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 Arguments regarding the Restriction Requirement
The applicant’s election with traverse of Group I, claims 1, 4-8, and 17-20, is
acknowledged.
The applicant’s argument regarding the lack-of-unity requirement have been fully considered but is not persuasive. Although the applicant identifies certain features generally common to the claimed inventions, including first measurement data obtained using a first measurement tool, second measurement data obtained using a second measurement tool, and a model determined or configured based on such data, the mere presence of common technical subject matter does not, by itself, establish unity of invention. Under 37 CFR 1.475(a), unity requires a technical relationship among the claimed inventions involving one or more of the same or corresponding special technical features, i.e., technical features that define a contribution which each of the claimed inventions, considered as a whole, makes over the prior art. The broadly recited common aspects identified by the applicant do not constitute the same or corresponding special technical features linking the claimed inventions. Rather, the respective groups rely on different technical features defining their respective contributions: Group I is directed to mapping measured data to virtual data associated with virtual tool and converting measured data based on mapping functions; Group II is directed to determining a model based on measurement data from different tools, including the claimed difference-based, projection, constraint, and model-optimization techniques; and Group III is directed a metrology-tool apparatus including a sensor and processor configured to determine a physical characteristic using the model. These features are neither the same nor corresponding and therefore do not establish the technical relationship necessary for a single general inventive concept. The applicant’s comments regarding potential divisional applications, double patenting, and possible patent-term consequences do not establish that the present claimed inventions share the same or corresponding special technical features and therefore do not overcome the lack-of-unity requirement. Accordingly, applicant’s traversal is persuasive, and the lack-of-unity requirement is maintained and made FINAL.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 4-8 and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
Under Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed to a manufacture (claim 1, a non-transitory computer-readable medium), which are statutory categories.
However, evaluating claim 1, under Step 2A, Prong One, the claim is directed to the judicial exception of an abstract idea using the grouping of a mathematical relationship/mental process. The limitations include:
obtain (i) training data comprising a first set of measured data associated with a first set of one or more patterned substrates using a first measurement tool, and reference measurements of a physical characteristic associated with the first set of one or more patterned substrates, (ii) a second set of measured data associated with a second set of one or more patterned substrates that is measured using a second set of one or more measurement tools, the second set of one or more measurement tools being different from the first measurement tool, and (iii) virtual data based on the second set of measured data, the virtual data being associated with a virtual tool; generate a set of mapping functions between the second set of measured data and the virtual data, each mapping function mapping each measured data of the second set of measured data to the virtual data; convert, based on the set of mapping functions, the first set of measured data of the training data; and determine a model based on the reference measurements and the converted first set of measured data such that the model predicts values of the physical characteristic that are within an acceptable threshold of the reference measurements.
The recited mapping functions, mathematical conversion of measured data using the mapping functions, determination of a model from the converted data and reference measurements, prediction of values using the model, and evaluation of the predicted values relative to an acceptable threshold constitute mathematical relationships, mathematical calculations, and/or mathematical operations and therefore fall within the “mathematical concepts” grouping of abstract ideas.
Accordingly, claim 1 recites a judicial exception.
Next, Step 2A, Prong Two evaluates whether additional elements of the claim “integrate the abstract idea into a practical application” in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The claim does not recite additional elements that integrate the judicial exception into a practical application.
The recited mapping functions, mathematical conversion of measured data using the mapping functions, determination of a model from the converted data and reference measurements, prediction of values using the model, and evaluation of the predicted values relative to an acceptable threshold constitute mathematical relationships, mathematical calculations, and/or mathematical operations and therefore fall within the “mathematical concepts” grouping of abstract ideas.
Accordingly, claim 1 recites a judicial exception.
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas. As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application. Therefore, the claim is directed to an abstract idea.
The additional elements beyond the mathematical concepts include the non-transitory computer-readable medium, one or more processors, the first and second sets of patterned substrates; the first and second sets of measurement tools; obtaining the first and second sets of measured data using those tools; and obtaining reference measurements of the physical characteristic.
Considered individually and in combination, these additional elements do not integrate the mathematical concepts into a practical application. The processors and computer-readable medium merely provide a computer environment in which mathematical mapping, conversion, and model-determination operations are performed. The claim does not recite a particular improvement to operation of the processor or computer-readable medium.
The recitation of patterned substrates and metrology tools limit the information being mathematically processed to the field of semiconductor metrology but do not require the resulting model to control a metrology tool, modify manufacture of the patterned substrate, control a semiconductor fabrication process, or otherwise effect a physical operation using the result of the mathematical processing.
