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 .
Status of the Application
2. Claim 1-18 have been examined in this application. Claim 19-33 have been canceled. This communication is the first action on the merits.
Drawings
3. The drawings filed on 3/21/24 are acceptable for examination proceedings.
Claim Rejections - 35 USC § 101
4. 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.
5. Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more as fully discussed below.
6. Regarding Independent claim 1:
Step 1: Yes
Claim 1 is drawn to a method for determining a source of an anomaly in a manufacturing process. Therefore claim 1 falls under one of four categories of statutory subject matter (process/method, machines/products/apparatus, manufactures, and compositions of matter).
Step 2A, Prong 1: Yes
Independent claim 1 is directed to a judicially recognized exception of an abstract idea without significantly more.
Claim 1 recites claim limitation of “and (c) determining, based at least on the one or more process parameters and/or the one or more predetermined features, an anomaly index indicative of a likelihood of a candidate processing apparatus being the source of the anomaly” that under their broadest reasonable interpretation, enumerates a mental concept. A human can mentally determine an anomaly index based on received parameter and/or predetermined features.
Thus, these claimed functions are the judicial exceptions that are no more than a mental abstract idea (See MPEP 2106.04(a)(2)(III)).
Step 2A, Prong 2: No
Claim 1 recites additional limitation of “(a) receiving sensor data of one or more process parameters associated with processing apparatuses for carrying out the manufacturing process; (b) receiving measurement data of one or more predetermined features associated with products of the manufacturing process” are forms of insignificant input or output solution activities (i.e., extra solution), such that receiving data are necessary for the use of the judicial exception (See MPEP 2106.05(g)). The combination of these additional elements does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: No
The additional limitation that are a form of insignificant extra-solution activities, do not amount to significantly more than an abstract idea because the court decisions have determined that this additional element as discussed above in step 2A of acquiring and displaying to be well-understood, routine, and conventional when claimed in a merely generic manner for data collecting (i.e., receiving) and data outputting (i.e., displaying) (See MPEP § 2106.05(d)(II) (i: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (See Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) and Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)).
As such, claim 1 is not patent eligible.
7. Dependent claims 2-18:
Step 1: Yes
Claim 2-18 is drawn to a method, and therefore claim 2-17 falls under one of four categories of statutory subject matter (process/method, machines/products/apparatus, manufactures, and compositions of matter).
Step 2A, Prong 1: Yes
Dependent claim 2-17 are directed to a judicially recognized exception of an abstract idea without significantly more.
Claim 5 recites limitation of “upon detection of the anomaly in a target product among the products based on the measurement data, identifying one or more processing apparatuses among the processing apparatuses”;
Claim 6 recites limitation of “determining, for each candidate processing apparatus, at least one first index indicating at least one first degree of likelihood of the candidate processing apparatus being the source of the anomaly in the target product based on the measurement data obtained for a subset of one or more products among a set of products that have traversed the candidate processing apparatus and a first reference set of the measurement data obtained for the remaining products among the set of products”;
Claim 7 recites limitation of “determining, for each candidate processing apparatus, at least one second index indicating at least one second degree of likelihood of the candidate processing apparatus being the source of the anomaly in a target product based on the sensor data for the candidate processing apparatus obtained from performing the corresponding manufacturing process on the target product and a second reference set of the sensor data for the candidate processing apparatus obtained from performing the corresponding manufacturing process on one or more products among the plurality of products that have been processed before the target product by the candidate processing apparatus”;
Claim 9 recites limitation “determining the at least one first index indicating the at least one first degree of likelihood comprises comparing the measurement data obtained for the subset of one or more products among the set of products that have traversed the candidate processing apparatus and the first reference set of the measurement data obtained for the remaining products among the set of products, and wherein determining the at least one second index indicating the at least one second degree of likelihood comprises comparing the sensor data for the candidate processing apparatus obtained from performing the corresponding manufacturing process on the target product and the second reference set of the sensor data”;
Claim 13 recites limitation “wherein determining the at least one second index indicating the at least one second degree of likelihood comprises determining a plurality of second indices respectively indicating the second degrees of likelihood of the candidate processing apparatus being the source of the anomaly in the target product, and wherein determining the at least one first index indicating the at least one first degree of likelihood comprises determining a plurality of first indices respectively indicating the first degrees of likelihood of the candidate processing apparatus being the source of the anomaly in the target product”;
Claim 14 recites limitation “determining a probability density function of the first reference set of the measurement data obtained for the remaining products among the set of products; determining a representative value of the measurement data obtained for the subset of one or more products among the set of products that have traversed the candidate processing apparatus; and determining the at least one first degree of likelihood of the candidate processing apparatus being the source of the anomaly in the target product based on the representative value and the probability density function of the first reference set”;
Claim 15 recites limitation “determining a probability density function of the second reference set of the sensor data for the candidate processing apparatus obtained from performing the corresponding manufacturing process on the one or more products among the plurality of products that have been processed before the target product by the candidate processing apparatus; and determining the at least one second degree of likelihood of the candidate processing apparatus being the source of the anomaly in the target product based on the sensor data for the candidate processing apparatus and the probability density function of the second reference set”;
Claim 18 recites limitation “and selecting a threshold performance for the validated model such that the validated model determines the source of the anomaly within the threshold performance” that under their broadest reasonable interpretation, enumerates a mental concept. A human can mentally perform the above all claimed functions of determining, selecting, identifying etc.
