DETAILED ACTION
This Final Office Action is responsive to the claims filed on February 10, 2022. Claims 1-20 are under examination. Claims 1 and 15 are the independent claims.
Claim 15 is objected to.
Claims 1-20 are rejected under 35 USC 101 as ineligible.
Claims 1-10, 12-16, and 18-20 are rejected under 35 U.S.C. 103 over David and Ha.
Claims 11 and 17 are rejected under 35 USC 103 over David, Ha, and Wu.
Response to Arguments/Amendments
35 USC 112(b) rejections: The Applicant’s arguments and amendments have been considered and are persuasive. The 35 USC 112(b) rejections have been withdrawn.
35 USC 101 rejections: The Applicant’s arguments and amendments have been considered, but they are not persuasive. The Applicant’s arguments will be addressed in the order presented in the Applicant’s response.
The claims as allegedly applied ala Diehr: The Applicant has amended the independent claims to recite
“cause physical configuration of the manufacturing process of manufacturing a pattern on a substrate based on the model and/or providing a signal representing, or based on, the model to a system for use in configuration of the manufacturing process.”
In one sense, a scope of this could be “manufacturing the pattern on a substrate according to the design of the model.” This is a mere “apply it” step similar to the example in MPEP 2106.05(f), “vi. A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair, In re Brown, 645 Fed. App'x 1014, 1017 (Fed. Cir. 2016).” Because this is an “apply it” step, under MPEP 2106.05(f), it cannot confer eligibility at Step 2A, Prong 2 or at Step 2B.
Another interpretation of the claim feature that is within the scope of the claim feature is indicating that the modeling process is over and the manufacturing process can begin. Providing a notice that an abstract idea has been completed after its completion is insignificant extra-solution activity and well-understood, routine, and conventional (WURC) activity. Still another interpretation is that the model data is transferred to a manufacturing computer for manufacturing after the abstract idea completes the model, which is insignificant extra-solution activity and well-understood, routine, and conventional (WURC) activity. (See MPEP 2106.05(d): “i. Receiving or transmitting data over a network” “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price” and MPEP 2106.05(g): “An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent.” “v. Consulting and updating an activity log” “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display.”) Because these other valid claim interpretations are insignificant extra-solution activity and WURC, the claim feature cannot confer eligibility at Step 2A, Prong 2 and Step 2B.
In Diehr, the abstract idea was woven into the claim, integrating the abstract idea into a practical application. The claims at issue tack on a dangling apply it step at the end after the abstract idea has already accomplished the purported inventive features of the claim. Further, in the claim at issue in Diehr, the manufacturing steps are explicitly conducted by automated elements. The independent claims of the instant application do not require automation for any manufacturing step. The claims at issue are not analogous to those in Diehr.
35 USC 103: The Applicant’s arguments and amendments have been considered, but they are not persuasive. The Applicant’s arguments will be addressed in the order presented in the Applicant’s response.
Definition of De-Corrected Overlay Data: The Applicant asserts on Pages 11-12 of the response:
“The Office Action relies on paragraphs [0034], [0039], [0043]-[0045], [0066], [0113] and [0159] and claim 17 of David. However, the cited portions of David do not appear to disclose or teach, for example, de-corrected measured overlay data associated with the current layer of the current substrate. There is no disclosure or teaching of "de-corrected" or de-correction or anything comparable in the cited portions of David. The Office Action in particular relies on paragraph [0045] of David. However, not only does paragraph [0045] of David not say anything about a correction (there is no use of the word "correct" or anything comparable therein), paragraph [0045] of David has no mention of overlay data.
The Office Action argues that "de-corrected overlay data... is merely sensed data sensed on the currently [sic] layer of the substrate." Respectfully, that is an unreasonable and overbroad interpretation. Overlay data is not "merely sensed data." It is overlay data. As noted above, paragraph [0045] of David does not reference overlay data at all. Moreover, "de-corrected" necessarily implicates that there has been, or will be, an overlay correction in forming the structures to which the measured de-corrected overlay relates. In other words, a removal ("de-") of a correction. It doesn't say "pre- correction" as seemingly interpreted in the Office Action. For example, de-corrected measured overlay can be determined by measurement of overlay of structures on a substrate wherein one or more overlay corrections that would otherwise be applied in forming those one or more structures are not applied to arrive at de-corrected measured overlay data. Or, for example, the de-corrected measured overlay can be determined by measurement of overlay of structures on a substrate to which one or more overlay corrections are applied in forming those one or more structures and removing (e.g., adding to or subtracting from the measured overlay) those one or more overlay corrections to arrive at de-corrected measured overlay data. The cited portions of David have no disclosure or teaching regarding anything like this.”
The Applicant attempts to assert a definition of “de-corrected overlay data.” A Google search yielded that “de-corrected overlay data” is not a term of art (See Google Search of Record, backdated to the earliest priority date of the filing). Therefore, the Applicant’s asserted definition would have to come from the specification. However, the Applicant’s specification states in paragraph [0082]:
“For example, Figure 4B illustrates the difference between a predicted de-corrected overlay data PDOD (e.g., a map) and a measured de-corrected overlay data MDOD (an example of a ground truth or a reference map). In the present example, the predicted de-corrected overlay data PDOD is associated with a current layer of the substrate being processed. Such predicted data PDOD is obtained, via executing the model (e.g., CNN in Figure 4A) using the inputs DS1 and DS2, before any corrections are applied to the current layer. Similarly, the measured data MDOD is obtained via a metrology tool before any corrections are applied to the current layer. If the predictions of the model (e.g., CNN in Figure 4A) are accurate, then the difference DIFF should very close to zero, and ideally be zero.”
Contrary to the assertions by the Applicant, if there is an expectation that there will be a correction, then the de-corrected overlay data, whether predicted or measured, as defined in the Applicant’s specification, absolutely is pre-correction data (“before any corrections are applied to the current layer”). For this reason, the measured de-corrected overlay data, as claimed and guided by the specification, includes in its scope measured overlay data measured prior to a correction of some kind. David, Paragraph [0045] states:
“In another example, metrology data can be collected during etch processes. Optical emissions spectra or spectral data from photoluminescence can be utilized as input data. Data transformation or feature engineering can be performed on in-situ spectral data or other sensor data that is collected during a particular process Such as etch, deposition, or CMP. As an example, multiple spectra may be collected in-situ during processing. The spectral set used may be all spectra collected during processing, or a Subset of spectra collected during processing. Statistics such as mean, standard deviation, min, and max may be collected at each wavelength interval of the spectral set over time and used as data inputs. As an alternative example, similar statistics can be collected for a given spectrum, and the time series of those statistics can be used as data inputs. As yet another example, peaks and Valleys in the spectrum can be identified and used as data inputs (applying similar statistical transformation). The spectra may need to be normalized or filtered (e.g., lowpass filter) to reduce process or system noise.”
David collects metrology data prior to correcting the layer by data transformation, feature engineering, normalization, or filtering. Therefore, David teaches the claimed de-corrected overlay data. The art rejections are maintained.
Claim Objections
Claim 15 is objected to because of the following informalities:
Claim 15 recites, “the current substrate,” but there is insufficient antecedent basis for this term.
Appropriate correction is required.
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.
Independent Claims
Claim 15 (Statutory Category – Machine)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claims recite a mental process and a mathematical operation, which are abstract ideas.
MPEP 2106.04(a)(2)(Ill): “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions. […] The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
MPEP 2106.04(a)(2)(I): “When determining whether a claim recites a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations), examiners should consider whether the claim recites a mathematical concept or merely limitations that are based on or involve a mathematical concept […] a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea).
MPEP 2106.04(a)(2)(I)(A): “Mathematical Relationships. A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.”
Claim 15 recites (claim features in italics, paragraph references are to the Applicant’s application specification):
determine, based on (i) the first data set, (ii) the second data set, and (iii) the de- corrected measured overlay data, values of a set of model parameters associated with a model for predicting overlay data associated with the current substrate being patterned such that the model predicts overlay data for the current substrate, wherein the values of the set of model parameters are determined such that a cost function is minimized, the cost function comprising a difference between the predicted overlay data and the de-corrected measured overlay data.
The determine step and associated wherein clause is an element of an evaluation, a mental process, which can be performed in the mind of a person or with a pen and paper. Evaluating a machine learning model using regression is just an evaluation of mathematical operations (feed-forward for numerical output, comparison for cost/loss, backpropagation using regression to adjust numerical weights of the model. ([00206] “Procedure P1503 includes providing the performance data1501 of the portions of the patterned substrate layers as input to a base prediction model to obtain predicted performance data 1503 associated with the portions of a first layer of the substrate. In an embodiment, the model is at least one of: a linear model; or a machine learning model. In an embodiment, the machine learning model can be a neural network. For example the machine learning model can be at least one of: multi- layer perceptron; random forest; adaptive boosting trees; support vector regression; Gaussian process regression; k-nearest neighbors; feed forward; recurrent neural network; long/short term memory; gated recurrent; auto encoder; Markov chain; Hopfield network; Boltzmann machine; deep belief network, or other versions of a neural network. In an embodiment, the machine learning model is an advanced machine learning model including at least one of: a residual neural network (RNN); a convolutional neural network (CNN); or a deep CNN. In an embodiment, the RNN model is formulated to include input associated with patterned substrate layers of a current lot of substrates or patterned substrate layers of a prior of substrates as time axis. RNN has the ability to model correlations between features in time and in frequency domain. It's a way to stack the inputs. For example, in the RNN, a set of filters is convolved with the input that results in multiple output-maps, one per filter. This is followed by the application of an element-wise activation function, such as the 6(-) function. These operations are performed on an input data with two axes, such as a spectrogram (time x frequency).”) Further, determine step and associated wherein clause includes and/or is expressed as mathematical calculations or mathematical relationships, which are mathematical concepts. Evaluating a machine learning model using regression is just an evaluation of mathematical operations (feed-forward of numerical input through the model for numerical output, comparison of the output vs expected numerical amount for numerical cost/loss, and backpropagation using regression to adjust numerical weights of the model based on the cost/loss). ([00206] - as previously quoted) The wherein clause merely qualifies the nature of the numerical parameters used in the determination. Being a mental process and a mathematical concept, the determine step and associated wherein are elements of an abstract idea.
