Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed 1/14/26 have been fully considered but they are not persuasive.
In regards to the 101 rejection, applicant argues “the claim amended claim 1 now ties its operations to concrete technical activity that, as a practical matter, cannot be performed mentally and that is integrated into a real-world application… discovering and modeling unknown inter-step and inter-queue-time dependencies across the entire claimed process-step sequence to control manufacturing tools and assembly tools are data-driven computational task at industrial scale, not a mental observation… this limitations ties the claimed mining and prediction to control actions over specific machines that process physical components into a testable product, including maintaining and adjusting queue-times and operations to achieve desire yield and quality outcomes”
Examiner notes that using these steps to provide instructions to control to generate a product does not actually require processing physical components into testable products as argued, additionally the claim language does not include adjusting the data to change the outcomes. Therefore examiner does not agree that the limitations add practical application, and has maintained the 101 rejection.
In regards to the 103 rejection applicant argues, “Support for the new claim language in amended claims…the “unknown dependency relationships” among process-steps and queue-times are supported by the description that entire fabrication sequences involve “combinatorial dependency structure…Reconsideration and allowance of the claims are respectfully requested in view of the above amendments and the following remarks”
Examiner notes that the claim language requires “wherein the plurality of process-steps and the plurality of queue-times comprise process-steps and queue times having unknown dependency relationships with one another” and applicant pointed to [0031] and [0038] for support and although [0038] does discloses not annotated or labeled (i.e. the quality and yield characteristics of the training process-step sequence are unknown. Where is the connection to the queue times? As the word time is not even mentioned in either of these paragraphs indicated as support. Therefore this unknown dependency relationship between process steps and queue times is unclear. For interpretation purpose examiner has interpreted this as defining the relationship between dependent variables for prediction models. Please view the rejection for further information.
Claim Rejections - 35 USC § 112
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1, 8, and 15 recite “wherein the plurality of process-steps and the plurality of queue-times comprise process-steps and queue times having unknown dependency relationships with one another” which is lacking sufficient support in the specification. Applicant points to paragraphs 31 which discloses data dependency and paragraph 38 which discloses training process-step sequences are unknown, however the link between unknown and dependency relationships as claimed was not described in the specification. Dependent claims 2-7, 9-14, and 16-20 are rejected due to their dependency on Claims 1, 8, and 15.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 1, 8, and 15 recite “wherein the plurality of process-steps and the plurality of queue-times comprise process-steps and queue times having unknown dependency relationships with one another” which is not sufficiently supported in the specification, making “unknown dependency relationships” unclear. For interpretation purpose examiner has interpreted this as defining the relationship between dependent variables for prediction models. Dependent claims 2-7, 9-14, and 16-20 are rejected due to their dependency on Claims 1, 8, and 15.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “making a prediction, taking into account the predicted dependency relationships, of an impact of one or more portions of the process-step sequence on characteristics of a testable product generated by the process-step sequence”, and “discovering the unknown dependency relationships” which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example the language in the context of this claim encompasses that the user mentally could make a decision, observation, and calculation. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements- “A computer-implemented method comprising: accessing, using a processor system, a process-step sequence comprising a plurality process-steps and a plurality of queue-times”, “ performing, using the processor system, a process-step sequence mining operation on the process-step sequence”, “wherein performing the process-step sequence mining comprises:”, “generating therefrom predicted dependency relationship”, “using the process-step sequence and the prediction to provide instructions to controller components to control how equipment processes product components to generate the testable product”, “wherein the equipment comprises manufacturing tools and assembly tools, the controller components configured to control operations performed by the manufacturing tools and the assembly tools to execute the process-step sequence” which is simply using a computer as a tool to perform abstract ideas -Mere instructions to apply an exception – see MPEP 2106.05(f). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Accordingly these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of“A computer-implemented method comprising: accessing, using a processor system, a process-step sequence comprising a plurality process-steps and a plurality of queue-times”, “ performing, using the processor system, a process-step sequence mining operation on the process-step sequence”, “wherein performing the process-step sequence mining comprises:”, “generating therefrom predicted dependency relationship”, “using the process-step sequence and the prediction to provide instructions to controller components to control how equipment processes product components to generate the testable product”, “wherein the equipment comprises manufacturing tools and assembly tools, the controller components configured to control operations performed by the manufacturing tools and the assembly tools to execute the process-step sequence” , which is simply using a computer as a tool to perform abstract ideas -Mere instructions to apply an exception – see MPEP 2106.05(f), which is considered to be well-understood, routine, conventional activity. Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim inherits the mental abstract idea from claim 1. The claim recites “wherein the process-step sequence mining operation comprises: encoding the process-step sequence to generate an encoded process-step sequence having a plurality of encoded process-steps and a plurality of encoded queue-times; and applying, using the processor, a dimensionality reduction operation to the encoded process-step sequence” which is simply using a computer as a tool to perform abstract ideas -Mere instructions to apply an exception – see MPEP 2106.05(f). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim inherits the mental abstract idea from claim 1. The claim recites “wherein: applying the dimensionality reduction operation to the encoded process-step sequence generates a reduced-dimension encoded process-step sequence; and the process-step sequence mining operation further comprises applying the reduced-dimension encoded process-step sequence to a predictive model operable to perform a task” which is simply using a computer as a tool to perform abstract ideas -Mere instructions to apply an exception – see MPEP 2106.05(f). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim inherits the mental abstract idea from claim 1. The claim recites “wherein the task comprises the prediction of the impact of the one or more portions of the process-step sequence on the characteristics of the testable product generated by the process-step sequence” which is simply using a computer as a tool to perform abstract ideas -Mere instructions to apply an exception – see MPEP 2106.05(f). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “wherein making the prediction of the impact of the one or more portions of the process-step sequence on the characteristics of the testable product comprises: performing a first comparison of the reduced-dimension encoded process-step sequence with a first cluster associated with a first measurement range of the characteristics; performing a second comparison of the reduced-dimension encoded process-step sequence to a second cluster associated with a second measurement range of the characteristics; and associating the reduced-dimension encoded process-step sequence with the first cluster or the second cluster based on a result of the first comparison and a result of the second comparison” which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example the language in the context of this claim encompasses that the user mentally could make a decision, observation, and calculation. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “to predict a portion of the process-step sequence having a positive impact on the characteristics of the testable product” which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example the language in the context of this claim encompasses that the user mentally could make a decision, observation, and calculation. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The claim additionally recites “comprising using a pattern sequence extraction module of the processor system” which falls under field of use and technological environment- see MPEP 2106.05(h) Parker v. Flook ("Flook established that limiting an abstract idea to one field of use or adding token postsolution components did not make the concept patentable"). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “the characteristics are selected from the group consisting of wafer yield, die yield, wafer quality, and die quality… and making the prediction of the impact of the one or more portions of the process-step sequence on the characteristics of the testable product generated by the process-step sequence comprises evaluating the plurality of symbols against a process-step sequence language domain” which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example the language in the context of this claim encompasses that the user mentally could make a decision, observation, and calculation. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The claim additionally recites “the plurality of process-steps comprises a plurality of semiconductor product fabrication operations “, and “the testable product comprises a wafer having dies and completed integrated circuitry ready for testing” which falls under field of use and technological environment- see MPEP 2106.05(h) Parker v. Flook ("Flook established that limiting an abstract idea to one field of use or adding token postsolution components did not make the concept patentable"). The claim recites “encoding the process-step sequence to generate the encoded process-step sequence comprise converting the plurality of process-steps and the plurality of queue-times to a plurality of symbols; the dimensionality reduction operation comprises an embedding operation” which is simply using a computer as a tool to perform abstract ideas -Mere instructions to apply an exception – see MPEP 2106.05(f). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 8 is rejected under 35 U.S.C. 101 for similar reason to claim 1.
