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 .
Specification
The abstract of the disclosure is objected to because of undue length. 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 content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
Claim Objections
Claim 20 is objected to because of the following informalities: the header recites “The system of Claim 19, the non-transitory, computer-readable medium further including instructions”, for clarity the Examiner suggests --The system of Claim 19, wherein the non-transitory, computer-readable medium further includes instructions--. Appropriate correction is required.
Double Patenting
An analysis of a nonstatutory double patenting rejection was thoroughly considered for this application with U.S. Patent 11,520,321. The differences between the instant application and the reference application in a claim to claim analysis include the use of the terms regularization measurement data, whole wafer measurement model and an optimization function regularized by measurement performance metrics. These limitations are not rendered an obvious addition by the prior art to the instant application to one of ordinary skill in the art. Ultimately the Examiner determined that a nonstatutory double patenting rejection was not appropriate. Applicant is reminded to maintain a clear line of demarcation between the applications. See MPEP § 822.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 13-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 13 is a method which recites determining an estimated value of a parameter of interest characterizing each instance of the one or more structures at each of the plurality of measurement sites across the wafer based on the amount of measurement data using a trained whole wafer measurement model valid across the wafer, wherein the trained whole wafer measurement model is evaluated based on the amount of measurement data at each of the plurality of measurement sites.
In the context of the claim, given the broadest reasonable interpretation in light of the specification, determine an estimated value encompasses using a neural network (see e.g. [0085]) which a computational network. Thus, the limitation is implemented using mathematical relationships, formulas, equations or calculations (e.g. [0010]) and falls within the Mathematical Concepts grouping of abstract ideas. See MPEP 2106.04(a)(2) I. The claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claim recites the additional claim limitations of collecting an amount of measurement data from each of a plurality of measurement sites across a wafer, each measurement site including one or more instances of one or more structures disposed on the wafer and using a trained whole wafer measurement model.
The limitation of collecting an amount of measurement data is recited at a high level of generality, without imposing any other meaningful limitation, such that it amounts to no more than mere necessary data gathering and outputting. As such, it amounts to adding insignificant extra-solution activity. See MPEP 2106.05(g). The recitation of using a trained whole wafer measurement model without reciting any details of how the trained model aids in determining the estimated value is, at best, nothing more than providing mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). Further, it merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Even when view in combination, these additional elements do not integrate the recited judicial exception into a practical application. The claim is directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional claim elements either amount to insignificant extra-solution activity or amount mere instructions to implement an abstract idea on a generic computer. Merely adding insignificant extra-solution activity cannot provide an inventive concept. Additionally, the background of the specification (e.g. [0003]-[0004]) and the prior art (US 20210165398, [0059]-[0060]) establishes that collecting an amount of measurement data from each of a plurality of measurement sites across a wafer, each measurement site including one or more instances of one or more structures disposed on the wafer is known in the art. As such, the limitation is well-understood, routine and conventional activity. See MPEP 2106.05(d) II. Mere instructions to implement an abstract idea on a generic computer cannot provide an inventive concept. The claim is not patent eligible.
Claims 14-18 depend from claim 13 and recite the same abstract idea as claim 13. The additional claim elements recited serve to add additional steps to the abstract idea (clams 14-17), amount to insignificant extra-solution activity (claims 14-15) or merely use a computer to implement the abstract idea (claim 18). The steps of determining an estimated value, estimating values of coefficients, determining the value of the parameter, determining the reference values and determining the amount of Design of Experiments are implemented using mathematical relationships, formulas, equations or calculations (e.g. [0010]) and falls within the Mathematical Concepts grouping of abstract ideas. See MPEP 2106.04(a)(2)I.
The receiving steps in claims 14-15 are recited at a high level of generality, without imposing any other meaningful limitation, such that it amounts to no more than mere necessary data gathering and outputting. As such, it amounts to adding insignificant extra-solution activity. See MPEP 2106.05(g). Additionally, the limitations amount to no more than receiving and or transmitting data over a network, and as such, are well-understood, conventional, routine activity. See MPEP 2106.05(d) II. The recitation of the trained whole wafer measurement model is physics based or machine learning based without reciting any details of how the trained model aids in determining the estimated value is, at best, nothing more than providing mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). Further, it merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Even when view in combination, these additional elements do not integrate the recited judicial exception into a practical application.
The additional claim elements neither integrate the recited judicial exception into a practical application nor recite additional limitations that amount to significantly more. The claims are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 8-14 and 18-20 is/are rejected under 35 U.S.C. 102(a)(1) and/or 102(a)(2) as being anticipated by Pandev et al. in U.S. Patent Publication 2021/0165398 (see IDS filed 09/03/2024).