Further, obtaining measured data from the first and second measurement tools and obtaining reference measurements supply the information upon which the mapping functions and model operate. These limitations constitute data-gathering and thus insignificant extra-solution activity (MPEP § 2106.05(g)).
Accordingly, claim 1 does not integrate the judicial exception into a practical application and is directed to the abstract idea.
At Step 2B, consideration is given to additional elements that may make the abstract idea significantly more. Under Step 2B, there are no additional elements that make the claim significantly more than the abstract idea.
The non-transitory computer-readable medium and one or more processors are recited at a high level of generality and merely provide implementation for the recited mathematical operations. Likewise, obtaining measurement and reference data supplies input information for the mathematical mapping, conversion, and model-determination operations. Nothing in the claim requires a specialized computer architecture or technological operation beyond obtaining the input data and performing the recited mathematical processing. Considered as an ordered combination, the additional elements merely provide the technological environment and input data in which the mathematical mapping and model-generation operations are performed and do not amount to “significantly more” that the abstract idea.
Accordingly, claim 1 does not recite significantly more that the judicial exception and is ineligible under 35 U.S.C. § 101.
Dependent claims 5-7 and 17-20 do not add anything which would render the claimed invention a patent eligible application of the abstract idea. The claim merely extends (or narrow) the abstract idea which do not amount for "significant more" because it merely adds details to the algorithm which forms the abstract idea as discussed above.
Claim 8 is considered patent eligible under 35 U.S.C. § 101. Although claim 8
Incorporates the mathematical concepts recited in claim 4, including generation of mapping functions, conversion of measured data, and determination of a predictive model, claim 8 further requires capturing, via metrology tool, signals associated with a portion of a patterned substrate and executing the determined model using the captured signals as input to determine measurements of a physical characteristic associated with the patterned substrate. When the claim is considered as a whole under Step 2A, Prong Two, these additional limitations meaningfully apply the mathematical model in a semiconductor-metrology process: the metrology tool acquires signals from the physical patterned substrate, and the determined model is then applied to those acquired signals to determine the physical characteristic of the substrate. Thus, unlike merely generating a model or mathematical result, claim 8 affirmatively uses the recited mathematical concepts in carrying out the claimed metrology operation. The claim therefore integrates the recited judicial exception into practical application and is not directed to the judicial exception. Accordingly, claim 8 is eligible under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-6, 8 and 17-20 are rejected under 35 U.S.C. 103 as being
unpatentable over Pandev et al. (Pub. No. US 2016/0141193) (hereinafter Pandev) in view of Stanke et al. (Pub. No. US 2007/0268498) (hereinafter Stanke).
As per claims 1, 4 and 19, Pandev teaches a computer-implemented system, method, and computer-program-product arrangement for combining raw data from multiple metrology tools and generating a training model therefrom (see ¶¶ [0043]-[0046]).
Pandev further teaches obtaining training data associated with one or more patterned substrates and reference measurements of physical characteristics thereof. In particular, Pandev describes a training component, such as DOE training wafer, and obtaining reference values for parameters of the training component, including reference values obtainable using metrology techniques such as CD-SEM, TEM, and AFM (see ¶¶ [0045]- [0049]).
Pandev further teaches collecting a first set of measurement signals using a first metrology tool and collecting a second set of measurement signals using a second metrology tool different from the first metrology tool (see ¶¶ [0050] and [0052]).
Pandev further teaches preprocessing and combining signals from the different metrology tools and transforming the combined signals into a third set of signals constituting a unified dataset (see ¶ [0053]). Pandev identifies PCA and ICA as exemplary transformations (see ¶ [0054]).
Pandev further teaches determining a relationship between the transformed signals and the reference values (see ¶ [0055]). And creating a training model based on that relationship (see ¶ [0056]). Pandev explains that the model may include, for example, a linear model, neural network, support vector machine, or other machine-learning model (see ¶ [0056]). The trained model is subsequently applied to measurements from a target component to determine parameter values (see ¶ [0058], i.e., predictive model).
Pandev fails to explicitly characterize the common or unified representation as virtual data” associated with a “virtual tool”, nor does Pandev explicitly disclose, a respective mapping function mapping each measured data of the second set of measured data to such virtual data.