Claim 9 is rejected under 35 U.S.C. 101 for similar reason to claim 2.
Claim 10 is rejected under 35 U.S.C. 101 for similar reason to claim 3.
Claim 11 is rejected under 35 U.S.C. 101 for similar reason to claim 4.
Claim 12 is rejected under 35 U.S.C. 101 for similar reason to claim 5.
Claim 13 is rejected under 35 U.S.C. 101 for similar reason to claim 6.
Claim 14 is rejected under 35 U.S.C. 101 for similar reason to claim 7.
Claim 15 is rejected under 35 U.S.C. 101 for similar reason to claim 1.
Claim 16 is rejected under 35 U.S.C. 101 for similar reason to claim 2.
Claim 17 is rejected under 35 U.S.C. 101 for similar reason to claim 3 & 4.
Claim 18 is rejected under 35 U.S.C. 101 for similar reason to claim 5.
Claim 19 is rejected under 35 U.S.C. 101 for similar reason to claim 6.
Claim 20 is rejected under 35 U.S.C. 101 for similar reason to claim 7.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Honda et al (US20220327268, herein Honda), in view of Norman et al. (US20220026891, herein Norman).
Regarding claim 1, Honda teaches A computer-implemented method comprising: accessing, using a processor system, a process-step sequence comprising a plurality process-steps…([0025] Fig. 1 …semiconductor manufacturing process 100… voluminous amounts of data that represent various aspects of the process can be collected at every step and sub-step of a production run and provided as input data to various forms of systematic analysis. For example, yield and other performance characteristics may be calculated from selected input data for each step, as well as predictions made for key processing parameters for the entirety of the process); performing, using the processor system, a process-step sequence mining operation on the process-step sequence ([0026] Wafer fabrication occurs in step 102, wherein a large number of integrated circuits are formed on a single slice of semiconductor substrate, such as silicon, known as a wafer. Many steps are required in various sequences to build different integrated circuits); wherein the plurality of process-steps … comprise process-steps and … times having unknown dependency relationships with one another ([0055] methods to pre-process data can include but are not limited to the following: (i) time series segmentation (including based on known manufacturing steps or automatic detection of sharp changes in signal measurements, [0113] Regression techniques can also be effective for defining the relationships between dependent variables and independent variables when modeling a target feature)); wherein performing the process-step sequence mining operation comprises: discovering the unknown dependency relationships and generating therefrom predicted dependency relationships ([0113] Regression techniques can also be effective for defining the relationships between dependent variables and independent variables when modeling a target feature. As an example, predicting yield for low-yielding wafers may be identified by the customer as more critical than predicting yield for high-yielding wafers, since correction of the issue causing the low yield has a more significant impact on overall yield and cost. Further, since this issue is usually a function of equipment failure or wear, a model to predict remaining useful life (RUL) for processing equipment can be very useful for the customer and effectively modeled using regression techniques, [0025] yield and other performance characteristics may be calculated from selected input data for each step, as well as predictions made for key processing parameters for the entirety of the process); and making a prediction, taking into account the predicted dependency relationships, of an impact of one or more portions of the process-step sequence on characteristics of a testable product generated by the process-step sequence, ([0076] log data could be parsed using Natural Language Processing (NLP) and Text Mining techniques to convert the log data into features…data from a “normal” operation condition should be added to the ML model training set such that a “normal” class is one of the possible predictions of the model, [0087] a robust predictive model with rational decision making that is production-worthy is illustrated in FIG. 9A), [0099] the optimal model and relevant effective thresholds are selected based on the consideration of trade-offs from the customer survey. The results of different combinations of limits and thresholds for different parameters can be presented graphically or as data tables in the GUI in order to illustrate for the customer the impact of changes in selection criteria. For example, if returning a false positive for chip has an economic impact that is 100 times more costly than returning a true positive, then model A 1001 on FIG. 10 should be selected and thresholds set that produce 40% true positive and less than 0.5% false positives. However, if returning a true positive is considered 100 times more important than returning a false positive for any rationale, the model B 1002 on FIG. 10 should be selected and thresholds set that produce 30% false positive and greater than 99.5% true positives, [0085] This description provides a method for guiding a user in rationally selecting predictive models for semiconductor manufacturing applications, [0113] Regression techniques can also be effective for defining the relationships between dependent variables and independent variables when modeling a target feature. As an example, predicting yield for low-yielding wafers may be identified by the customer as more critical than predicting yield for high-yielding wafers, since correction of the issue causing the low yield has a more significant impact on overall yield and cost. Further, since this issue is usually a function of equipment failure or wear, a model to predict remaining useful life (RUL) for processing equipment can be very useful for the customer and effectively modeled using regression techniques,); and using the process-step sequence and the prediction to provide instructions to controller components to control how equipment processes product components to generate the testable product ([0085] This description provides a method for guiding a user in rationally selecting predictive models for semiconductor manufacturing applications, [0051] The FDC pipeline 300 can be used as a prognostic/preventive maintenance tool for process equipment by combining temporal trends in wafer prediction with drift and shift data in sensor measurements and ML features); wherein the equipment comprises manufacturing tools and assembly tools, the controller components configured to control operations performed by the manufacturing tools and the assembly tools to execute the process-step sequence ([0106] For example, FIG. 16 illustrates a process 1600 for using the decision-making tool to evaluate equipment issues. After building a model with customer preferences and limits and deploying the same into production at step 1601, an equipment failure issue arises and the first question in step 1602 is whether the failure is catastrophic. If so, then in step 1604 an immediate process shutdown is called for and the problem corrected. If the equipment failure is not catastrophic, then an inspection of the equipment is undertaken in step 1606, [0069] semiconductor tools, [0110] early detection/prediction of catastrophic failure is obviously important for any manufacturing operation, [0033] Standard statistical and process control techniques were used to analyze and utilize the datasets to improve yields and manufacturing efficiencies ).