Regarding claim 1, Pandev et al. teaches:
A system (Abstract, Fig. 1) comprising:
a metrology system (see “metrology system”, Abstract, [0022]-[0025]) including an illumination source (see “an illuminator 102”, [0043], Fig. 1) and a detector (see “a spectrometer 104”, [0043], Fig. 1) configured to collect an amount of measurement data from each of a plurality of measurement sites across a wafer (see “generate and direct illumination of a selected wavelength range (e.g., 100-2500 nm) to the structure disposed on the surface of the specimen over a measurement spot 110…receive illumination reflected from structure 101. It is further noted that the light emerging from the illuminator 102 is polarized”, [0043], Fig. 1), each measurement site including one or more instances of one or more structures disposed on the wafer (see “actual measurement data collected from multiple instances of one or more regularization structures disposed on one or more wafers”, [0042]; [0046]); and
a computing system (see “the computing system 130”, [0043]-0046], Fig. 1) configured to:
receive the amount of measurement data from each of the plurality of measurement sites on the wafer (see “These spectra 111 are passed to the computing system 130”, [0043], Fig. 1; [0045]); and
determine an estimated value of a parameter of interest characterizing each instance of the one or more structures at each of the plurality of measurement sites across the wafer based on the amount of measurement data using a trained whole wafer measurement model valid across the wafer (see “the trained measurement model is employed to estimate values of parameters of interest based on measurements of structures…The trained measurement model is employed to estimate values of one or more parameters of interest from actual measurement data (e.g., measured spectra) collected by the measurement system (e.g., metrology system 100)…”, [0056]-[0057]), wherein the trained whole wafer measurement model is evaluated based on the amount of measurement data at each of the plurality of measurement sites (see “estimate values of parameters of interest based on a single measured spectrum or estimate values of parameters of interest simultaneously based on multiple spectra”, [0057]).
Regarding claim 2, Pandev et al. teaches the limitations of claim 1 as indicated above. Further, Pandev et al. teaches the computing system (see “the computing system 130”, [0043]-0046], Fig. 1; claim 1) further configured to: receive an amount of Design of Experiments (DOE) measurement data associated with measurements of one or more DOE instances of the one or more structures at each of a plurality of DOE measurement sites (see “receive an amount of Design of Experiments (DOE) measurement data associated with measurements of one or more Design of Experiments (DOE) metrology targets”, claim 1); receive reference values of one or more parameters of interest characterizing the one or more DOE instances of the one or more structures at each of the plurality of DOE measurement sites (see “ reference values of parameters of interest associated with the DOE metrology targets, actual measurement data collected from multiple instances of one or more regularization structures disposed on one or more wafers”, [0018], [0046]; see “receive known, reference values of one or more parameters of interest associated with the DOE metrology targets; receive the regularization measurement data; receive values of one or more measurement performance metrics associated with the regularization measurement data”, claim 1); and iteratively (see “…an iterative process (e.g., regression)…values of the weight parameters are iteratively adjusted…For each iteration…”, [0008]-[0012]) train the whole wafer measurement model based on the amount of DOE measurement data and the corresponding reference values at the plurality of DOE measurement sites in parallel (see “The training is based on measurement data associated with multiple instances of one or more Design of Experiments (DOE) metrology targets disposed on one or more wafers, reference values of parameters of interest associated with the DOE metrology targets, actual measurement data collected from multiple instances of one or more regularization structures disposed on one or more wafers, and measurement performance metrics associated with the actual measurement data”, [0042]).
Regarding claim 8, Pandev et al. teaches the limitations of claim 2 as indicated above. Further, Pandev et al. teaches wherein the reference values of one or more parameters of interest characterizing the one or more DOE instances of the one or more structures at each of the plurality of DOE measurement sites are generated by a process simulator (see “The simulated data is generated from a parameterized model of the measurement of each of the one or more DOE metrology structures by the metrology system”, ]0024]; see “ the reference values 155 are simulated. In these embodiments, the reference source 156 is a simulation engine that generates the corresponding simulated DOE measurement data 153 for known reference values 156”, [0046]).
Regarding claim 9, Pandev et al. teaches the limitations of claim 2 as indicated above. Further, Pandev et al. teaches wherein the reference values of one or more parameters of interest characterizing the one or more DOE instances of the one or more structures at each of the plurality of DOE measurement sites are measured by a trusted, reference metrology system (see “ In some embodiments, the reference values 155 are values measured by a trusted measurement system (e.g., a scanning electron microscope, etc.).”, [0046]).