Claim 15 recites an abstract idea.
Step 2A – Prong 2: Integrated into a Practical Solution?
No.
MPEP 2106.04(d): “[A]fter determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception 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 judicial exception. Whether or not a claim integrates a judicial exception into a practical application is evaluated using the considerations set forth in subsection I below, in accordance with the procedure described below in subsection II.”
MPEP 2106.05(f) Mere Instructions To Apply An Exception: “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to […] more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners should explain why they do not meaningfully limit the claim in an eligibility rejection. For example, an examiner could explain that implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B.
MPEP 2106.05(g): “Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent.”
MPEP 2106.05(h): “Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. […] Examples of limitations that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception include: […] vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);”
The claim 15 additional limitations (in italics):
A computer program product comprising a non-transitory computer readable medium having instructions therein, the instructions, when executed by a computer system, configured to cause the computer system to at least:
The computer program product is a recitation of a general purpose computer with no specific configurations to execute the claimed method. As such, under MPEP 2106.05(f), the computer implementation implements the recited abstract idea on a generic computer, and does not integrate the abstract idea into a practical application in Step 2A Prong Two.
obtain (i) a first data set associated with one or more prior layers and/or current layer of the current substrate being patterned, (ii) a second data set comprising overlay metrology data associated with one or more prior substrates that were patterned before the current substrate, and (iii) de-corrected measured overlay data associated with the current layer of the current substrate; and
The obtain step merely gathers existing information (first, second, and de-corrected measured overlay data) for evaluation. Mere data gathering is insignificant extra solution activity under MPEP 2106.05(g). Under Mere Data Gathering, an analogous example is provided: “iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011).” Under MPEP 2106.05(g), receiving data for evaluation is not significant in meaningfully limiting the invention, and the receiving of the data is necessary to the evaluations and mathematical operations of the claim. Under MPEP 2106.05(g), the obtain step adds nothing more than insignificant extra solution activity, so it does not integrate the abstract idea into a practical application in Step 2A, Prong Two.
The obtain step and wherein clause also describe different types of data, but these different types of data merely limit the abstract idea to a particular field of use (e.g., lithography). The obtaining step is analogous to the MPEP 2106.05(h) example, “vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment.” Under MPEP 2106.05(h), lining this limitation to a particular field of use is not an additional limitation that can integrate the abstract idea into a practical application under Step 2A, Prong 2.
Cause physical configuration of a manufacturing process of manufacturing a pattern on a substrate based on the model and/or providing a signal representing, or based on, the model to a system for use in configuration of the manufacturing process.
In one sense, a scope of this could be “manufacturing the pattern on a substrate according to the design of the model.” This is a mere “apply it” step similar to the example in MPEP 2106.05(f), “vi. A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair, In re Brown, 645 Fed. App'x 1014, 1017 (Fed. Cir. 2016).” Because this is an “apply it” step, under MPEP 2106.05(f), it cannot confer eligibility at Step 2A, Prong 2 or at Step 2B.
Another interpretation of the claim feature that is within the scope of the claim feature is indicating that the modeling process is over and the manufacturing process can begin. Providing a notice that an abstract idea has been completed after its completion is insignificant extra-solution activity and well-understood, routine, and conventional (WURC) activity. Still another interpretation is that the model data is transferred to a manufacturing computer for manufacturing after the abstract idea completes the model, which is insignificant extra-solution activity and well-understood, routine, and conventional (WURC) activity. (See MPEP 2106.05(d): “i. Receiving or transmitting data over a network” “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price” and MPEP 2106.05(g): “An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent.” “v. Consulting and updating an activity log” “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display.”) Because these other valid claim interpretations are insignificant extra-solution activity and WURC, the claim feature cannot confer eligibility at Step 2A, Prong 2 and Step 2B.
Claim 15 does not include any additional limitations that integrate the abstract idea into a practical application.
Therefore, claim 15 does not integrate the abstract idea into a practical application and is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No.
MPEP 2106.05(I) “An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself. […] Instead, an "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself.”
MPEP 2106.05(f) Mere Instructions To Apply An Exception: “[I]mplementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B.
MPEP 2106.05(d)(II)(i): “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. […] i. Receiving or transmitting data over a network, e.g., using the Internet to gather data […] iv. Storing and retrieving information in memory”
MPEP 2106.05(g): “As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978).”
MPEP 2106.05(h): “Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use. […] Examples of limitations that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception include: […] vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);”
A computer program product comprising a non-transitory computer readable medium having instructions therein, the instructions, when executed by a computer system, configured to cause the computer system to at least:
The computer program product is a recitation of a general purpose computer with no specific configurations to execute the claimed method. As such, under MPEP 2106.05(f), the computer implementation implements the recited abstract idea on a generic computer, and does not combine with the other elements of the claim to provide significantly more that would confer eligibility at Step 2B.
obtain (i) a first data set associated with one or more prior layers and/or current layer of the current substrate being patterned, (ii) a second data set comprising overlay metrology data associated with one or more prior substrates that were patterned before the current substrate, and (iii) de-corrected measured overlay data associated with the current layer of the current substrate; and
This obtain step is receiving or transmitting data also storing and retrieving information from memory, so it is analogous to the examples cited in MPEP 2106.05(d)(II)(i) (“i. Receiving or transmitting data over a network, e.g., using the Internet to gather data […] iv. Storing and retrieving information in memory”) representing well-understood, routine, and conventional functions. Also, as demonstrated with respect to Step 2A, Prong 2, the obtain step is insignificant extra-solution activity and merely links the abstract idea to a particular field of use.
As discussed the additional limitation of the computer program product, is mere execution on a generic computer, and the additional limitation of the receiving step with the wherein clause is insignificant extra-solution activity, a well-understood, routine, and conventional function, and the data recited merely link the use of the abstract idea to a particular field of use. Therefore, none of the additional limitations can provide the abstract idea with significantly more to render the combination of the additional limitations an inventive concept, under MPEP 2106.05(d), MPEP 2106.05(f), MPEP 2106.05(g), and MPEP 2106.05(h).
physically configuring a manufacturing process of manufacturing a pattern on a substrate based on the model and/or providing a signal representing, or based on, the model to a system for use in configuration of the manufacturing process.
In one sense, a scope of this could be “manufacturing the pattern on a substrate according to the design of the model.” This is a mere “apply it” step similar to the example in MPEP 2106.05(f), “vi. A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair, In re Brown, 645 Fed. App'x 1014, 1017 (Fed. Cir. 2016).” Because this is an “apply it” step, under MPEP 2106.05(f), it cannot confer eligibility at Step 2A, Prong 2 or at Step 2B.
Another interpretation of the claim feature that is within the scope of the claim feature is indicating that the modeling process is over and the manufacturing process can begin. Providing a notice that an abstract idea has been completed after its completion is insignificant extra-solution activity and well-understood, routine, and conventional (WURC) activity. Still another interpretation is that the model data is transferred to a manufacturing computer for manufacturing after the abstract idea completes the model, which is insignificant extra-solution activity and well-understood, routine, and conventional (WURC) activity. (See MPEP 2106.05(d): “i. Receiving or transmitting data over a network” “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price” and MPEP 2106.05(g): “An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent.” “v. Consulting and updating an activity log” “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display.”) Because these other valid claim interpretations are insignificant extra-solution activity and WURC, the claim feature cannot confer eligibility at Step 2A, Prong 2 and Step 2B.
Therefore, there are no additional limitations in claim 15 that furnish claim 15 with an inventive concept to ensure that claim 15, as a whole, amounts to significantly more than the bolded abstract idea.
Claim 15 is ineligible.
Claim 1 (Statutory Category – Process)
Claim 1 recites the method performed by the computer program product of claim 15. Therefore, because claim 1’s obtaining and determining steps and wherein clause and the amended physically configuring step are treated in the same way as claim 15’s obtain and determine steps and wherein clause and the amended physically configure step, respectively, under 35 USC 101. Accordingly, Claim 1 is ineligible for at least the same reasons as claim 15.
Claim 1 is ineligible.
Dependent Claims
Claims 2-14 and 16-20 are also ineligible subject matter for at least the following reasons.
Claim 2
Claim 2 recites,
wherein the first data set further comprises:
lithographic apparatus data associated with one or more lithographic apparatuses being used for patterning the one or more prior layers and/or the current layer of the current substrate, and
fabrication context data associated with one or more processing tools that the current substrate was subjected to before the current layer being patterned or will be subjected to after the current layer is patterned.
Claim 2 merely qualifies the parameters used in the model, which is an element of the abstract idea (the model is a mathematical relationship evaluated with mathematical operations- mental process and mathematic concept). Therefore, this merges with the abstract idea. Additionally or alternatively, this also represents an element of the obtaining step, which was demonstrated to be insignificant extra-solution activity and a well-understood, routine, and conventional function. That is, the features of the claim are mere data gathering analogous to the example of insignificant extra-solution activity, “iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011),” under 2106.05(g), and are also analogous to the well-understood, routine, and conventional function examples, “[…] i. Receiving or transmitting data over a network, e.g., using the Internet to gather data […] iv. Storing and retrieving information in memory,” under 2106.05(d). Also, additionally or alternatively, the description of data to be input merely link the use of the abstract idea to a particular field of use analogous to the example, “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015),” under MPEP 2106.05(h). Regardless of whether the features of claim 2 are elements of the abstract idea or are directed to an additional limitation that fails to integrate the abstract idea into a practical application under Step 2A, Prong 2 and fails to combine with the other elements of the claim to provide significantly more that would confer an inventive concept under Step 2B, claim 2 fails to provide any additional limitations that integrate the abstract idea into a practical application under Step 2A, Prong 2 and or combine with the other elements of the claim to provide significantly more that would confer an inventive concept under Step 2B. Therefore, claim 2 is directed to the abstract idea without providing significantly more. MPEP 2106.04; MPEP 2106.05(d); MPEP 2106.05(f); MPEP 2106.05(g); MPEP 2106.05(h)
Claim 2 is ineligible.