Honda does not teach and a plurality of queue-times
Norman teaches and a plurality of queue-times ([0055] the number of substrates of the substrate queue that can be started that the initiating operation within the time period, [0021] Based on the additional simulation output, the processing device can identify additional candidate substrates (e.g., from the substrate queue) and can initiate the set of operations to process the number of candidate substrates from the simulation output and the additional candidate substrates over the time period, [0056] a second number of candidate substrates that were successfully processed during each of the simulated set of operations to reach the end of the second time period, [0008] first number of candidate substrates over the first time period)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Honda’s teaching a predictive model for semiconductor processes with Norman’s teaching of the semiconductor processes including the queue times. The combined teaching provides an expected result of a predictive model for semiconductor processes including the queue times. Therefore, one of ordinary skill in the art would be motivated to optimize the prediction model as disclosed by Norman [0004] enable yield enhancement, improvement of process quality control, reduce manufacturing costs and amount of scrap, as well as improve equipment uptime by identifying systematic issues quickly.
Regarding claim 2, the combination of Honda and Norman teach The computer-implemented method of claim 1, wherein the process-step sequence mining operation comprises: encoding the process-step sequence to generate an encoded process-step sequence having a plurality of encoded process-steps…; and applying, using the processor, a dimensionality reduction operation to the encoded process-step sequence (Honda, [0076] log data could be parsed using Natural Language Processing (NLP) and Text Mining techniques to convert the log data into features…data from a “normal” operation condition should be added to the ML model training set such that a “normal” class is one of the possible predictions of the model, [0155] the data set can be prepared for modeling by… we can use linear as well as non-linear dimensionality reduction on the full dataset. Approaches can include: Auto encoder)
Norman further teaches and a plurality of encoded queue-times ([0055] the number of substrates of the substrate queue that can be started that the initiating operation within the time period, [0021] Based on the additional simulation output, the processing device can identify additional candidate substrates (e.g., from the substrate queue) and can initiate the set of operations to process the number of candidate substrates from the simulation output and the additional candidate substrates over the time period, [0056] a second number of candidate substrates that were successfully processed during each of the simulated set of operations to reach the end of the second time period, [0008] first number of candidate substrates over the first time period)
Regarding claim 3, the combination of Honda and Norman teach The computer-implemented method of claim 2, wherein: applying the dimensionality reduction operation to the encoded process-step sequence generates a reduced-dimension encoded process-step sequence; and the process-step sequence mining operation further comprises applying the reduced-dimension encoded process-step sequence to a predictive model operable to perform a task (Honda, [0076] log data could be parsed using Natural Language Processing (NLP) and Text Mining techniques to convert the log data into features…data from a “normal” operation condition should be added to the ML model training set such that a “normal” class is one of the possible predictions of the model, [0155] the data set can be prepared for modeling by assigning to each chip the raw measurement fields… predictor variables … we can use linear as well as non-linear dimensionality reduction on the full dataset. Approaches can include: Auto encoder)
Regarding claim 4, the combination of Honda and Norman teach The computer-implemented method of claim 3, wherein the task comprises the prediction of the impact of the one or more portions of the process-step sequence on the characteristics of the testable product generated by the process-step sequence (Honda, [0076] log data could be parsed using Natural Language Processing (NLP) and Text Mining techniques to convert the log data into features…data from a “normal” operation condition should be added to the ML model training set such that a “normal” class is one of the possible predictions of the model, [0155] the data set can be prepared for modeling by assigning to each chip the raw measurement fields… predictor variables … we can use linear as well as non-linear dimensionality reduction on the full dataset. Approaches can include: Auto encoder)
Regarding claim 5, the combination of Honda and Norman teach The computer-implemented method of claim 4, wherein making the prediction of the impact of the one or more portions of the process-step sequence on the characteristics of the testable product comprises: performing a first comparison of the reduced-dimension encoded process-step sequence with a first cluster associated with a first measurement range of the characteristics; performing a second comparison of the reduced-dimension encoded process-step sequence to a second cluster associated with a second measurement range of the characteristics; and associating the reduced-dimension encoded process-step sequence with the first cluster or the second cluster based on a result of the first comparison and a result of the second comparison (Honda, [0088] In step 904, a predictive model is generated for predicting relevant target features. If the results from the predictive model meet the minimum performance criteria in step 905, such as False Positive Rate (FPR) of 0.1% and False Negative Rate (FNR) of 1%, the model is saved in step 906 for use in the comparative analysis described below. Other performance criteria could be used, including Area under the Curve (AUC), Area under the Precision-Recall curve, F1 Score, Skip Rate, etc. In step 907, if additional predictive models are needed for an adequate analysis, another predictive model is generated for predicting relevant target features using a different methodology in step 912, and then compared as before to the minimum performance criteria in step 905. It should be noted that different methodologies include different algorithms and mathematical functions including statistical solutions that may be implemented as machine learning models with supervised learning and reinforced learning, [0155] the data set can be prepared for modeling by assigning to each chip the raw measurement fields… predictor variables … we can use linear as well as non-linear dimensionality reduction on the full dataset. Approaches can include: Auto encoder)
Regarding claim 6, the combination of Honda and Norman teach. The computer-implemented method of claim 5 further comprising using a pattern sequence extraction module of the processor system to predict a portion of the process-step sequence having a positive impact on the characteristics of the testable product (Honda, [0087] The basic steps for building a first embodiment of a robust predictive model with rational decision making that is production-worthy is illustrated in FIG. 9A. In step 902, minimum performance criteria for the target feature(s) are obtained for a predictive model (including a machine learning model, pattern recognition model, physics-based Model, hybrid model, etc.). These criteria include but are not limited to training speed, prediction speed, accuracy, false positive rate, false negative rate, recall, precision, F1 score, F2 score, F-N score, root mean square error, root mean square log error, mean absolute error, worst case error, robustness, generalizability, etc., [0076] For example, log data could be parsed using Natural Language Processing (NLP) and Text Mining techniques to convert the log data into features. Temporal data analysis could be transformed using a time series analysis like Autoregressive Integrated Moving Average (ARIMA), Kalman Filter, Particle Filter, etc. to extract relevant features)