Stanke, however, teaches matching measurements generated by different optical metrology tools. Stanke teaches obtaining measured diffraction signals from first and second optical metrology tools (see ¶¶ [0008] and [0057]-[0058]), and obtaining a common reference diffraction signal (see ¶ [0061]). Stanke further teaches that the reference may be based on selected reference tool or may be generated from measurements of the tools, including by averaging the measured diffraction signals (see ¶ [0060]).
Stanke further teaches generating a plurality of transforms relative to the common reference diffraction signal. In particular, a first transform is generated from measurements of the first tool relative to the reference diffraction signal and a second transform is generated from measurements of the second tool relative to the same reference diffraction signal (see ¶ [0061], the examiner notes that the reference diffraction signal corresponds to the claimed virtual data). Stanke, therefore, obtains subsequent measurements from respective tools and applies the corresponding transforms to those measurements to produce transformed signals (see Fig. 5 and ¶ [0064]).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify Pandev’s multi-tool training architecture to employ Stanke’s respective tool-specific transforms to a common reference representation, such that measurement data from the different tools are mapped to common virtual/reference data before model generation, because Stanke teaches that the tool-specific transforms compensate for differences among metrology tools (see ¶ [0007]) and improve matching of their measurement results, thereby providing Pandev’s predictive training model with measurement data represented consistently in a common reference measurement space.
Accordingly, Pandev in view of Stanke teaches or suggests obtaining virtual data based on measurements from different metrology tools, generating respective mapping functions between the measured data and the common virtual/reference data, converting measured data using those mapping functions, and determining a predictive model based on the converted data and reference measurements. Therefore, claim 1 would have been obvious over Pandev in view of Stanke.
Regarding the limitation of claim 4 “that the model predicts values of the physical characteristic that are within an acceptable threshold of the reference measurements”, Pandev teaches that the metrology analysis may employ a parametric model having predefined parameter values and value ranges and that such a model provides a mathematical approximation that reproduces the solution for the target component with “reasonable accuracy” (see ¶ [0004]). Accordingly, although Pandev does not explicitly recite the phrase “within an acceptable threshold of the reference measurements”, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to determine the training model such that the physical-parameter values produced by the model are within an acceptable accuracy threshold relative to the known reference values used to establish the model, because Pandev explicitly teaches constructing the model from the relationship between the measurement signals and known reference values for purposes of obtaining parameter values with reasonable accuracy, thereby ensuring that the trained metrology model provides sufficient accurate physical-characteristic measurements.
As per claims 5 and 18, the combination of Pandev and Stanke teaches the system as stated above. Pandev further teaches measurement of physical characteristics of semiconductor structures, including critical dimensions and overlay-related metrology parameters (see ¶ [0017]).
As per claims 6 and 20, the combination of Pandev and Stanke teaches the system as stated above. Stanke further teaches obtaining measured diffraction signals from different optical metrology tools (see ¶ [0059]), obtaining reference diffraction signal, wherein the reference diffraction signal may be averaging the measured diffraction signals obtained from different tools (see ¶ [0060])), generating a first transform based on the first-tool measured diffraction signals and the reference diffraction signal and generating a second transform based on the second-tool measured diffraction signals and the same reference diffraction signal ((see ¶ [0061]). Stanke further teaches applying the respective transforms to subsequently obtained measurements from the corresponding tools to produce transformed diffraction signals (see ¶ [0064]). Thus, Stanke teaches respective tool-specific transforms for transforming measurement data obtained from different physical metrology tools with respect to a common reference diffraction signal.
Stanke mathematically defines the reference diffraction signals as an average across the tools, determines a respective transform for each tool relative to that reference, and applies the respective transform to data obtained from the corresponding tool (see ¶¶ [0073]-[0076]).
Thus, Stanke teaches respective mapping functions that map measurement data obtained from different physical metrology tools to a common mathematically generated reference representation. It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to employ Stanke’s common reference representation as the virtual data of the virtual tool in the multi-tool metrology system of Pandev, such that each respective mapping function maps the measured data of the corresponding physical measurement tool to the common virtual data, because Stanke teaches that tool-specific transforms referenced to a common representation compensate for deterministic differences among metrology tools, thereby permitting measurement data obtained from different physical tools to be represented om a common measurement basis.
As per claim 8, the combination of Pandev and Stanke teaches the system as stated above. Pandev further teaches generating the training model, collecting measurement signals from a target component and applying the generated training model to those signals to determine parameter values of the target component (see ¶ [0058]). Pandev further describes optical metrology arrangements for obtaining signals from semiconductor targets (see Abstract and ¶ [0003]). Thus, Pandev teaches capturing, via a metrology tool, signals associated with a portion of a patterned substrate and executing the determined model using those signals as input to determine measurements of the physical characteristic.