Claim 3
Claim 3 recites,
wherein the lithographic apparatus data comprises one or more selected from:
a lithographic apparatus identifier and a lithographic apparatus chuck identifier associated with the one or more lithographic apparatuses;
measurements computed via one or more sensors or a measurement system of the one or more lithographic apparatuses;
one or more key performance indicators associated with the one or more lithographic apparatuses and related to an overlay of the current substrate; and/or
metrology data obtained from one or more alignment sensors, one or more leveling sensors, one or more height sensors, or one or more other sensors attached in the one or more lithographic apparatuses.
Claim 3 merely qualifies the parameters used in the model, which is an element of the abstract idea (the model is a mathematical relationship evaluated with mathematical operations- mental process and mathematic concept). Therefore, this merges with the abstract idea. Additionally or alternatively, this also represents an element of the obtaining step, which was demonstrated to be insignificant extra-solution activity and a well-understood, routine, and conventional function. That is, the features of the claim are mere data gathering analogous to the example of insignificant extra-solution activity, “iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011),” under 2106.05(g), and are also analogous to the well-understood, routine, and conventional function examples, “[…] i. Receiving or transmitting data over a network, e.g., using the Internet to gather data […] iv. Storing and retrieving information in memory,” under 2106.05(d). Also, additionally or alternatively, the description of data to be input merely link the use of the abstract idea to a particular field of use analogous to the example, “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015),” under MPEP 2106.05(h). Regardless of whether the features of claim 3 are elements of the abstract idea or are directed to an additional limitation that fails to integrate the abstract idea into a practical application under Step 2A, Prong 2 and fails to combine with the other elements of the claim to provide significantly more that would confer an inventive concept, claim 3 fails to provide any additional limitations that integrate the abstract idea into a practical application under Step 2A, Prong 2 and or combine with the other elements of the claim to provide significantly more that would confer an inventive concept under Step 2B. Therefore, claim 3 is directed to the abstract idea without providing significantly more. MPEP 2106.04; MPEP 2106.05(d); MPEP 2106.05(f); MPEP 2106.05(g); MPEP 2106.05(h)
Claim 3 is ineligible
Claim 4
Claim 4 recites,
wherein the one or more processing tools comprise one or more selected from:
an etch chamber, a chemical mechanical polishing tool, an overlay measurement tool, and/or a critical dimension (CD) metrology tool.
NOTE: Claim 4 recites sources of information that are unrelated to any active element of the claim. For example, the claim includes a situation where all of the data collected is first stored and the method begins after all of the data is acquired and stored. Even if one were to positively recite the measurement of the data with the claimed machines, it would still be insignificant extra-solution activity, a well-understood, routine, and conventional function, and would be merely linking the abstract idea to a particular field of use. Therefore, even if claim 4 were to positively recite measuring using these machines, it would be insufficient to integrate the abstract idea into a practical application or to combine with the other elements of the claim to provide significantly more that would confer an inventive concept.
Claim 4 merely qualifies the parameters used in the model, which is an element of the abstract idea (the model is a mathematical relationship evaluated with mathematical operations- mental process and mathematic concept). Therefore, this merges with the abstract idea. Additionally or alternatively, this also represents an element of the obtaining step, which was demonstrated to be insignificant extra-solution activity and a well-understood, routine, and conventional function. That is, the features of the claim are mere data gathering analogous to the example of insignificant extra-solution activity, “iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011),” under 2106.05(g), and are also analogous to the well-understood, routine, and conventional function examples, “[…] i. Receiving or transmitting data over a network, e.g., using the Internet to gather data […] iv. Storing and retrieving information in memory,” under 2106.05(d). Also, additionally or alternatively, the description of data to be input merely link the use of the abstract idea to a particular field of use analogous to the example, “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015),” under MPEP 2106.05(h). Regardless of whether the features of claim 4 are elements of the abstract idea or are directed to an additional limitation that fails to integrate the abstract idea into a practical application under Step 2A, Prong 2 and fails to combine with the other elements of the claim to provide significantly more that would confer an inventive concept Step 2B, claim 4 fails to provide any additional limitations that integrate the abstract idea into a practical application under Step 2A, Prong 2 and or combine with the other elements of the claim to provide significantly more that would confer an inventive concept under Step 2B. Therefore, claim 4 is directed to the abstract idea without providing significantly more. MPEP 2106.04; MPEP 2106.05(d); MPEP 2106.05(f); MPEP 2106.05(g); MPEP 2106.05(h)
Claim 4 is ineligible
Claim 5
Claim 5 recites,
wherein the first data set comprises:
overlay metrology data of the one or more prior layers and/or the current layer of a current substrate, the overlay metrology data comprising: (i) measured overlay data obtained after an overlay correction is applied to the one or more prior layers of the current substrate, and/or (ii) de-corrected overlay data obtained before the overlay correction is applied to the one or more prior layers of the current substrate;
alignment metrology data of the one or more prior layers and/or the current layer of the current substrate, the alignment metrology data comprising: (i) alignment sensor data, (ii) a residual map generated via an alignment system model, (iii) a substrate quality map comprising signals of varying strength, the substrate quality map indicative of reliability of the alignment data, and/or (iv) a color2color difference map obtained via projection of a plurality of colored-laser beams on the substrate, each colored-laser beam reflecting from an alignment mark on the one or more prior layers, the respective reflected beam generating a diffraction pattern, the color2color difference map being a difference between a first diffraction pattern and a second diffraction pattern, the first diffraction pattern being associated with a first color of the plurality of colored-laser beams and the second diffraction pattern being associated with a second color of the plurality of colored-laser beams;
leveling metrology data of the one or more prior layers and/or the current layer of the current substrate, the leveling metrology data comprising: (i) a substrate height data, and/or (ii) the substrate height data converted to x and y direction displacements; and/or
fabrication context information of the one or more prior layers and/or the current layer of the current substrate, the context information comprising: (i) a lag time associated with a process of a patterning process, (ii) a chuck identifier on which a current substrate was mounted, (iii) a chamber identifier indicating a chamber in which a process of the patterning process was performed, and/or (iv) a chamber fingerprint characterizing an overlay contribution of one or more processing parameters associated with the chamber.
Claim 5 merely qualifies the parameters used in the model, which is an element of the abstract idea (the model is a mathematical relationship evaluated with mathematical operations- mental process and mathematic concept). Therefore, this merges with the abstract idea. Additionally or alternatively, this also represents an element of the obtaining step, which was demonstrated to be insignificant extra-solution activity and a well-understood, routine, and conventional function. That is, the features of the claim are mere data gathering analogous to the example of insignificant extra-solution activity, “iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011),” under 2106.05(g), and are also analogous to the well-understood, routine, and conventional function examples, “[…] i. Receiving or transmitting data over a network, e.g., using the Internet to gather data […] iv. Storing and retrieving information in memory,” under 2106.05(d). Also, additionally or alternatively, the description of data to be input merely link the use of the abstract idea to a particular field of use analogous to the example, “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015),” under MPEP 2106.05(h). Regardless of whether the features of claim 5 are elements of the abstract idea or are directed to an additional limitation that fails to integrate the abstract idea into a practical application under Step 2A, Prong 2 and fails to combine with the other elements of the claim to provide significantly more that would confer an inventive concept, claim 5 fails to provide any additional limitations that integrate the abstract idea into a practical application under Step 2A, Prong 2 and or combine with the other elements of the claim to provide significantly more that would confer an inventive concept under Step 2B. Therefore, claim 5 is directed to the abstract idea without providing significantly more. MPEP 2106.04; MPEP 2106.05(d); MPEP 2106.05(f); MPEP 2106.05(g); MPEP 2106.05(h)
Claim 5 is ineligible
Claim 6
Claim 6 recites,
wherein the first data set further comprises derived data associated with one or more parameters of a patterning process associated with a contribution to overlay, wherein the derived data is derived from the lithographic apparatus data and/or fabrication context information.
Claim 6 merely qualifies the parameters used in the model, which is an element of the abstract idea (the model is a mathematical relationship evaluated with mathematical operations- mental process and mathematic concept). Therefore, this merges with the abstract idea. Additionally or alternatively, this also represents an element of the obtaining step, which was demonstrated to be insignificant extra-solution activity and a well-understood, routine, and conventional function. That is, the features of the claim are mere data gathering analogous to the example of insignificant extra-solution activity, “iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011),” under 2106.05(g), and are also analogous to the well-understood, routine, and conventional function examples, “[…] i. Receiving or transmitting data over a network, e.g., using the Internet to gather data […] iv. Storing and retrieving information in memory,” under 2106.05(d). Also, additionally or alternatively, the description of data to be input merely link the use of the abstract idea to a particular field of use analogous to the example, “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015),” under MPEP 2106.05(h). Regardless of whether the features of claim 6 are elements of the abstract idea or are directed to an additional limitation that fails to integrate the abstract idea into a practical application under Step 2A, Prong 2 and fails to combine with the other elements of the claim to provide significantly more that would confer an inventive concept, claim 6 fails to provide any additional limitations that integrate the abstract idea into a practical application under Step 2A, Prong 2 and or combine with the other elements of the claim to provide significantly more that would confer an inventive concept under Step 2B. Therefore, claim 6 is directed to the abstract idea without providing significantly more. MPEP 2106.04; MPEP 2106.05(d); MPEP 2106.05(f); MPEP 2106.05(g); MPEP 2106.05(h)
Claim 6 is ineligible
Claim 7
Claim 7 recites,
wherein the model is configured to predict overlay data at a point-level of the current substrate, where a point is a location associated with an overlay mark formed on the current substrate.