Thus, these claimed functions are the judicial exceptions that are no more than a mental abstract idea (See MPEP 2106.04(a)(2)(III)).
Claim 17 recites limitation of “applying a trained machine learning model to each anomaly score to determine that at least one anomaly score is indicative of a processing apparatus being the source of the anomaly; and (iii) generating, based at least on the one anomaly score, one or more recommendations to correct the source of the anomaly”;
Claim 18 recites limitation of “wherein the trained machine learning model is obtained by: training the model using (1) a first and second subset of the sensor data, (2) a first and second subset of the measurement data, and (3) associating an anomaly score with each of the first and second subsets of the sensor data and/or the measurement data; validating the model on an independent subset of the sensor data and/or the measurement data associated with the processing apparatuses that have been determined to be sources of past anomalies” that under their broadest reasonable interpretation, enumerates a mathematical concept.
As discloses in the specification, machine learning methods implemented as algorithms:
[0148] Many machine learning methods implemented as algorithms are suitable as approaches to perform the methods described herein. Such methods include but are not limited to supervised learning approaches, unsupervised learning approaches, semi-supervised approaches, or any combination thereof.
[0149] Machine learning algorithms may include, without limitation, neural networks (e.g., artificial neural networks (ANN), multi-layer perceptrons (MLP)), support vector machines, k-nearest neighbors, Gaussian mixture model, Gaussian, naive Bayes, decision trees, or radial basis functions (RBF). Linear machine learning algorithms may include without limitation linear regression, logistic regression, naive Bayes classifier, perceptron, or support vector machines (SVMs). Other machine learning algorithms for use with methods according to the disclosure may include without limitation quadratic classifiers, k-nearest neighbor, boosting, decision trees, random forests, neural networks, pattern recognition, Bayesian networks, or Hidden Markov models. Other machine learning algorithms, including improvements or combinations of any of these, commonly used for machine learning, can also be suitable for use with the methods described herein. Any use of a machine learning algorithm in a workflow can also be suitable for use with the methods described herein. The workflow can include, for example, training, testing, validation, cross-validation, nested-cross-validation, feature selection, row compression, data transformation, binning, normalization, standardization, or algorithm selection (Refer to current case PG Pub: 2024/0361759).
Thus, these claimed functions are the judicial exceptions that are no more than an abstract idea processed by a mathematical algorithm (See MPEP 2106.04(a)(2)(I)).