Regarding claim 7, the combination of Honda and Norman teach The computer-implemented method of claim 4, wherein: the plurality of process-steps comprises a plurality of semiconductor product fabrication operations (Honda, [0026] Wafer fabrication occurs in step 102, wherein a large number of integrated circuits are formed on a single slice of semiconductor substrate, such as silicon, known as a wafer); the testable product comprises a wafer having dies and completed integrated circuitry ready for testing ([0029] The wafer is diced up into separate individual circuits or dies, and each die that passes through wafer sort and test is bonded to and electrically connected to a frame to form a package. Each die/package is then encapsulated to protect the circuit); the characteristics are selected from the group consisting of wafer yield, die yield, wafer quality, and die quality ([0039] the input data is analyzed using machine learning techniques to identify key features and/or characteristics of the data in order to classify wafers and/or lots of semiconductor devices. In step 206, the wafers or devices are classified based on the analysis of step 204, [0099] the optimal model and relevant effective thresholds are selected based on the consideration of trade-offs from the customer survey. The results of different combinations of limits and thresholds for different parameters can be presented graphically or as data tables in the GUI in order to illustrate for the customer the impact of changes in selection criteria. For example, if returning a false positive for chip has an economic impact that is 100 times more costly than returning a true positive, then model A 1001 on FIG. 10 should be selected and thresholds set that produce 40% true positive and less than 0.5% false positives. However, if returning a true positive is considered 100 times more important than returning a false positive for any rationale, the model B 1002 on FIG. 10 should be selected and thresholds set that produce 30% false positive and greater than 99.5% true positives, [0102] a key question for the customer is how much yield loss is acceptable? Further, would additional visual inspection, triggered by the predictive model, detect equipment issues earlier, such as a few hours, a few days, or few weeks earlier?) ; encoding the process-step sequence to generate the encoded process-step sequence comprise converting the plurality of process-steps [0155] the data set can be prepared for modeling by assigning to each chip the raw measurement fields… predictor variables … we can use linear as well as non-linear dimensionality reduction on the full dataset. Approaches can include: Auto encoder) … to a plurality of symbols ([0076] log data could be parsed using Natural Language Processing (NLP) and Text Mining techniques to convert the log data into features…data from a “normal” operation condition should be added to the ML model training set such that a “normal” class is one of the possible predictions of the model); the dimensionality reduction operation comprises an embedding operation; and making the prediction of the impact of the one or more portions of the process-step sequence on the characteristics of the testable product generated by the process-step sequence comprises evaluating the plurality of symbols against a process-step sequence language domain ([0155] the data set can be prepared for modeling by assigning to each chip the raw measurement fields… predictor variables … we can use linear as well as non-linear dimensionality reduction on the full dataset. Approaches can include: Auto encoder, [0076] log data could be parsed using Natural Language Processing (NLP) and Text Mining techniques to convert the log data into features…data from a “normal” operation condition should be added to the ML model training set such that a “normal” class is one of the possible predictions of the model).
Norman further teaches and a plurality of queue-times ([0055] the number of substrates of the substrate queue that can be started that the initiating operation within the time period, [0021] Based on the additional simulation output, the processing device can identify additional candidate substrates (e.g., from the substrate queue) and can initiate the set of operations to process the number of candidate substrates from the simulation output and the additional candidate substrates over the time period, [0056] a second number of candidate substrates that were successfully processed during each of the simulated set of operations to reach the end of the second time period, [0008] first number of candidate substrates over the first time period)
Regarding claim 8, Honda teaches A computer system comprising a memory communicatively coupled to a processor system, wherein the processor system is configured to perform processor system operations comprising: accessing a process-step sequence comprising a plurality process-steps …(Claim 22 A robust predictive model for predicting a target feature in a semiconductor process including a processor and non-transitory storage for program instruction, the program instructions configured for causing the processor to,[0025] Fig. 1 …semiconductor manufacturing process 100… voluminous amounts of data that represent various aspects of the process can be collected at every step and sub-step of a production run and provided as input data to various forms of systematic analysis. For example, yield and other performance characteristics may be calculated from selected input data for each step, as well as predictions made for key processing parameters for the entirety of the process); performing a process-step sequence mining operation to the process-step sequence ([0026] Wafer fabrication occurs in step 102, wherein a large number of integrated circuits are formed on a single slice of semiconductor substrate, such as silicon, known as a wafer. Many steps are required in various sequences to build different integrated circuits); wherein the plurality of process-steps and the plurality of …times comprise process-steps and …times having unknown dependency relationships with one another ([0055] methods to pre-process data can include but are not limited to the following: (i) time series segmentation (including based on known manufacturing steps or automatic detection of sharp changes in signal measurements, [0113] Regression techniques can also be effective for defining the relationships between dependent variables and independent variables when modeling a target feature); wherein performing the process-step sequence mining operation comprise: discovering the unknown dependency relationships and generating therefrom predicted dependency relationships ([0113] Regression techniques can also be effective for defining the relationships between dependent variables and independent variables when modeling a target feature. As an example, predicting yield for low-yielding wafers may be identified by the customer as more critical than predicting yield for high-yielding wafers, since correction of the issue causing the low yield has a more significant impact on overall yield and cost. Further, since this issue is usually a function of equipment failure or wear, a model to predict remaining useful life (RUL) for processing equipment can be very useful for the customer and effectively modeled using regression techniques, [0025] yield and other performance characteristics may be calculated from selected input data for each step, as well as predictions made for key processing parameters for the entirety of the process); and making a prediction, taking into account the predicted dependency relationships of an impact of one or more portions of the process-step sequence on a characteristics of a testable product generated by the process-step sequence (([0076] log data could be