NOTE: The claim does not positively recite the step of displaying or emitting light to gorm an overlay mark.
Claim 7 merely qualifies the parameters used in the model, which is an element of the abstract idea (the model is a mathematical relationship evaluated with mathematical operations- mental process and mathematic concept). Therefore, this merges with the abstract idea. Also, additionally or alternatively, the description of data to be output merely link the use of the abstract idea to a particular field of use analogous to the example, “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015),” under MPEP 2106.05(h). Regardless of whether the features of claim 7 are elements of the abstract idea or are directed to an additional limitation that fails to integrate the abstract idea into a practical application under Step 2A, Prong 2 and fails to combine with the other elements of the claim to provide significantly more that would confer an inventive concept, claim 7 fails to provide any additional limitations that integrate the abstract idea into a practical application under Step 2A, Prong 2 and or combine with the other elements of the claim to provide significantly more that would confer an inventive concept under Step 2B. Therefore, claim 7 is directed to the abstract idea without providing significantly more. MPEP 2106.04; MPEP 2106.05(d); MPEP 2106.05(f); MPEP 2106.05(h)
Claim 7 is ineligible
Claim 8
Claim 8 recites,
wherein the model is a point-level model, wherein the values of the set of model parameters of the point-level model are determined based on the first data set, the second data set, and the de-corrected measured overlay data that are obtained at a given location of a plurality of locations on the current substrate having an overlay mark.
Claim 8 merely qualifies the parameters used in the model, which is an element of the abstract idea (the model is a mathematical relationship evaluated with mathematical operations- mental process and mathematic concept). Therefore, this merges with the abstract idea. Also, additionally or alternatively, the description of data to be output merely link the use of the abstract idea to a particular field of use analogous to the example, “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015),” under MPEP 2106.05(h). Regardless of whether the features of claim 8 are elements of the abstract idea or include an additional limitation that fails to integrate the abstract idea into a practical application under Step 2A, Prong 2 and fails to combine with the other elements of the claim to provide significantly more that would confer an inventive concept, claim 8 fails to provide any additional limitations that integrate the abstract idea into a practical application under Step 2A, Prong 2 and or combine with the other elements of the claim to provide significantly more that would confer an inventive concept under Step 2B. Therefore, claim 8 is directed to the abstract idea without providing significantly more. MPEP 2106.04; MPEP 2106.05(d); MPEP 2106.05(f); MPEP 2106.05(h)
Claim 8 is ineligible
Claim 9
MPEP 2106.05(d): “Below are examples of other types of activity that the courts have found to be well-understood, routine, conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: […] vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015).”
Claim 9 recites,
wherein obtaining the first data set, the second data set, and the de-corrected measured overlay data at the given location on the current substrate having the overlay mark comprises:
representing values of the first data set, the second data set, and the de-corrected measured overlay data in the form of a respective substrate map;
aligning, via modeling and/or interpolation, each of the substrate maps;
sharing substrate-level information, within the first data set, the second data set, and the de-corrected measured overlay data, respectively, uniformly across the current substrate; and
extracting the values of the first data set, the second data set, and the de-corrected measured overlay data, respectively, associated with the given location.
This is merely reforming data for ingestion and is an element that qualifies the obtaining step, which has been demonstrated to a be well-understood, routine, and conventional function as well as an insignificant extra-solution activity. First, the obtaining step, which the features of claim 9 qualify, has been demonstrated to be insignificant extra-solution activity as well as a well-understood, routine, and conventional function. Also, based on the example from MPEP 2106.05(g), (“i. Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989);”), the features of claim 9 are insignificant extra-solution activity. Further, based on the example from MPEP 2106.05(d) quoted above, (“vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015).”), this data modification for the purposes of formatting data to be used as a parameter in a model is a well-understood, routine and conventional function. Further still, based on the example from 2106.05(h) quoted above (“vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment”), the type of data recited also merely links the abstract idea to a particular field of use. For these reasons, claim 9 fails to provide any additional limitations that integrate the abstract idea into a practical application under Step 2A, Prong 2 and or combine with the other elements of the claim to provide significantly more that would confer an inventive concept under Step 2B. Therefore, claim 9 is directed to the abstract idea without providing significantly more. MPEP 2106.04; MPEP 2106.05(d); MPEP MPEP 2106.05(h)
Claim 9 is ineligible.
NOTE: Claims 16-20 are “computer program product” claims configured to execute instructions to perform the operations of claims 10-14, respectively. The additional limitations of computer programs and execution of instructions thereby are mere execution on a general-purpose computer. As discussed with respect to the recitation of the “computer program product” and its configuration to have its instructions executed are tantamount to “apply it” on a general purpose computer, so these are not additional limitations that confer eligibility. That is, the “computer program product” and configuration to execute instructions limitations are not additional limitations that integrate the abstract idea into a practical application under Step 2A, Prong 2 or additional limitations that combine with the other elements of the claim to provide significantly more that would confer an inventive concept under Step 2B. Accordingly, claims 16-20 will be treated the same as claims 10-14, respectively, under this eligibility analysis/
Claims 10 and 16
Claim 10 (and claim 16) recites,
wherein the model is a substrate- level model, and wherein the values of the set of model parameters of the substrate-level model are determined based on the values of the first data set, the second data set, and the de-corrected measured overlay data across an entire substrate.
This merely qualifies the mathematical relationships (the model structure) and mathematical operations performed thereon in the evaluation of the determining step of claim 1 (or the determine step of claim 15), which is an element of the mathematical evaluation (mental process, mathematical concept). Therefore, the limitations of claim 10 are elements of the abstract idea, and claim 10 fails to provide additional limitations that integrate the abstract idea into a practical application under Step 2A, Prong 2 or additional limitations that combine with the other elements of the claim to provide significantly more that would confer an inventive concept under Step 2B. Claim 16 recites features analogous to the features of claim 10 and receives the same treatment under the eligibility analysis.
Claims 10 and 16 are ineligible
Claims 11 and 17
Claim 11 (and 17) recites,
wherein the determining of the values of the set of model parameters of the substrate-level model further comprises: generating a plurality of substrate maps using values of the first data set, the second data set, and the de-corrected measured overlay data, respectively, associated with each of a plurality of substrates; (evaluation, mathematical calculation using mathematical relationships; [0095] “In another example, where a training data set is presented at the substrate-level (e.g., an entire substrate, as opposed to a single point on the substrate), the trained model may be referred as a substrate-level model (not illustrated). In an embodiment, a given substrate may be associated with a plurality of substrate maps such as an alignment map, a leveling map, and/or measured overlay map, for example. In a substrate-level model, each substrate becomes a data sample source, in which each associated map (e.g., alignment map, leveling map, overlay map, etc.) is projected on to a set of basis functions to obtain its coefficients as a numerical representation for the projected map. In an embodiment, the projection map can be used either as input or output for the substrate-level model. In an embodiment, the basis functions can be principal component analysis basis function, Zernike polynomial, or other more complicated overlay model including basis functions that contain both inter-field and intra-field function components. In an embodiment, the substrate-level information (e.g., chuck id, RF time, etc.) may also be encoded and then used as additional inputs for determining the substrate level model. Again, any cost function discussed above may be used to determine the values of the model parameters associated with the substrate-level model.)
projecting each of the plurality of substrate maps to a basis function; and determining, based on the projecting, projection coefficients associated with the basis function, the projection coefficients and other substrate-level data being used to define the substrate model. ; (evaluation, mathematical calculation using mathematical relationships; [0095]-[0096] “In an embodiment, the projection map can be used either as input or output for the substrate-level model. In an embodiment, the basis functions can be principal component analysis basis function, Zernike polynomial, or other more complicated overlay model including basis functions that contain both inter-field and intra-field function components. In an embodiment, the substrate-level information (e.g., chuck id, RF time, etc.) may also be encoded and then used as additional inputs for determining the substrate level model. Again, any cost function discussed above may be used to determine the values of the model parameters associated with the substrate-level model. or example, the cost function can be the on product overlay (OPO). To determine OPO, first, a set of projection coefficients are be determined by applying the substrate-level model using input data (in appropriate formats) associated with the current substrate of interest. Then, an overlay map may be re-constructed based on the predicted coefficients. Then, the cost function may be calculated, for example, based on the difference between the re-constructed overlay map and ground truth map. Further, a standard gradient based method may be used to determine optimized values of the cFP model parameters that result in best predicted results (e.g., very close to or equal to ground truth map).
The generating, projecting, and determining steps qualify the determining step of claim 1 (or determine step of claim 15), which was demonstrated to be an evaluation of mathematical calculations using mathematical relationships (e.g., the model). Also, as demonstrated above, the generating, projecting, and determining steps, when considered individually, are all evaluations of mathematical calculations on sets of mathematical relationships (e.g., the model), making them elements of a mental process and a mathematical concept, an abstract idea. Because the limitations of claim 11 are all elements of the abstract idea, claim 11 fails to recite additional limitations that integrate the abstract idea into a practical application under Step 2A, Prong 2 or additional limitations that combine with the other elements of the claim to provide significantly more that would confer an inventive concept under Step 2B. Claim 17 recites features analogous to the features of claim 11 and receives the same treatment under the eligibility analysis.
Claims 11 and 17 are ineligible
Claims 12 and 18
Claim 12 (and claim 18) recite,
wherein the model is at least one selected from:
a linear model that is determined based on (i) the first data set associated with a selected layer of the current substrate or the prior substrates, or (ii) the first data set associated with multiple layers of the current substrate or the prior substrates; or
a machine learning model.