Regarding claim 10, Pandev et al. teaches the limitations of claim 1 as indicated above. Further, Pandev et al. teaches wherein the trained whole wafer measurement model is machine learning based (see “a trained machine learning based measurement model ”, [0009]-[0013]).
Regarding claim 11, Pandev et al. teaches the limitations of claim 1 as indicated above. Further, Pandev et al. teaches wherein the trained whole wafer measurement model is physics based (see “a physics based measurement model is also trained”, [0012]-[0013]).
Regarding claim 12, Pandev et al. teaches the limitations of claim 1 as indicated above. Further, Pandev et al. teaches wherein the amount of measurement data includes measurements of the one or more structures by at least one optical based metrology system, at least one x-ray based metrology system, or any combination thereof (see “the measurement models are implemented as an element of a SpectraShape® optical critical-dimension metrology system”, [0092]).
Regarding claim 13, Pandev et al. teaches:
A method (Abstract, Fig. 6) comprising:
collecting an amount of measurement data from each of a plurality of measurement sites across a wafer (see “ the actual regularization measurement data collected from the multiple instances of the one or more regularization structures is collected by a particular metrology system…the actual regularization measurement data collected from the multiple instances of the one or more regularization structures is collected by multiple instances of a metrology system”, [0059]-[0060]; [0056]), each measurement site including one or more instances of one or more structures disposed on the wafer (see “the actual regularization measurement data collected from the multiple instances of the one or more regularization structures is collected by multiple instances of a metrology system”, [0060]); and
determining an estimated value of a parameter of interest characterizing each instance of the one or more structures at each of the plurality of measurement sites across the wafer based on the amount of measurement data using a trained whole wafer measurement model valid across the wafer (see “the trained measurement model is employed to estimate values of parameters of interest based on measurements of structures…The trained measurement model is employed to estimate values of one or more parameters of interest from actual measurement data (e.g., measured spectra) collected by the measurement system (e.g., metrology system 100)…”, [0056]-[0057]), wherein the trained whole wafer measurement model is evaluated based on the amount of measurement data at each of the plurality of measurement sites (see “estimate values of parameters of interest based on a single measured spectrum or estimate values of parameters of interest simultaneously based on multiple spectra”, [0057]).
Regarding claim 14, Pandev et al. teaches the limitations of claim 13 as indicated above. Further, Pandev et al. teaches receiving an amount of Design of Experiments (DOE) measurement data associated with measurements of one or more DOE instances of the one or more structures at each of a plurality of DOE measurement sites (see “receive an amount of Design of Experiments (DOE) measurement data associated with measurements of one or more Design of Experiments (DOE) metrology targets”, claim 1); receiving reference values of one or more parameters of interest characterizing the one or more DOE instances of the one or more structures at each of the plurality of DOE measurement sites (see “ reference values of parameters of interest associated with the DOE metrology targets, actual measurement data collected from multiple instances of one or more regularization structures disposed on one or more wafers”, [0018], [0046]; see “receive known, reference values of one or more parameters of interest associated with the DOE metrology targets; receive the regularization measurement data; receive values of one or more measurement performance metrics associated with the regularization measurement data”, claim 1); and iteratively (see “…an iterative process (e.g., regression)…values of the weight parameters are iteratively adjusted…For each iteration…”, [0008]-[0012]) training the whole wafer measurement model based on the amount of DOE measurement data and the corresponding reference values at the plurality of DOE measurement sites in parallel (see “The training is based on measurement data associated with multiple instances of one or more Design of Experiments (DOE) metrology targets disposed on one or more wafers, reference values of parameters of interest associated with the DOE metrology targets, actual measurement data collected from multiple instances of one or more regularization structures disposed on one or more wafers, and measurement performance metrics associated with the actual measurement data”, [0042]).
Regarding claim 18, Pandev et al. teaches the limitations of claim 13 as indicated above. Further, Pandev et al. teaches wherein the trained whole wafer measurement model is physics based or machine learning based (see “…Both physics based measurement models and machine learning based measurement models…the training of both machine learning based measurement models and physics based measurement models…”, [0009]-[0013]).