parsed using Natural Language Processing (NLP) and Text Mining techniques to convert the log data into features…data from a “normal” operation condition should be added to the ML model training set such that a “normal” class is one of the possible predictions of the model, [0087] a robust predictive model with rational decision making that is production-worthy is illustrated in FIG. 9A), [0099] the optimal model and relevant effective thresholds are selected based on the consideration of trade-offs from the customer survey. The results of different combinations of limits and thresholds for different parameters can be presented graphically or as data tables in the GUI in order to illustrate for the customer the impact of changes in selection criteria. For example, if returning a false positive for chip has an economic impact that is 100 times more costly than returning a true positive, then model A 1001 on FIG. 10 should be selected and thresholds set that produce 40% true positive and less than 0.5% false positives. However, if returning a true positive is considered 100 times more important than returning a false positive for any rationale, the model B 1002 on FIG. 10 should be selected and thresholds set that produce 30% false positive and greater than 99.5% true positives, [0085] This description provides a method for guiding a user in rationally selecting predictive models for semiconductor manufacturing applications, [0113] Regression techniques can also be effective for defining the relationships between dependent variables and independent variables when modeling a target feature. As an example, predicting yield for low-yielding wafers may be identified by the customer as more critical than predicting yield for high-yielding wafers, since correction of the issue causing the low yield has a more significant impact on overall yield and cost. Further, since this issue is usually a function of equipment failure or wear, a model to predict remaining useful life (RUL) for processing equipment can be very useful for the customer and effectively modeled using regression techniques) and using the process-step sequence and the prediction to provide instructions to controller components to control how equipment processes product components to generate the testable product ([0085] This description provides a method for guiding a user in rationally selecting predictive models for semiconductor manufacturing applications, [0051] The FDC pipeline 300 can be used as a prognostic/preventive maintenance tool for process equipment by combining temporal trends in wafer prediction with drift and shift data in sensor measurements and ML features)); wherein the equipment comprises manufacturing tools and assembly tools, the controller components configured to control operations performed by the manufacturing tools and the assembly tools to execute the process-step sequence ([0106] For example, FIG. 16 illustrates a process 1600 for using the decision-making tool to evaluate equipment issues. After building a model with customer preferences and limits and deploying the same into production at step 1601, an equipment failure issue arises and the first question in step 1602 is whether the failure is catastrophic. If so, then in step 1604 an immediate process shutdown is called for and the problem corrected. If the equipment failure is not catastrophic, then an inspection of the equipment is undertaken in step 1606, [0069] semiconductor tools, [0110] early detection/prediction of catastrophic failure is obviously important for any manufacturing operation, [0033] Standard statistical and process control techniques were used to analyze and utilize the datasets to improve yields and manufacturing efficiencies ).
Honda does not teach and a plurality of queue-times
Norman teaches and a plurality of queue-times ([0055] the number of substrates of the substrate queue that can be started that the initiating operation within the time period, [0021] Based on the additional simulation output, the processing device can identify additional candidate substrates (e.g., from the substrate queue) and can initiate the set of operations to process the number of candidate substrates from the simulation output and the additional candidate substrates over the time period, [0056] a second number of candidate substrates that were successfully processed during each of the simulated set of operations to reach the end of the second time period, [0008] first number of candidate substrates over the first time period)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Honda’s teaching a predictive model for semiconductor processes with Norman’s teaching of the semiconductor processes including the queue times. The combined teaching provides an expected result of a predictive model for semiconductor processes including the queue times. Therefore, one of ordinary skill in the art would be motivated to optimize the prediction model as disclosed by Norman [0004] enable yield enhancement, improvement of process quality control, reduce manufacturing costs and amount of scrap, as well as improve equipment uptime by identifying systematic issues quickly.
Regarding claim 9, the combination of Honda and Norman teach The computer system of claim 8, wherein the process-step sequence mining operation comprises: encoding the process-step sequence to generate an encoded process-step sequence having a plurality of encoded process-steps and a plurality of encoded… ; and applying, using the processor, a dimensionality reduction operation to the encoded process-step sequence (Honda, [0076] log data could be parsed using Natural Language Processing (NLP) and Text Mining techniques to convert the log data into features…data from a “normal” operation condition should be added to the ML model training set such that a “normal” class is one of the possible predictions of the model, [0155] the data set can be prepared for modeling by… we can use linear as well as non-linear dimensionality reduction on the full dataset. Approaches can include: Auto encoder).
Norman further teaches and a plurality of encoded queue-times ([0055] the number of substrates of the substrate queue that can be started that the initiating operation within the time period)
Regarding claim 10, the combination of Honda and Norman teach The computer system of claim 9, wherein: applying the dimensionality reduction operation to the encoded process-step sequence generates a reduced-dimension encoded process-step sequence; and the process-step sequence mining operation further comprises applying the reduced-dimension encoded process-step sequence to a predictive model operable to perform a task (Honda, [0076] log data could be parsed using Natural Language Processing (NLP) and Text Mining techniques to convert the log data into features…data from a “normal” operation condition should be added to the ML model training set such that a “normal” class is one of the possible predictions of the model, [0155] the data set can be prepared for modeling by assigning to each chip the raw measurement fields… predictor variables … we can use linear as well as non-linear dimensionality reduction on the full dataset. Approaches can include: Auto encoder).
Regarding claim 11, the combination of Honda and Norman teach. The computer system of claim 10, wherein the task comprises the prediction of the impact of the one or more portions of the process-step sequence on the characteristics of the testable product generated by the process-step sequence (Honda, [0076] log data could be parsed using Natural Language Processing (NLP) and Text Mining techniques to convert the log data into features…data from a “normal” operation condition should be added to the ML model training set such that a “normal” class is one of the possible predictions of the model, [0155] the data set can be prepared for modeling by assigning to each chip the raw measurement fields… predictor variables … we can use linear as well as non-linear dimensionality reduction on the full dataset. Approaches can include: Auto encoder).