As per claim 17, the combination of Pandev and Stanke teaches the system as stated above. Pandev further teaches collecting a first set of measurement signals using the first metrology tool (see ¶ [0017]) and describes optical metrology tools having illumination and collection/detection systems for obtaining measurement signals from semiconductor targets (see ¶ [0017]). Accordingly, Pandev teaches or suggests that the first set of measured data comprises signals detected by a sensor or the first measurement tool configured to measure a portion of a patterned substrate.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Pandev
in view of Stanke and further in view of Thattaisundaram et al. (Pub. No. US 2012/0116733) (hereinafter Thattaisundaram).
As per claim 7, the combination of Pandev and Stanke teaches the system as stated above except for creating, based on the set of mapping functions, a recipe of the virtual tool, the recipe comprising configuration of one or more tool characteristics used during a measurement.
Thattaisundaram, however teaches wafer inspection/metrology tool-to-tool matching using models that characterize differences between different tools. In particular, Thattaisundaram teaches modeling tool-to-tool variability and generating a model for each tool relative to a “golden” or reference tool (see ¶¶ [0031] and [0052]). Thattaisundaram further teaches using model-generated or perturbed results to generate a matching recipe (see ¶¶ [0039] and [0041]) and describes generating a matching recipe in connection with the golden/reference-tool model (see ¶ [0052]).
Thattaisundaram further teaches that the recipe includes configuration of adjustable tool characteristics used during inspection or metrology, including illumination wavelength, illumination angle, polarization, pixel-related parameters, apertures, and other acquisition/measurement setting (see ¶ [0028]).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify Pande-Stanke system to generate a metrology recipe based on the modeled tool-to-reference relationships in the manner taught by Thattaisundaram because Thattaisundaram teaches that model-based generation of matching recipes facilitates tool matching without repeatedly transferring and empirically adjusting recipes among multiple tools, thereby reducing tool time and engineering effort while maintaining matched measurement performance.
Prior art
The prior art made record and not relied upon is considered pertinent to applicant’s
disclosure:
Smith et al. [‘410] discloses a method comprising selecting sites to be measured on a device that is to be fabricated using at least one fabrication process, the sites being selected based on a pattern-dependent model of the process. A metrology tool to measure a parameter of a semiconductor device includes a control element to select sites for measurement based on a pattern dependent model of a process with respect to the device. Problematic areas, within a chip or die and within a wafer, are identified that result from process variation. The variation is identified and characterized, and the location of each site is stored. The sites may be manually entered into a metrology tool or the method will automatically generate a measurement plan. Process variation and electrical impact are used to direct the measurement of within-die and wafer-level integrated circuit locations.
Poslavsky et al. [‘872] discloses methods and systems for determining a meta-model to correct model-based measurements are presented. Such systems are employed to measure structural and material characteristics (e.g., material composition, dimensional characteristics of structures and films, etc.) associated with different semiconductor fabrication processes. In one aspect, model-based measurement parameter values are corrected based on a meta-model that maps specimen parameter values determined based on the measurement model to reference parameter values determined based on a more accurate reference measurement. In another aspect, parameters of a meta-model are determined such that errors between reference parameter values and specimen parameter values determined based on the measurement model are minimized. In some embodiments, the accuracy of a corrected parameter value is an order of magnitude greater than the uncorrected parameter value.
Flock et al. [‘985] discloses methods and systems for matching critical dimension measurement applications at high precision across multiple optical metrology systems are presented. In one aspect, machine parameter values of a metrology system are calibrated based on critical dimension measurement data. In one further aspect, calibration of the machine parameter values is based on critical dimension measurement data collected by a target measurement system from a specimen with assigned critical dimension parameter values obtained from a reference measurement source. In another further aspect, the calibration of the machine parameter values of a target measurement system is based on measurement data without knowledge of critical dimension parameter values. In some examples, the measurement data includes critical dimension measurement data and thin film measurement data. Calibration of machine parameter values based on critical dimension data enhances application and tool-to-tool matching among systems for measurement of critical dimensions, film thickness, film composition, and overlay.
Contact information
Any inquiry concerning this communication or earlier communications from the
examiner should be directed to MOHAMED CHARIOUI whose telephone number is (571)272-2213. The examiner can normally be reached Monday through Friday, from 9 am to 6 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached on (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Mohamed Charioui
/MOHAMED CHARIOUI/Primary Examiner, Art Unit 2857
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