The model recited in claim 1 (and claim 15) is a set of mathematical relationships, For example, a machine learning model is a set of mathematical nodes and edges with associated operations, weights, and biases that are expressed mathematically. This makes them elements of the abstract idea recited in the determining step of claim 1 (or determine step of claim 15). Because the limitations of claim 12 are elements of the abstract idea, claim 12 fails to recite additional limitations that integrate the abstract idea into a practical application under Step 2A, Prong 2 or additional limitations that combine with the other elements of the claim to provide significantly more that would confer an inventive concept under Step 2B. Claim 18 recites features analogous to the features of claim 12 and receives the same treatment under the eligibility analysis.
Claims 12 and 18 are ineligible
Claims 13 and 19
Claim 13 (and 19) recites,
wherein the model is a machine learning model and wherein the machine learning model is at least one selected from: multi-layer perceptron, random forest, adaptive boosting trees, support vector regression, Gaussian process regression, or k-nearest neighbors.
The model recited in claim 1 (and claim 15) is a set of mathematical relationships. For example, a machine learning model is a set of mathematical nodes and edges with associated operations, weights, and biases that are expressed and evaluated mathematically/numerically. This makes them elements of the abstract idea recited in the determining step of claim 1 (or determine step of claim 15). Because the limitations of claim 13 are elements of the abstract idea, claim 13 fails to recite additional limitations that integrate the abstract idea into a practical application under Step 2A, Prong 2 or additional limitations that combine with the other elements of the claim to provide significantly more that would confer an inventive concept under Step 2B. Claim 19 recites features analogous to the features of claim 13 and receives the same treatment under the eligibility analysis.
Claims 13 and 19 are ineligible
Claims 14 and 20
Claims 14 and 20 recite,
wherein the model is a machine learning model and wherein the machine learning model is an advanced machine learning model including at least one selected from: a residual neural network (RNN) or a convolutional neural network (CNN).
The model recited in claim 1 (and claim 15) is a set of mathematical relationships. For example, a machine learning model is a set of mathematical nodes and edges with associated operations, weights, and biases that are expressed and evaluated mathematically/numerically. This makes them elements of the abstract idea recited in the determining step of claim 1 (or determine step of claim 15). Because the limitations of claim 14 are elements of the abstract idea, claim 14 fails to recite additional limitations that integrate the abstract idea into a practical application under Step 2A, Prong 2 or additional limitations that combine with the other elements of the claim to provide significantly more that would confer an inventive concept under Step 2B. Claim 20 recites features analogous to the features of claim 14 and receives the same treatment under the eligibility analysis.
Claims 14 and 20 are ineligible
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 factual inquiries for establishing a background for determining obviousness under 35 USC 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 under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1-10, 12-16, and 18-20: David and Ha
Claims 1-10, 12-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2016/0148850 A1 to David (David) in view of US 2018/0330511 A1 to Ha et al. (Ha).
NOTE ON INTERPRETATION: Claims that recite the limitation “and/or” will be interpreted as an “or” limitation under the broadest reasonable interpretation.
Claims 1 and 15
Regarding Claim 15, David Teaches:
A computer program product comprising a non-transitory computer readable medium having instructions therein, the instructions, when executed by a computer system, configured to cause the computer system to at least: (David Claim 17 “A non-transitory machine-readable medium having stored thereon one or more sequences of instructions, which instructions, when executed by one or more processors, cause the one or more processors to carry out the steps of:” – CRM)
obtain
(i) a first data set associated with one or more prior layers and/or current layer of the current substrate being patterned, (David [0034] “This opens up the possibility of incorporating data analysis to make corrections on the lithographic apparatus for overlay error and critical dimension (CD) variation. For example, in addition to using the usual parameters to correct for overlay error (e.g., CD metrology, on-scanner data, wafer shape and geometry metrology, DBO measurement), process parameters and other metrology from upstream processes and metrology can also be used to train a machine learning algorithm.” – Upstream processes include a prior layer of the substrate. On-scanner data is sensor data on the current layer of the substrate.; [0039] “By drilling deeper into the details of semiconductor manufacturing and knowing how to apply predictive analytics to detect and resolve yield issues faster, and to tighten and target the specifications of individual manufacturing steps, increased yield can result. FIG. 2 shows an example of the cumulative effects of process variation on product performance. The relationships can be complex and difficult to correlate, e.g., key performance indicators (KPIs) of the process steps, such as the critical dimensions of lithographic and etch steps 202, the dielectric film thickness 204, and film resistivity 206; parametrics, such as channel length and width 212, transistor and diode thresholds 214, and resistance 216; and product performance, Such as maximum frequency 222, and maximum current 224. We can use predictive analytics to quantify those relationships, and then leverage the relationships to predict and improve product performance.” – These are other data that can be used.)
(ii) a second data set comprising overlay metrology data associated with one or more prior substrates that were patterned before the current substrate, and (David [0044] “In yet another example, the metrology measurements taken in-situ, or after a particular semiconductor process is complete, can be used as part of the input data for the virtual metrology system. For example, metrology data can be collected after a CMP step that occurred in one or more processing steps preceding the current lithography step. These metrology measurements can also be thickness data determined by each metrology system, or the refractive index and absorption coefficient.” - Metrology data can be used as input for training a model.)
(iii) de-corrected measured overlay data associated with the current layer of the current substrate; and (NOTE: De-corrected overlay data is being interpreted as overlay data before any correction is applied. This is merely sensed data sensed on the currently layer of the substrate; David [0045] “In another example, metrology data can be collected during etch processes. Optical emissions spectra or spectral data from photoluminescence can be utilized as input data. Data transformation or feature engineering can be performed on in-situ spectral data or other sensor data that is collected during a particular process Such as etch, deposition, or CMP. As an example, multiple spectra may be collected in-situ during processing. The spectral set used may be all spectra collected during processing, or a Subset of spectra collected during processing. […] As yet another example, peaks and Valleys in the spectrum can be identified and used as data inputs (applying similar statistical transformation). The spectra may need to be normalized or filtered (e.g., lowpass filter) to reduce process or system noise. Examples of in-situ spectral data include reflectometry from the wafer, optical emissions spectra (OES), or photoluminescence.” – Data on the current layer is sensed and included as input to the machine learning model.)
determine, based on (i) the first data set, (ii) the second data set, and (iii) the de- corrected measured overlay data, values of a set of model parameters associated with a model for predicting overlay data associated with the current substrate being patterned such that the model predicts overlay data for the current substrate, (NOTE: This is interpreted as a training step to train the model based on the data obtained in the obtain step.; David [0043] “The algorithm can be a supervised learning algorithm, where a model can be trained using a set of input data and measured targets. The targets can be the critical dimensions that are to be controlled. The input data can be upstream metrology measurements, or data from process equipment (such as temperatures and run times.” – Using the data the machine learning model can be trained to predict overlay data for a substrate. The determine step is training. The “predict[ion of] overlay data” is the function of the model once the model is trained.)
wherein the values of the set of model parameters are determined (David [0066] “For example, a set of measured and observed values can be associated with an overlay offset. Those values would be an input vector to the model, and would be associated with the target, e.g., the measured offset.” - The measured offset is the difference between an expected target position and a predicted position of the overlay.; [0159] “The algorithm can be a supervised learning algorithm, where a model can be trained using a set of input data and measured targets. The targets can be the critical dimensions that are to be controlled. The input data can be upstream metrology measurements, or data from process equipment (such as temperatures and run times).” – The critical dimensions to be controlled can be target variables for which the labeled data for supervised learning is provided.; [0039] “key performance indicators (KPIs) of the process steps, such as the critical dimensions of lithographic and etch steps 202, the dielectric film thickness 204, and film resistivity 206; parametrics, such as channel length and width 212, transistor and diode thresholds 214, and resistance 216; and product performance, such as maximum frequency 222, and maximum current 224. We can use predictive analytics to quantify those relationships, and then leverage the relationships to predict and improve product performance.” – The critical dimensions can include overlay specs.; This teaches that output of the machine learning model can be a predicted overlay feature to be compared, and that training the machine learning model with supervised learning would require labeled/ground truth data of the overlay feature. That is the nature of supervised learning.)
cause physical configuration of a manufacturing process of manufacturing a pattern on a substrate based on the model and/or providing a signal representing, or based on, the model to a system for use in configuration of the manufacturing process. (David [0087] “For each input vector of size 1 xn fed into the algorithmic model, a score will be generated in step 806. The score is a prediction of the target made by the model, given the input data. The score generated by the model will correspond to whatever metric was used as a target for training the algo rithm that generated the model. For example, if a DBO measurement was used for the target to train the algorithm, then the score will be a predicted DBO measurement. If the target was a parametric test value, then the score will be a prediction of that parametric test value. In a typical situation, the score can be the overlay offset prediction, for example, an offset in the X direction or they direction. In step 808, the score is used to determine an adjustment to be made to one or more components of the lithographic apparatus. For example, the offset data could be applied to a control system to make an adjustment to the lithography apparatus parameters or “control knobs' to adjust for the overlay error.” – David causes the physical configuration of a manufacturing process of manufacturing a pattern on a substrate based on the model.)