Regarding claim 12, the combination of Honda and Norman teach. The computer system of claim 11, wherein making the prediction of the impact of the one or more portions of the process-step sequence on the characteristics of the testable product comprises: performing a first comparison of the reduced-dimension encoded process-step sequence with a first cluster associated with a first measurement range of the characteristics; performing a second comparison of the reduced-dimension encoded process-step sequence to a second cluster associated with a second measurement range of the characteristics; and associating the reduced-dimension encoded process-step sequence with the first cluster or the second cluster based on a result of the first comparison and a result of the second comparison (Honda, [0088] In step 904, a predictive model is generated for predicting relevant target features. If the results from the predictive model meet the minimum performance criteria in step 905, such as False Positive Rate (FPR) of 0.1% and False Negative Rate (FNR) of 1%, the model is saved in step 906 for use in the comparative analysis described below. Other performance criteria could be used, including Area under the Curve (AUC), Area under the Precision-Recall curve, F1 Score, Skip Rate, etc. In step 907, if additional predictive models are needed for an adequate analysis, another predictive model is generated for predicting relevant target features using a different methodology in step 912, and then compared as before to the minimum performance criteria in step 905. It should be noted that different methodologies include different algorithms and mathematical functions including statistical solutions that may be implemented as machine learning models with supervised learning and reinforced learning, [0155] the data set can be prepared for modeling by assigning to each chip the raw measurement fields… predictor variables … we can use linear as well as non-linear dimensionality reduction on the full dataset. Approaches can include: Auto encoder).
Regarding claim 13, the combination of Honda and Norman teach. The computer system of claim 12, wherein the processor system operations further comprise using a pattern sequence extraction module of the processor system to predict a portion of the process-step sequence having a positive impact on the characteristics of the testable product (Honda, [0087] The basic steps for building a first embodiment of a robust predictive model with rational decision making that is production-worthy is illustrated in FIG. 9A. In step 902, minimum performance criteria for the target feature(s) are obtained for a predictive model (including a machine learning model, pattern recognition model, physics-based Model, hybrid model, etc.). These criteria include but are not limited to training speed, prediction speed, accuracy, false positive rate, false negative rate, recall, precision, F1 score, F2 score, F-N score, root mean square error, root mean square log error, mean absolute error, worst case error, robustness, generalizability, etc., [0076] For example, log data could be parsed using Natural Language Processing (NLP) and Text Mining techniques to convert the log data into features. Temporal data analysis could be transformed using a time series analysis like Autoregressive Integrated Moving Average (ARIMA), Kalman Filter, Particle Filter, etc. to extract relevant features)
Regarding claim 14, the combination of Honda and Norman teach. The computer system of claim 11, wherein: the plurality of process-steps comprises a plurality of semiconductor product fabrication operations (Honda, [0026] Wafer fabrication occurs in step 102, wherein a large number of integrated circuits are formed on a single slice of semiconductor substrate, such as silicon, known as a wafer); the testable product comprises a wafer having dies and completed integrated circuitry ready for testing ([0029] The wafer is diced up into separate individual circuits or dies, and each die that passes through wafer sort and test is bonded to and electrically connected to a frame to form a package. Each die/package is then encapsulated to protect the circuit); the characteristics is selected from the group consisting of wafer yield, die yield, wafer quality, and die quality ([0039] the input data is analyzed using machine learning techniques to identify key features and/or characteristics of the data in order to classify wafers and/or lots of semiconductor devices. In step 206, the wafers or devices are classified based on the analysis of step 204, [0099] the optimal model and relevant effective thresholds are selected based on the consideration of trade-offs from the customer survey. The results of different combinations of limits and thresholds for different parameters can be presented graphically or as data tables in the GUI in order to illustrate for the customer the impact of changes in selection criteria. For example, if returning a false positive for chip has an economic impact that is 100 times more costly than returning a true positive, then model A 1001 on FIG. 10 should be selected and thresholds set that produce 40% true positive and less than 0.5% false positives. However, if returning a true positive is considered 100 times more important than returning a false positive for any rationale, the model B 1002 on FIG. 10 should be selected and thresholds set that produce 30% false positive and greater than 99.5% true positives, [0102] a key question for the customer is how much yield loss is acceptable? Further, would additional visual inspection, triggered by the predictive model, detect equipment issues earlier, such as a few hours, a few days, or few weeks earlier?); encoding the process-step sequence to generate the encoded process-step sequence comprise converting the plurality of process-steps [0155] the data set can be prepared for modeling by assigning to each chip the raw measurement fields… predictor variables … we can use linear as well as non-linear dimensionality reduction on the full dataset. Approaches can include: Auto encoder) … to a plurality of symbols ([0076] log data could be parsed using Natural Language Processing (NLP) and Text Mining techniques to convert the log data into features…data from a “normal” operation condition should be added to the ML model training set such that a “normal” class is one of the possible predictions of the model); the dimensionality reduction operation comprises an embedding operation; and making the prediction of the impact of the one or more portions of the process-step sequence on the characteristics of the testable product generated by the process-step sequence comprises evaluating the plurality of symbols against a process-step sequence language domain ([0155] the data set can be prepared for modeling by assigning to each chip the raw measurement fields… predictor variables … we can use linear as well as non-linear dimensionality reduction on the full dataset. Approaches can include: Auto encoder, [0076] log data could be parsed using Natural Language Processing (NLP) and Text Mining techniques to convert the log data into features…data from a “normal” operation condition should be added to the ML model training set such that a “normal” class is one of the possible predictions of the model).