Regarding claim 19, Pandev et al. teaches:
A system (Abstract, Fig. 1) comprising:
a metrology system (see “metrology system”, Abstract, [0022]-[0025]) including an illumination source (see “an illuminator 102”, [0043], Fig. 1) and a detector (see “a spectrometer 104”, [0043], Fig. 1) configured to collect an amount of measurement data from each of a plurality of measurement sites across a wafer (see “generate and direct illumination of a selected wavelength range (e.g., 100-2500 nm) to the structure disposed on the surface of the specimen over a measurement spot 110…receive illumination reflected from structure 101. It is further noted that the light emerging from the illuminator 102 is polarized”, [0043], Fig. 1), each measurement site including one or more instances of one or more structures disposed on the wafer (see “actual measurement data collected from multiple instances of one or more regularization structures disposed on one or more wafers”, [0042]; [0046]); and
a non-transitory, computer-readable medium including instructions (see “execute instructions from a memory medium…”, [0109]-[0110]) that when executed by one or more processors of a computing system (see “the computing system 130”, [0043]-0046], Fig. 1) cause the computing system to:
receive the amount of measurement data from each of the plurality of measurement sites on the wafer (see “These spectra 111 are passed to the computing system 130”, [0043], Fig. 1; [0045]); and
determine an estimated value of a parameter of interest characterizing each instance of the one or more structures at each of the plurality of measurement sites across the wafer based on the amount of measurement data using a trained whole wafer measurement model valid across the wafer (see “the trained measurement model is employed to estimate values of parameters of interest based on measurements of structures…The trained measurement model is employed to estimate values of one or more parameters of interest from actual measurement data (e.g., measured spectra) collected by the measurement system (e.g., metrology system 100)…”, [0056]-[0057]), wherein the trained whole wafer measurement model is evaluated based on the amount of measurement data at each of the plurality of measurement sites (see “estimate values of parameters of interest based on a single measured spectrum or estimate values of parameters of interest simultaneously based on multiple spectra”, [0057]).
Regarding claim 20, Pandev et al. teaches the limitations of claim 19 as indicated above. Further, Pandev et al. teaches the non-transitory, computer-readable medium further including instructions (see “execute instructions from a memory medium…”, [0109]-[0110]) that when executed by one or more processors of the computing system cause the computing system (see “one or more processors of computing system 130”, [0064], Fig. 1) to: receive an amount of Design of Experiments (DOE) measurement data associated with measurements of one or more DOE instances of the one or more structures at each of a plurality of DOE measurement sites (see “receive an amount of Design of Experiments (DOE) measurement data associated with measurements of one or more Design of Experiments (DOE) metrology targets”, claim 1); receive reference values of one or more parameters of interest characterizing the one or more DOE instances of the one or more structures at each of the plurality of DOE measurement sites (see “ reference values of parameters of interest associated with the DOE metrology targets, actual measurement data collected from multiple instances of one or more regularization structures disposed on one or more wafers”, [0018], [0046]; see “receive known, reference values of one or more parameters of interest associated with the DOE metrology targets; receive the regularization measurement data; receive values of one or more measurement performance metrics associated with the regularization measurement data”, claim 1); and iteratively (see “…an iterative process (e.g., regression)…values of the weight parameters are iteratively adjusted…For each iteration…”, [0008]-[0012]) train the whole wafer measurement model based on the amount of DOE measurement data and the corresponding reference values at the plurality of DOE measurement sites in parallel (see “The training is based on measurement data associated with multiple instances of one or more Design of Experiments (DOE) metrology targets disposed on one or more wafers, reference values of parameters of interest associated with the DOE metrology targets, actual measurement data collected from multiple instances of one or more regularization structures disposed on one or more wafers, and measurement performance metrics associated with the actual measurement data”, [0042]).
Allowable Subject Matter
Claims 3-7 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Sriraman et al. in Foreign Patent Document WO 2019199697 teaches “a) receiving after development inspection metrology results produced from one or more first test substrates on which resist was applied and patterned using a set of design layout segments; (b) receiving after etch inspection metrology results produced from one or more second test substrates which were etched after resist was applied and patterned using said set of design layout segments; and (c) generating the transfer function using the set of design layout segments together with corresponding after development inspection metrology results and corresponding after etch inspection metrology results” (Abstract).
Chao et al. in U.S. Patent 11,199,505 teaches “A method for machine learning enhanced optical-based screening for in-line wafer testing includes receiving optical spectra data for a wafer-under-test by performing scatterometry on the wafer-under-test, performing predictive model screening by applying a predictive model based on the optical spectra data, determining whether a device associated with the wafer-under-test is defective based on the predictive model screening, and if the device is determined to be defective, dynamically modifying a yield map associated with the wafer-under-test, including reassigning at least one die” (Abstract).
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/MI'SCHITA' HENSON/ Primary Examiner, Art Unit 2857