David suggests (David [0043] “The algorithm can be a supervised learning algorithm, where a model can be trained using a set of input data and measured targets.” – Supervised learning requires that data output of by the machine learning during training be compared with labeled data to determine a loss/error. The loss is then backpropagated through the weights of the model to adjust the model to better predict the data in the future. One common way to determine/augment loss is to use a cost function. Cost functions are common elements of machine learning model.) but does not appear to explicitly teach, but Ha teaches:
wherein the values of the set of model parameters are determined such that a cost function is minimized, the cost function comprising a difference between the predicted overlay data and the de-corrected measured overlay data. (Ha [0004] “Fabricating semiconductor devices such as logic and memory devices typically includes processing a substrate such as a semiconductor wafer using a large number of semiconductor fabrication processes to form various features and multiple levels of the semiconductor devices. For example, lithography is a semiconductor fabrication process that involves transferring a pattern from a reticle to a resist arranged on a semiconductor wafer. Additional examples of semiconductor fabrication processes include, but are not limited to, chemical-mechanical polishing (CMP), etch, deposition, and ion implantation. Multiple semiconductor devices may be fabricated in an arrangement on a single semiconductor wafer and then separated into individual semiconductor devices.” – Lithography processes render a pattern.; [0015] “In addition to imaging simulation models, the physics-based approach may require some additional simulation models which may not be available. For example, in SEM-to-CAD registration use cases, the physics-based approaches will need the simulation of the lithographical process from post-OPC-CAD to actual patterns on wafers to simulate lithographical OPC errors which are substantially significant in high resolution SEM images. In most of the cases, these simulation models are not publicly available due to confidentiality.” – Non-machine learning approaches often require data not available; [0123] “The training process will determine what are the best features to describe the images from each image modality (e.g., by minimizing the cost functions).”; - The supervised learning uses cost functions to augment losses for backpropragation. [0112] “To avoid overfitting and reduce redundancy in the extracted features, sparsity in the feature maps may be enforced by using a drop out layer at the end of the encoder and also including a L1 regularization on the codes in the L2 cost function. Again, these specific learning based model configurations are not meant to be limiting to the learning based models that are appropriate for use in the embodiments described herein. The learning based model may vary in type and parameter values from those described above and still be used in the embodiments described herein.” – The use of L2 loss reduces overfit. While the reference has focused on overlay images, other parameters can be used as target variables; Ha uses a cost function to penalize the loss between the predicted/target value and the value output in order to avoid overfitting to the data.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the supervised learning of David by the cost function of Ha because a person of ordinary skill in the art, when looking at David’s explicit aim to reduce overfitting using k-fold cross-validation (another standard supervised learning technique), would be motivated to look to Ha to further reduce overfitting using the loss function of Ha. (David [0113] “The training dataset can be partitioned into training, testing, and validation portions to ensure a robust model is built that is not over-fit or over-biased. […] It is important to note that techniques such as k-fold cross validation can be employed during the model building phase to ensure the model is not over-fit to any given training set. This involves rotating the training/testing/validation portions of the dataset to ensure that all data sees a training or testing portion.”; Ha [0112] “To avoid overfitting and reduce redundancy in the extracted features, sparsity in the feature maps may be enforced by using a drop out layer at the end of the encoder and also including a L1 regularization on the codes in the L2 cost function.”)
Regarding Claim 1, claim 1 recites the steps and wherein clause of claim 15. Accordingly, claim 1 recites features substantially similar to features of claim 15 and is rejected for at least the same reasons as claim 15.
Claim 2
Regarding claim 2, David and Ha teach the method of claim 1. David further teaches:
wherein the first data set further comprises: lithographic apparatus data associated with one or more lithographic apparatuses being used for patterning the one or more prior layers and/or the current layer of the current substrate, and (David [0068] “Some of the parameters that are already regularly used in overlay error compensation and lithography apparatus control will be used as part of this input dataset. For example, these regularly used parameters can include DBO measurements from the metrology equipment, wafer shape and geometry measurements, or parameters from the lithography apparatus.” – Lithographic apparatus data (parameters from the lithography apparatus) can be used as inputs.)
fabrication context data associated with one or more processing tools that the current substrate was subjected to before the current layer being patterned or will be subjected to after the current layer is patterned. (David [0069] “Most importantly, other parameters from upstream semiconductor processes and metrology can be used as inputs to the algorithm as well. These input parameters can include other metrology measurements from earlier process steps, including optical reflectometry or ellipsometry (normal incident, polarized or unpolarized light, oblique angles of incidence, and varying azimuth angles).” – Data from upstream processes (e.g., prior layers) can be used as inputs a well.)
Claim 3
Regarding claim 3, David and Ha teach the method of claim 2. David further teaches:
wherein the lithographic apparatus data comprises one or more selected from:
a lithographic apparatus identifier and a lithographic apparatus chuck identifier associated with the one or more lithographic apparatuses; measurements computed via one or more sensors or a measurement system of the one or more lithographic apparatuses; one or more key performance indicators associated with the one or more lithographic apparatuses and related to an overlay of the current substrate; and/or metrology data obtained from one or more alignment sensors, one or more leveling sensors, one or more height sensors, or one or more other sensors attached in the one or more lithographic apparatuses. (David [0068] “Some of the parameters that are already regularly used in overlay error compensation and lithography apparatus control will be used as part of this input dataset. For example, these regularly used parameters can include DBO measurements from the metrology equipment, wafer shape and geometry measurements, or parameters from the lithography apparatus.” – Lithographic apparatus data (parameters from the lithography apparatus) can be used as inputs.; [0069] “Most importantly, other parameters from upstream semiconductor processes and metrology can be used as inputs to the algorithm as well. These input parameters can include other metrology measurements from earlier process steps, including optical reflectometry or ellipsometry (normal incident, polarized or unpolarized light, oblique angles of incidence, and varying azimuth angles).” – Metrology and Data from upstream processes (e.g., prior layers) can be used as inputs a well.; [0039] “The relationships can be complex and difficult to correlate, e.g., key performance indicators (KPIs) of the process steps, such as the critical dimensions of lithographic and etch steps 202, the dielectric film thickness 204, and film resistivity 206; parametrics, such as channel length and width 212, transistor and diode thresholds 214, and resistance 216; and product performance, such as maximum frequency 222, and maximum current 224. We can use predictive analytics to quantify those relationships, and then leverage the relationships to predict and improve product performance.” – Key performance indicators can be used as inputs as well.; [0160] “Identifying a machine fault or failure, and finding the root cause of faults quickly, can be essential in semiconductor manufacturing. If faults in the manufacturing process can be better detected and resolved, downtime and scrap can be reduced. This is also referred to as fault detection and classification (FDC). If faults can be predicted before they occur, then downtime can be optimally scheduled and scrap can be even further reduced. Thus, algorithms can be used to predict when a fault or defect will occur in the manufacturing process or on a specific tool at a process step.” – If the model determines which piece of equipment is faulty, it would require some input regarding the identification of that equipment to identify the equipment as faulty.)
Claim 4
Regarding claim 4, David and Ha teach the method of claim 2. David further teaches:
wherein the one or more processing tools comprise one or more selected from:
an etch chamber, a chemical mechanical polishing tool, an overlay measurement tool, and/or a critical dimension (CD) metrology tool. (David [0075] “ Process equipment measurements or metrics can also be used as inputs to the algorithm, such as gas flow sensors, power sensors, pressure sensors, temperature sensors, current sensors, voltage sensors, etc. This data can be collected in process steps that occurred before the lithography step where overlay is to be measured and controlled. Examples of these include process time, RF frequency and power from an etch chamber, electric current and impedance measurements, CMP polish times, motor current from the CMP tool, CVD deposition times and information from mass flow controllers, temperatures, pressures, etc. This data could be from any or all upstream processes from the lithography step being performed.” – This teaches using data about tools, including RF frequency and power from an etch chamber, CMP (polisher) polishing time or motor currents.; [0108] “parameters of other overlay measurements such as DBO and IBO (can also include the intensity values of the diffraction signature along with the DBO measurement itself),” - Data associated with the overlay measurement tool; [0085] “Input data may also include metrology data 730 such a CD, wafer shape, film thickness, film resistivity, inline or in-situ measurements, etc.” – Input data can include CD metrology data associated, which is associated with a machine configured to measure it.)
Claim 5
Regarding claim 5, David and Ha teach the method of claim 1. David further teaches:
wherein the first data set comprises: overlay metrology data of the one or more prior layers and/or the current layer of the current substrate, the overlay metrology data comprising: (i) measured overlay data obtained after an overlay correction is applied to the one or more prior layers of the current substrate, and/or (ii) de-corrected overlay data obtained before the overlay correction is applied to the one or more prior layers of the current substrate; (David [0071] “The metrology measurements can be taken in-situ, or after a particular semiconductor process is complete. For example, metrology data can be collected after a CMP step that occurred in one or more processing steps preceding the current lithography step. These metrology measurements can also be thickness data determined by each metrology system, or the refractive index and absorption coefficient. In another example, metrology data can be collected during etch processes. Optical emissions spectra or spectral data from photoluminescence can be utilized as input data.” – The input data can include metrology data from this layer or a prior layer.)
alignment metrology data of the one or more prior layers and/or the current layer of the current substrate, the alignment metrology data comprising: (i) alignment sensor data, (ii) a residual map generated via an alignment system model, (iii) a substrate quality map comprising signals of varying strength, the substrate quality map indicative of reliability of the alignment data, and/or (iv) a color2color difference map obtained via projection of a plurality of colored-laser beams on the substrate, each colored-laser beam reflecting from an alignment mark on the one or more prior layers, the respective reflected beam generating a diffraction pattern, the color2color difference map being a difference between a first diffraction pattern and a second diffraction pattern, the first diffraction pattern being associated with a first color of the plurality of colored-laser beams and the second diffraction pattern being associated with a second color of the plurality of colored-laser beams; (David [0108] “All available wafer geometry parameters, such as thickness, diameter wafer shape variation, in-plane displacement, stress-induced local curvature, wafer thickness and flatness variation, front and back surface nanotopography (NT), wafer edge roll-off (ERO), sliplines; scanner parameters such as translation (x,y,z), rotation (x,y,z), focus tilt, dose error, focus residual, magnification, asymmetric magnification, asymmetric rotation; CD measurements such as film thickness, trench depth, metal gate recess, high k recess, side wall angle, resist height, hard mask height, pitch walking; film property parameters such as refractive index and absorption coefficient (n & k optical constants); parameters of other overlay measurements such as DBO and IBO (can also include the intensity values of the diffraction signature along with the DBO measurement itself), are used as inputs to the training model, with the corresponding actual overlay error as the target.” – The input data can include alignment data, focus residual, and other pattern variation data.)
leveling metrology data of the one or more prior layers and/or the current layer of the current substrate, the leveling metrology data comprising: (i) a substrate height data, and/or (ii) the substrate height data converted to x and y direction displacements; and/or (David [0143] “Wafer level data from the semiconductor fabrication processes and metrology that are collected before wafer sort and test can be used as part or all of the total inputs to the algorithm. These input parameters can include metrology measurements from process steps or metrology measurements collected during the wafer fabrication process.” – Level data is used as input data.)
fabrication context information of the one or more prior layers and/or the current layer of the current substrate, the context information comprising: (i) a lag time associated with a process of the patterning process, (ii) a chuck identifier on which a current substrate was mounted, (iii) a chamber identifier indicating a chamber in which a process of the patterning process was performed, and/or (iv) a chamber fingerprint characterizing an overlay contribution of one or more processing parameters associated with the chamber. (David [0043] “The input data can be upstream metrology measurements, or data from process equipment (such as temperatures and run times).” – Slow run times (e.g., lag times) associated with the process equipment can be included in the input data.)