Norman further teaches and a plurality of queue-times ([0055] the number of substrates of the substrate queue that can be started that the initiating operation within the time period)
Regarding claim 15, Honda teaches A computer program product analyzing a process-step sequence, the computer program product comprising a computer readable program stored on a computer readable storage medium, wherein the computer readable program, when executed on a processor system, causes the processor system to perform processor system operations comprising: accessing a process-step sequence comprising a plurality process-steps…([0025] Fig. 1 …semiconductor manufacturing process 100… voluminous amounts of data that represent various aspects of the process can be collected at every step and sub-step of a production run and provided as input data to various forms of systematic analysis. For example, yield and other performance characteristics may be calculated from selected input data for each step, as well as predictions made for key processing parameters for the entirety of the process); performing a process-step sequence mining operation on the process-step sequence; wherein the plurality of process-steps and the plurality of …times comprise process-steps and … times having unknown dependency relationships with one another ([0055] methods to pre-process data can include but are not limited to the following: (i) time series segmentation (including based on known manufacturing steps or automatic detection of sharp changes in signal measurements, [0113] Regression techniques can also be effective for defining the relationships between dependent variables and independent variables when modeling a target feature)); wherein performing the process-step sequence mining operation comprises: discovering the unknown dependency relationships and generating therefrom predicted dependency relationships ([0113] Regression techniques can also be effective for defining the relationships between dependent variables and independent variables when modeling a target feature. As an example, predicting yield for low-yielding wafers may be identified by the customer as more critical than predicting yield for high-yielding wafers, since correction of the issue causing the low yield has a more significant impact on overall yield and cost. Further, since this issue is usually a function of equipment failure or wear, a model to predict remaining useful life (RUL) for processing equipment can be very useful for the customer and effectively modeled using regression techniques, [0025] yield and other performance characteristics may be calculated from selected input data for each step, as well as predictions made for key processing parameters for the entirety of the process); and making a prediction, taking into account the predicted dependency relationships, of an impact of one or more portions of the process-step sequence on characteristics of a testable product generated by the process-step sequence ([0076] log data could be parsed using Natural Language Processing (NLP) and Text Mining techniques to convert the log data into features…data from a “normal” operation condition should be added to the ML model training set such that a “normal” class is one of the possible predictions of the model, [0087] a robust predictive model with rational decision making that is production-worthy is illustrated in FIG. 9A), [0099] the optimal model and relevant effective thresholds are selected based on the consideration of trade-offs from the customer survey. The results of different combinations of limits and thresholds for different parameters can be presented graphically or as data tables in the GUI in order to illustrate for the customer the impact of changes in selection criteria. For example, if returning a false positive for chip has an economic impact that is 100 times more costly than returning a true positive, then model A 1001 on FIG. 10 should be selected and thresholds set that produce 40% true positive and less than 0.5% false positives. However, if returning a true positive is considered 100 times more important than returning a false positive for any rationale, the model B 1002 on FIG. 10 should be selected and thresholds set that produce 30% false positive and greater than 99.5% true positives, [0085] This description provides a method for guiding a user in rationally selecting predictive models for semiconductor manufacturing applications, [0113] Regression techniques can also be effective for defining the relationships between dependent variables and independent variables when modeling a target feature. As an example, predicting yield for low-yielding wafers may be identified by the customer as more critical than predicting yield for high-yielding wafers, since correction of the issue causing the low yield has a more significant impact on overall yield and cost. Further, since this issue is usually a function of equipment failure or wear, a model to predict remaining useful life (RUL) for processing equipment can be very useful for the customer and effectively modeled using regression techniques); and using the process-step sequence and the prediction to provide instructions to controller components to control how equipment processes product components to generate the testable product ([0085] This description provides a method for guiding a user in rationally selecting predictive models for semiconductor manufacturing applications, [0051] The FDC pipeline 300 can be used as a prognostic/preventive maintenance tool for process equipment by combining temporal trends in wafer prediction with drift and shift data in sensor measurements and ML features); wherein the equipment comprises manufacturing tools and assembly tools, the controller components configured to control operations performed by the manufacturing tools and the assembly tools to execute the process-step sequence ([0106] For example, FIG. 16 illustrates a process 1600 for using the decision-making tool to evaluate equipment issues. After building a model with customer preferences and limits and deploying the same into production at step 1601, an equipment failure issue arises and the first question in step 1602 is whether the failure is catastrophic. If so, then in step 1604 an immediate process shutdown is called for and the problem corrected. If the equipment failure is not catastrophic, then an inspection of the equipment is undertaken in step 1606, [0069] semiconductor tools, [0110] early detection/prediction of catastrophic failure is obviously important for any manufacturing operation, [0033] Standard statistical and process control techniques were used to analyze and utilize the datasets to improve yields and manufacturing efficiencies ).
Honda does not teach and a plurality of queue-times
Norman teaches and a plurality of queue-times ([0055] the number of substrates of the substrate queue that can be started that the initiating operation within the time period, [0021] Based on the additional simulation output, the processing device can identify additional candidate substrates (e.g., from the substrate queue) and can initiate the set of operations to process the number of candidate substrates from the simulation output and the additional candidate substrates over the time period, [0056] a second number of candidate substrates that were successfully processed during each of the simulated set of operations to reach the end of the second time period, [0008] first number of candidate substrates over the first time period)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Honda’s teaching a predictive model for semiconductor processes with Norman’s teaching of the semiconductor processes including the queue times. The combined teaching provides an expected result of a predictive model for semiconductor processes including the queue times. Therefore, one of ordinary skill in the art would be motivated to optimize the prediction model as disclosed by Norman [0004] enable yield enhancement, improvement of process quality control, reduce manufacturing costs and amount of scrap, as well as improve equipment uptime by identifying systematic issues quickly.
Regarding claim 16, the combination of Honda and Norman teach. The computer program product of claim 15, wherein the process-step sequence mining operation comprises: encoding the process-step sequence to generate an encoded process-step sequence having a plurality of encoded process-steps and a plurality of encoded …; and applying, using the processor, a dimensionality reduction operation to the encoded process-step sequence (Honda, [0076] log data could be parsed using Natural Language Processing (NLP) and Text Mining techniques to convert the log data into features…data from a “normal” operation condition should be added to the ML model training set such that a “normal” class is one of the possible predictions of the model, [0155] the data set can be prepared for modeling by… we can use linear as well as non-linear dimensionality reduction on the full dataset. Approaches can include: Auto encoder)
Norman further teaches and a plurality of … queue-times ([0055] the number of substrates of the substrate queue that can be started that the initiating operation within the time period)
Regarding claim 17, the combination of Honda and Norman teach The computer program product of claim 16, wherein: applying the dimensionality reduction operation to the encoded process-step sequence generates a reduced-dimension encoded process-step sequence; the process-step sequence mining operation further comprises applying the reduced-dimension encoded process-step sequence to a predictive model operable to perform a task (Honda, [0076] log data could be parsed using Natural Language Processing (NLP) and Text Mining techniques to convert the log data into features…data from a “normal” operation condition should be added to the ML model training set such that a “normal” class is one of the possible predictions of the model, [0155] the data set can be prepared for modeling by assigning to each chip the raw measurement fields… predictor variables … we can use linear as well as non-linear dimensionality reduction on the full dataset. Approaches can include: Auto encoder) ; and the task comprises the prediction of the impact of the one or more portions of the process-step sequence on the characteristics of the testable product generated by the process-step sequence ([0076] log data could be parsed using Natural Language Processing (NLP) and Text Mining techniques to convert the log data into features…data from a “normal” operation condition should be added to the ML model training set such that a “normal” class is one of the possible predictions of the model, [0155] the data set can be prepared for modeling by assigning to each chip the raw measurement fields… predictor variables … we can use linear as well as non-linear dimensionality reduction on the full dataset. Approaches can include: Auto encoder).
