means classifying an inspection image as having an unexpected distribution class means the sample is defective with respect to the expected classification.) based on the oxygen vacancy distribution image (Oxygen vacancy density generally increases with distance. See Eom at FIG. 6B; par. 43.).
The rationale for obviousness is the same as provided for claim 1.
Claims 14-16, 19, and 20 substantially correspond to claims 1-3, 10, and 13 by reciting a method of inspection comprising steps that correspond to the functions of each unit of the device of claims 1-3. Claim 14 differs from claim 1 in that “a plurality of images and a plurality of reference data” are generated in claim 14 as opposed to being stored as in claim 1. However, the rejection of claim 1 applies to claim 14 for the same reasons provided to reject claim 1. Differences in dependencies between the corresponding sets of claims do not change how the cited art is applied to the corresponding limitations, e.g., claim 19 corresponds to claim 10, but includes one additional intervening claim.
The rationales for obviousness are the same as provided for claim 1-3, 10, and 13.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Eom, in view of view of Nakazawa, and in further view of U.S. Pat. No. 9252283 to Matsubayashi et al. (hereinafter “Matsubayashi”).
Regarding claim 7, Lee in view of Eom and in further view of Nakazawa teaches the inspection device of claim 3, but does not teach that which is explicitly taught by Matsubayashi.
Matsubayashi teaches wherein an area (e.g., channel width x channel height) of the first region is about 900 nm2 to about 1600 nm2 (Matsubayashi, Abstract, “The Semiconductor device includes an oxide Semiconductor Stack”; col. 21, l. 60 - col. 22, l. 3, “In the fourth simulation, for a device model having a channel length of 30 nm and a channel width of 40 nm, the thickness of the second oxide semiconductor layer 532 is 5 nm to 90 nm. For a device model having a channel length of 30 nm and a channel width of 300 nm, the thickness of the second oxide semiconductor layer 532 is 5 nm to 50 nm.”; One possible channel area is 1200 nm2, which is obtained by 30 nm x 40 nm = 1200 nm2).
Lee in view of Eom and in further view of Nakazawa is analogous to the claimed invention for the reasons provided above. Matsubayashi discloses various designs for a channel region of an oxide semiconductor transistor, including a 1200 nm2 region. Thus, Matsubayashi shows that it was known in the art before the effective filing date of the claimed invention to model different sizes of oxide semiconductor regions, which is analogous to the claimed invention in that it is pertinent to the problem being solved by the claimed invention, improving the detection of semiconductor defects in a designed oxide semiconductor.
A person of ordinary skill in the art would have been motivated to combine model design data for an oxide semiconductor layer disclosed by Matsubayashi with the inspection device of Lee in view of Eom and in further view of Nakazawa, to thereby assess an oxygen vacancy distribution in a layer having a size within the range of 900 nm2 to about 1600 nm2. Based on the foregoing, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have made such modification according to known methods to yield the predictable results to have the benefit of testing samples with different size layers when optimizing the design of an oxide semiconductor for a manufacturing process thereof.
Claims 9, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Eom, in view of Nakazawa, and in further view of The formation and detection techniques of oxygen vacancies in titanium oxide-based nanostructures to Sarkar et al. (hereinafter “Sarkar”).
Regarding claim 9, Lee in view of Eom and in further view of Nakazawa teaches the inspection device of claim 2, but does not teach that which is taught by Sarkar.
Sarkar teaches wherein the comparison data is generated using X-ray photoelectron spectroscopy (XPS) (Sarkar, pg. 3429, section 6.4, “X-ray photoelectron spectroscopy is a very useful and sophisticated tool for investigating the chemical constituents of a material, the ionic states of the constituent elements, the ratio of the amounts of the different ionic states of a single constituent element, etc. Evidence of oxygen vacancies and other defects in nanostructures can also be confirmed by proper XPS analysis.”; X-ray Photoelectron Spectroscopy is a surface analysis technique that irradiates a sample with x-rays. Sarkar at pg. 3429, section 6.4.).
Lee in view of Eom and in further view of Nakazawa is analogous to the claimed invention for the reasons provided above. Sarkar discloses X-ray photoelectron spectroscopy (XPS) as being useful for confirming the amount of oxygen vacancies in a targeted semiconductor sample. Thus, Sarkar shows that it was known in the art before the effective filing date of the claimed invention to use XPS to assess oxygen vacancies in oxide semiconductor samples, which is analogous to the claimed invention in that it is pertinent to the problem being solved by the claimed invention, improving the detection of semiconductor defects.
A person of ordinary skill in the art would have been motivated to combine XPS analysis as disclosed by Sarkar with the inspection device disclosed by Lee in view of Eom and in further view of Nakazawa, to thereby use XPS to verify oxygen vacancies in an oxide semiconductor sample before its image is processed by the neural network model to produce output images (comparison images). Based on the foregoing, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have made such modification according to known methods to yield the predictable results to have the benefit of expediting the design process of an oxide semiconductor.
Claim 17 substantially corresponds to claim 9 by reciting a method of inspection comprising a step that corresponds to the function of the device of claim 9. The difference in claim dependencies do not affect how the prior art is applied to the claims.
The rationale for obviousness is the same as provided for claim 9.
Regarding claim 18, Lee in view of Eom, in view of view of Nakazawa, and in further view of Sarkar teaches the method of claim 17, wherein the comparison data is generated by a light source (X-ray source) disposed to emit light having a predetermined angle (the angle between the x-rays and the sample) with respect to a surface of the comparison target (X-ray Photoelectron Spectroscopy is a surface analysis technique that irradiates a sample with x-rays. See Sarkar at pg. 3429, section 6.4.).
The rationale for obviousness is the same as provided for claim 9.
Conclusion
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/RYAN P POTTS/Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672