Claim 6
Regarding claim 6, David and Ha teach the method of claim 2 (and/or 1). David further teaches:
wherein the first data set further comprises derived data associated with one or more parameters of the patterning process associated with a contribution to overlay, wherein the derived data is derived from the lithographic apparatus data and/or fabrication context information. (David [0052] “The signal generated by the optical marker is measured by a sensor arrangement. Using the output of the sensors, the overlay error can be derived. Typically, the patterns on which overlay error are measured are located within a scribe lane in between target portions.” – Derived data includes overlay error.; [0068] “Some of the parameters that are already regularly used in overlay error compensation and lithography apparatus control will be used as part of this input dataset. For example, these regularly used parameters can include DBO measurements from the metrology equipment, wafer shape and geometry measurements, or parameters from the lithography apparatus.” – Overlay error can be used as input data for the model training (e.g., as labeled data))
Claim 7
Regarding claim 7, David and Ha teach the method of claim 1. David further teaches:
wherein the model is configured to predict overlay data at a point-level of the current substrate, where a point is a location associated with an overlay mark formed on the current substrate. (David [0052] “The position of the mask pattern in the resist layer relative to the position of the pattern on the substrate is determined by measuring an optical response from an optical marker on the substrate which is illuminated by an optical source. The signal generated by the optical marker is measured by a sensor arrangement.” – The point at which the sensing or processing is done is illuminated by a marker.; [0126] “Based on the wafer geometry parameters and the deployed predictive model, the system predicts an overlay error for the remaining production wafers and adjusts the lithography scanner to correct for the predicted overlay error. Point-to-point prediction is crucial for feeding forward the predicted overlay, applying the adjustment, and hence reducing the actual overlay error after the exposure.” – David uses point-level data to reduce the overlay error.)
Claim 8
Regarding claim 8, David and Ha teach the method of claim 1, David further teaches:
Wherein the model is a point-level model, wherein the values of the set of model parameters of the point-level model are determined based on the first data set, the second data set, and the de-corrected measured overlay data that are obtained at a given location of a plurality of locations on the current substrate having an overlay mark. (David [0052] “The position of the mask pattern in the resist layer relative to the position of the pattern on the substrate is determined by measuring an optical response from an optical marker on the substrate which is illuminated by an optical source. The signal generated by the optical marker is measured by a sensor arrangement.” – The point at which the sensing or processing is done is illuminated by a marker.; [0126] “Based on the wafer geometry parameters and the deployed predictive model, the system predicts an overlay error for the remaining production wafers and adjusts the lithography scanner to correct for the predicted overlay error. Point-to-point prediction is crucial for feeding forward the predicted overlay, applying the adjustment, and hence reducing the actual overlay error after the exposure.” – David uses point-level data to reduce the overlay error. See the mapping of claims 1 and 15 for a demonstration of the types of data used as input in the model training/prediction, including the first data set, the second data set, and the de-corrected measured overlay data taken at every relevant point.)
Claim 9
Regarding claim 9, David and Ha teach the method of claim 1. David further teaches:
wherein obtaining the first data set, the second data set, and the de-corrected measured overlay data at the given location on the current substrate having the overlay mark comprises: representing values of the first data set, the second data set, and the de-corrected measured overlay data in the form of a respective substrate map; (David [0108] “All available wafer geometry parameters, such as thickness, diameter wafer shape variation, in-plane displacement, stress-induced local curvature, wafer thickness and flatness variation, front and back surface nanotopography (NT), wafer edge roll-off (ERO), sliplines; scanner parameters such as translation (x,y,z), rotation (x,y,z), focus tilt, dose error, focus residual, magnification, asymmetric magnification, asymmetric rotation; CD measurements such as film thickness, trench depth, metal gate recess, high k recess, side wall angle, resist height, hard mask height, pitch walking; film property parameters such as refractive index and absorption coefficient (n & k optical constants); parameters of other overlay measurements such as DBO and IBO (can also include the intensity values of the diffraction signature along with the DBO measurement itself), are used as inputs to the training model, with the corresponding actual overlay error as the target. The location on the wafer of the actual overlay measurement is matched with the location of all of the input parameters for that site, where applicable.” – The data is expressed based on position and orientation data.)
aligning, via modeling and/or interpolation, each of the substrate maps; sharing substrate-level information, within the first data set, the second data set, and the de-corrected measured overlay data, respectively, uniformly across the current substrate; and (David [0053] “Two common concepts for measuring overlay are image based overlay (IBO) and diffraction based overlay (DBO). For IBO, the image position of the substrate pattern is compared to the mask pattern position in the resist layer. Overlay error is a result of the comparison of these two image positions. Imaging approaches are conceptually straightforward, since they are based on analysis of a “picture” directly showing the alignment of the two layers. For example, box-in-box or line-in-line alignment marks are commonly used in the two layers. However, IBO error measurement may be sensitive to vibrations and also to the quality of focus during measurement, which can both result in blurring of the picture. Aberrations in the optics may further reduce the accuracy of the IBO measurement.” [0108] “An overlay process can be performed on one or more training wafers, and the training wafers are then analyzed for actual overlay errors. The most accurate way to measure overlay error is CD-SEM or TEM.” – Data for overlay is aligned.)
extracting the values of the first data set, the second data set, and the de-corrected measured overlay data, respectively, associated with the given location. (David [0055] “To make multi-patterning solutions work, especially in light of the extremely small dimensions now being implemented, the need for more precise and accurate mask overlay has become critically important. In addition to minimizing mask overlay errors, critical dimension uniformity (CDU) has also become important as the convolution of overlay error and critical dimension (CD) variation can lead to shorts, connection failures, and malfunctioning devices.”; [0108] “An overlay process can be performed on one or more training wafers, and the training wafers are then analyzed for actual overlay errors. The most accurate way to measure overlay error is CD-SEM or TEM. All available wafer geometry parameters, such as thickness, diameter wafer shape variation, in-plane displacement, stress-induced local curvature, wafer thickness and flatness variation, front and back surface nanotopography (NT), wafer edge roll-off (ERO), sliplines; scanner parameters such as translation (x,y,z), rotation (x,y,z), focus tilt, dose error, focus residual, magnification, asymmetric magnification, asymmetric rotation; CD measurements such as film thickness, trench depth, metal gate recess, high k recess, side wall angle, resist height, hard mask height, pitch walking; film property parameters such as refractive index and absorption coefficient (n & k optical constants); parameters of other overlay measurements such as DBO and IBO (can also include the intensity values of the diffraction signature along with the DBO measurement itself), are used as inputs to the training model, with the corresponding actual overlay error as the target. The location on the wafer of the actual overlay measurement is matched with the location of all of the input parameters for that site, where applicable. Some process parameters such as temperature, pressure, process duration, etc. and other tool-related parameters are collected on a per-wafer basis and cannot be mapped specifically to a site. Rather, all sites for a given wafer will contain the same values collected for the wafer when site-specific information is not applicable or available. Alternatively if the spatial resolution of the overlay error measurement is greater than the spatial resolution of a given input parameter (e.g. a 9-site CD measurement on a wafer), then the closest input parameter will be mapped to that actual overlay error measurement. A good technique for doing this is k-means clustering. Other techniques include interpolating (3-D) to determine the value of the input parameter or cubic spline.”; Also, see claims 1 and 15 for the mapping to the first data set, the second data set, and the de-corrected measured overlay data; The values would have to be extracted or otherwise retrieved from storage for use as input data in the model. [0111] “Once the training input data set is organized, it is cleansed. The training input data may have corrupted values, in which case the corrupted values are removed and replaced with blanks or null values. The dataset may also contain inconsistent values for various informational features such as lot or wafer ID. For example, a lot description may appear as ‘lot_A’ in some cases and ‘lot.A’ in other cases. These values will all have to be converted to the same nomenclature, for example ‘lot.A.’” – Extraction can include removing bad values from the data.; [0113] “The training dataset can be partitioned into training, testing, and validation portions to ensure a robust model is built that is not over-fit or over-biased. A typical partition can be 60% training, 30% testing, and 10% validation.” – Extraction can include extracting into different training, testing, and validation sets.)