Regarding claim 18, the combination of Honda and Norman teach The computer program product of claim 17, wherein making the prediction of the impact of the one or more portions of the process-step sequence on the characteristics of the testable product comprises: performing a first comparison of the reduced-dimension encoded process-step sequence with a first cluster associated with a first measurement range of the characteristics; performing a second comparison of the reduced-dimension encoded process-step sequence to a second cluster associated with a second measurement range of the characteristics; and associating the reduced-dimension encoded process-step sequence with the first cluster or the second cluster based on a result of the first comparison and a result of the second comparison (Honda, [0088] In step 904, a predictive model is generated for predicting relevant target features. If the results from the predictive model meet the minimum performance criteria in step 905, such as False Positive Rate (FPR) of 0.1% and False Negative Rate (FNR) of 1%, the model is saved in step 906 for use in the comparative analysis described below. Other performance criteria could be used, including Area under the Curve (AUC), Area under the Precision-Recall curve, F1 Score, Skip Rate, etc. In step 907, if additional predictive models are needed for an adequate analysis, another predictive model is generated for predicting relevant target features using a different methodology in step 912, and then compared as before to the minimum performance criteria in step 905. It should be noted that different methodologies include different algorithms and mathematical functions including statistical solutions that may be implemented as machine learning models with supervised learning and reinforced learning, [0155] the data set can be prepared for modeling by assigning to each chip the raw measurement fields… predictor variables … we can use linear as well as non-linear dimensionality reduction on the full dataset. Approaches can include: Auto encoder)
Regarding claim 19, the combination of Honda and Norman teach The computer program product of claim 18, wherein the processor system operations further comprise using a pattern sequence extraction module of the processor system to predict a portion of the process-step sequence having a positive impact on the characteristics of the testable product (Honda, [0087] The basic steps for building a first embodiment of a robust predictive model with rational decision making that is production-worthy is illustrated in FIG. 9A. In step 902, minimum performance criteria for the target feature(s) are obtained for a predictive model (including a machine learning model, pattern recognition model, physics-based Model, hybrid model, etc.). These criteria include but are not limited to training speed, prediction speed, accuracy, false positive rate, false negative rate, recall, precision, F1 score, F2 score, F-N score, root mean square error, root mean square log error, mean absolute error, worst case error, robustness, generalizability, etc., [0076] For example, log data could be parsed using Natural Language Processing (NLP) and Text Mining techniques to convert the log data into features. Temporal data analysis could be transformed using a time series analysis like Autoregressive Integrated Moving Average (ARIMA), Kalman Filter, Particle Filter, etc. to extract relevant features)
Regarding claim 20, the combination of Honda and Norman teach The computer program product of claim 17, wherein: the plurality of process-steps comprises a plurality of semiconductor product fabrication operations (Honda, [0026] Wafer fabrication occurs in step 102, wherein a large number of integrated circuits are formed on a single slice of semiconductor substrate, such as silicon, known as a wafer); the testable product comprises a wafer having dies and completed integrated circuitry ready for testing ([0029] The wafer is diced up into separate individual circuits or dies, and each die that passes through wafer sort and test is bonded to and electrically connected to a frame to form a package. Each die/package is then encapsulated to protect the circuit); the characteristics are selected from the group consisting of wafer yield, die yield, wafer quality, and die quality ([0039] the input data is analyzed using machine learning techniques to identify key features and/or characteristics of the data in order to classify wafers and/or lots of semiconductor devices. In step 206, the wafers or devices are classified based on the analysis of step 204, [0099] the optimal model and relevant effective thresholds are selected based on the consideration of trade-offs from the customer survey. The results of different combinations of limits and thresholds for different parameters can be presented graphically or as data tables in the GUI in order to illustrate for the customer the impact of changes in selection criteria. For example, if returning a false positive for chip has an economic impact that is 100 times more costly than returning a true positive, then model A 1001 on FIG. 10 should be selected and thresholds set that produce 40% true positive and less than 0.5% false positives. However, if returning a true positive is considered 100 times more important than returning a false positive for any rationale, the model B 1002 on FIG. 10 should be selected and thresholds set that produce 30% false positive and greater than 99.5% true positives, [0102] a key question for the customer is how much yield loss is acceptable? Further, would additional visual inspection, triggered by the predictive model, detect equipment issues earlier, such as a few hours, a few days, or few weeks earlier?); encoding the process-step sequence to generate the encoded process-step sequence comprise converting the plurality of process-steps … ([0155] the data set can be prepared for modeling by assigning to each chip the raw measurement fields… predictor variables … we can use linear as well as non-linear dimensionality reduction on the full dataset. Approaches can include: Auto encoder) to a plurality of symbols ([0076] log data could be parsed using Natural Language Processing (NLP) and Text Mining techniques to convert the log data into features…data from a “normal” operation condition should be added to the ML model training set such that a “normal” class is one of the possible predictions of the model); the dimensionality reduction operation comprises an embedding operation; and making the prediction of the impact of the one or more portions of the process-step sequence on the characteristics of the testable product generated by the process-step sequence comprises evaluating the plurality of symbols against a process-step sequence language domain ([0155] the data set can be prepared for modeling by assigning to each chip the raw measurement fields… predictor variables … we can use linear as well as non-linear dimensionality reduction on the full dataset. Approaches can include: Auto encoder, [0076] log data could be parsed using Natural Language Processing (NLP) and Text Mining techniques to convert the log data into features…data from a “normal” operation condition should be added to the ML model training set such that a “normal” class is one of the possible predictions of the model).
Norman further teaches and a plurality of queue-times ([0055] the number of substrates of the substrate queue that can be started that the initiating operation within the time period)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Seidel (US11640386) discloses modeling unknown influences (process parameters) for a process event using a processing tool to process workpieces such as a wafer.
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YVONNE T FOLLANSBEE whose telephone number is (571)272-0634. The examiner can normally be reached Monday - Friday 1pm - 9pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Fennema can be reached at (571) 272-2748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format.
For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/YVONNE TRANG FOLLANSBEE/Examiner, Art Unit 2117
/ROBERT E FENNEMA/Supervisory Patent Examiner, Art Unit 2117
Prosecution Insights