Claims 10 and 16
Regarding claim 16 (and claim 10), David and Ha teach the computer program product of claim 15 (and the method of claim 9). David further teaches:
wherein the model is a substrate-level model, and wherein the values of the set of model parameters of the substrate-level model are determined based on the values of the first data set, the second data set, and the de-corrected measured overlay data across an entire substrate. (David [0115] “That dataset may also have to be merged for a given key. The key typically is an x-y coordinate on the wafer or scanner, or could be a die number. As mentioned above, datasets may need to be mapped to a given key (cubic spline, interpolation, or nearest neighbor). The location on the wafer, such as a specific die or its location, is matched with the location of all of the input parameters for that site, where applicable. Some process parameters such as temperature, pressure, process duration, etc. and other tool-related parameters are collected on a per-wafer basis and cannot be mapped specifically to a site. Rather, all sites for a given wafer will contain the same values collected for the wafer when site-specific information is not applicable or available. Alternatively, if the spatial resolution of the die location is greater than the spatial resolution of a given input parameter (e.g., a 9-site CD measurement on a wafer), then the closest input parameter will be mapped to that actual die. A good technique for doing this is k-means clustering. Other techniques include interpolating (3-D) to determine the value of the input parameter or cubic spline.” – The model and the parameters are based on a model of the wafer/substrate. The teachings of the first data set, the second data set, and the de-corrected measured overlay data are provided in the mapping of claims 1 and 15.)
Regarding claim 10, claim 10 teaches similar to those of claim 16 and is rejected for at least the same reasons as claim 16.
Claims 12 and 18
Regarding claim 18 (and claim 12), David and Ha teach the computer program product of claim 15 (and the method of claim 1). David further teaches:
wherein the model is at least selected from: a linear model that is determined based on (i) the first data set associated with a selected layer of the current substrate or the prior substrates, or (ii) the first data set associated with multiple layers of the current substrate or the prior substrates; or (David [0113] “For other types of models, such as standard linear regression, it is acceptable to separate the partitions into training and testing only.” – David contemplates using linear regression as the model for all of the parameters, including those that were mapped with respect to claims 1 and 15 (e.g., the first data set associated with layers or substrates as claimed here).)
a machine learning model. (David [0082] “In step 612, the data is then fed into the algorithm for training. The algorithm could be one of many different types of algorithms. Examples of machine learning algorithms include Decision Trees, such as CART (Classification and Regression Trees), C5.0, C4.5, and CHAID; Support Vector Regression; Artificial Neural Networks, including Perceptron, Back Propagation, and Deep Learning (BigData enabled); and Ensemble, including Boosting/Bagging, Random Forests, and GBM (Gradient Boosting Machine). The best algorithm may not be a single algorithm, but can be an ensemble of algorithms.” – David teaches that the model is a machine learning model.)
Regarding claim 12, claim 12 recites features similar to claim 18 and is rejected for at least the same reasons.
Claims 13 and 19
Regarding claim 19 (and claim 13), David and Ha teach the computer program product of claim 18 (and the method of claim 12). David further teaches:
wherein the model is a machine learning model and the machine learning model is at least selected from: multi-layer perceptron, random forest, adaptive boosting trees, support vector regression, Gaussian process regression, or k-nearest neighbors. (David [0082] “In step 612, the data is then fed into the algorithm for training. The algorithm could be one of many different types of algorithms. Examples of machine learning algorithms include Decision Trees, such as CART (Classification and Regression Trees), C5.0, C4.5, and CHAID; Support Vector Regression; Artificial Neural Networks, including Perceptron, Back Propagation, and Deep Learning (BigData enabled); and Ensemble, including Boosting/Bagging, Random Forests, and GBM (Gradient Boosting Machine). The best algorithm may not be a single algorithm, but can be an ensemble of algorithms.”; [0115] “As mentioned above, datasets may need to be mapped to a given key (cubic spline, interpolation, or nearest neighbor).” – David teaches that the model is one of the machine learning models in claim 19.)
Regarding claim 13, claim 13 recites features similar to claim 19 and is rejected for at least the same reasons.
Claims 14 and 20
Regarding claim 20 (and claim 14), David and Ha teach the computer program product of claim 18 (and the method of claim 12). David further teaches:
wherein the model is a machine learning model and the machine learning model is a machine learning model including at least one selected from: a residual neural network (RNN) or a convolutional neural network (CNN). (David [0082] “In step 612, the data is then fed into the algorithm for training. The algorithm could be one of many different types of algorithms. Examples of machine learning algorithms include Decision Trees, such as CART (Classification and Regression Trees), C5.0, C4.5, and CHAID; Support Vector Regression; Artificial Neural Networks, including Perceptron, Back Propagation, and Deep Learning (Big Data enabled); and Ensemble, including Boosting/Bagging, Random Forests, and GBM (Gradient Boosting Machine). The best algorithm may not be a single algorithm, but can be an ensemble of algorithms.”; – David teaches that the model is an ANN, which is a genus and obvious variant of a CNN, as recited in claim 20.)
Regarding claim 14, claim 14 recites features similar to claim 20 and is rejected for at least the same reasons.
Claims 11 and 17: David, Ha, and Wu
Claims 11 and 17 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over US 2016/0148850 A1 to David (David) in view of US 2018/0330511 A1 to Ha et al. (Ha) and NPL: “Robust and efficient mask synthesis with basis function representation” to Wu et al. (Wu).
Regarding claim 17 (and claim 11), David and Ha teach the computer program product of claim 16 (and the method of claim 10). David and Ha appear to fail to teach, but Wu teaches:
wherein the instructions are further configured to: generate a plurality of substrate maps using values of the first data set, the second data set, and the de-corrected measured overlay data, respectively, associated with each of a plurality of substrates; (Wu Page B2, Left Column, Third Paragraph “In this paper, we develop a mask representation method with a set of basis functions, and incorporate this representation into robust ILT. Specifically, we use the 2D discrete cosine basis as the basis functions. The higher order coefficients are neglected to reduce the number of variables and avoid stagnation in the iterative optimization method. Instead of updating the pixel values in the mask patterns, we update the representation coefficients according to the gradient of the objective function to the coefficients.”; Page B2, Right Column, Third Paragraph “We also minimize the total variation of the mask patterns as a regularization term to improve the manufacturability.” – The masks are a plurality of substrate maps based on overlay data such as the first data set, the second data set, and the third data set (the mapping to the data as input provided with regard to claims 1 and 15).
project each of the plurality of substrate maps to a basis function; and (Wu Page B3, Left Column, Fourth Paragraph “In this paper, we use the 2D discrete cosine basis as the basis functions to represent the mask patterns.”)
determine, based on the projecting, projection coefficients associated with the basis function, the projection coefficients and other substrate-level data being used to define the substrate model. (Wu Page B3, Right Column, First Paragraph “We depict the DCT coefficients for a common mask pattern and the sampling scheme in Fig.1. It is obviously shown in Fig. 1(a) that the higher order coefficients are very small, and are almost flat at zero plane. Thus, we can select the lower order coefficients to represent the mask patterns. A further illustration of this point is shown in Fig.2, where we depict the mask patterns represented by its partial DCT coefficients and the corresponding aerial images.” – The projecting to the basis function yields coefficients representing the masks as well as scaling data associated with the substrate.)
Regarding claim 11, claim 11 recites features similar to claim 17 and is rejected for at least the same reasons.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the overlay analysis of David with the mask data reduction of Wu because a person of ordinary skill in the art would be motivated based on the expressed desire to reduce the overlay data input dimensionality in David to look to Wu to reduce the input load on the machine learning model by reducing the dimensionality of the mask/overlay representation used as input into the machine learning model. (David [0081] “In step 610, a dimensionality reduction or feature selection step is performed. The purpose of this step is to reduce the number of input parameters for the algorithm. Dimensionality reduction techniques are generally known, for example, principle component analysis (PCA).” [0116]-[0118] " A training input dataset may contain thousands of input features, and a relevant set of input features may need to be determined. A process for removing irrelevant input features that weakly correlate to overlay error may need to be implemented. As a first step in this process, input features that do not change at all can be removed. There are also a number of approaches to feature selection. One approach is implementing random forests which identify which input features are most relevant to predicting overlay error. Another technique is the CHAID decision tree, which will also identify features that are important. Linear regression is another technique. ANOVA is another technique. Alternatively, dimensionality reduction can also be employed. Common dimensionality reduction techniques include partial least squares and principal component analysis, which will create a new smaller set of input parameters based on the large set of initial input parameters. For example, an input set of 5000 features can be reduced to an input set of 30 newly-generated principle components that can explain a significant portion of the variance in the data. The outcome or output of the dimensionality reduction step can be used as new inputs to the model. For example, the principle components generated by PCA can be inputs to the model. The principle components will represent a reduced set of inputs from a larger set of inputs.”; Wu Page B2, Left Column, Second Paragraph “Nevertheless, these algorithms still need to handle all the pixel variables, which can be a large number in current mask optimization. These variables can cause stagnation in the inverse optimization, and slow down the convergence [31]. In this work, we reduce the number of variables in mask optimization by decomposing the mask patterns into a linear combination of a set of basis functions.” – The method reduces dimensionality of representations of masks with limited loss of data. ; Page B8, Left Column, 5. CONCLUSIONS “In this paper, we propose a mask optimization algorithm with basis function representation, and use an adaptive optimization method to accelerate the algorithms. We use the 2D discrete cosine basis functions to represent the mask patterns, and incorporate this representation into the CG algorithm for optimization. We also employ an adaptive method which uses a small number of kernels to get initial patterns, and then use more kernels for fine optimization to accelerate the mask optimization. Simulations performed on two test patterns at both the nominal plane and various defocus planes for robust ILT demonstrate that the proposed method can improve the efficiency by approximately 2 times, while achieving optimized mask patterns with competitive performance compared with the regular pixel-based method.”)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
(From Prior Action)
WO 2018/140534 A1 to Adel et al. (Teaches making modifications to a current layer based on measurements from the last layer)
US 2019/0094721 A1 to Tinnemans et al. (Teaches optimizing operating parameters based on comparison of a substrate to a substrate variation)
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