Step 2A, Prong 2: No
Claim 8 recites additional limitation of “outputting an indication of one or more candidate processing apparatuses as the source of the anomaly in the target product based at least on the anomaly index, wherein the anomaly index comprises the at least one first index and/or the at least one second index”;
Claim 15 recites additional limitation of “acquiring the sensor data for the candidate processing apparatus obtained from performing the corresponding manufacturing process on the target product”. The claimed functions of “acquiring the sensor data” are forms of insignificant input or output solution activities (i.e., extra solution), such that receiving data are necessary for the use of the judicial exception (See MPEP 2106.05(g)). The combination of these additional elements does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 16 recites additional limitation of “wherein outputting the indication of the one or more candidate processing apparatuses as the source of the anomaly in the target product comprises: outputting one or more first indices of a selected candidate processing apparatus among the one or more candidate processing apparatuses; outputting a first graph showing the measurement data obtained for the subset of one or more products among the set of products that have traversed the selected candidate processing apparatus and the first reference set of the measurement data obtained for the remaining products among the set of products; outputting one or more second indices of the selected candidate processing apparatus; and outputting a second graph showing the sensor data for the selected candidate processing apparatus obtained from performing the corresponding manufacturing process on the target product and the second reference set of the sensor data for the selected candidate processing apparatus obtained from performing the corresponding manufacturing process on the one or more products among the plurality of products that have been processed before the target product by the selected candidate processing apparatus”;
Claim 17 recites additional limitation of “obtaining an anomaly score for each of the processing apparatuses”.
The above all additional limitation of respective claims are forms of insignificant input or output solution activities (i.e., extra solution), such that receiving (or obtaining, or acquiring) data, and outputting indication are necessary for the use of the judicial exception (See MPEP 2106.05(g)). The combination of these additional elements does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 2-4 and 10-12 recites limitation that includes the type of information or data and does not recite any other limitation other than abstract idea of independent claim 1 on which these claims are depend.
Step 2B: No
The additional limitation that are a form of insignificant extra-solution activities, do not amount to significantly more than an abstract idea because the court decisions have determined that this additional element as discussed above in step 2A of receiving data and operation sequence to be well-understood, routine, and conventional when claimed in a merely generic manner for data collecting and data outputting (See MPEP § 2106.05(d)(II) (i: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (See Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) and Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)).
As such, dependent claim 2-18 are also not patent eligible.
Claim Rejections - 35 USC § 102
8. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
9. Claims 1-5 and 12 are rejected under 35 U.S.C. 102(a) (2) as being anticipated by Cheng (US PG Pub: 2005/0192698).
10. Regarding claim 1, Cheng discloses:
A method for determining a source of an anomaly in a manufacturing process, comprising:(a) receiving sensor data of one or more process parameters associated with processing apparatuses for carrying out the manufacturing process (e.g., The FDC 312 system monitors and classifies tool faults 314 by checking the operational and processing conditions for conformance to pre-defined limits. If the process temperature 314 of the hot plate bake tool 302 becomes an issue, the temperature fault 314 is detected and classified. The FDC system 312 then relays the temperature fault 314 information to the fault detection/classification real-time controller (FDCRTC) 316. The FDCRTC 316 analyzes the temperature fault data 314 and correlates the data to the measured CD metrology data 304 held within the metrology database 306) (it is understood that the operational and processing conditions are measured by different sensors such as temperature sensor in this case) (Para. [0008]);
(b) receiving measurement data of one or more predetermined features associated with products of the manufacturing process (e.g., FIG. 3 is a diagram 300 illustrating an example for applying the above described process and tool control method in a semiconductor photolithography hot plate bake process. The diagram 300 starts with a given product having been processed through a photolithography hot plate bake tool 302. A metrology tool 304 is then used to measure a critical dimension (CD) feature of the processed product. The CD feature measured has been previously characterized via past process/tool modeling and correlations to processing parameters of the hot plate bake tool 302. The measured CD data is then sent to the metrology database 306 for storage and assimilation for review) (Para. [0017]);
and (c) determining, based at least on the one or more process parameters and/or the one or more predetermined features, an anomaly index indicative of a likelihood of a candidate processing apparatus being the source of the anomaly (e.g., A tool fault detection, classification system (FDC) 312 is also used to monitor and classify specific errors and faults for the hot plate bake tool 302 in real time. The FDC 312 system monitors and classifies tool faults 314 by checking the operational and processing conditions for conformance to pre-defined limits. If the process temperature 314 of the hot plate bake tool 302 becomes an issue, the temperature fault 314 is detected and classified. The FDC system 312 then relays the temperature fault 314 information to the fault detection/classification real-time controller (FDCRTC) 316. The FDCRTC 316 analyzes the temperature fault data 314 and correlates the data to the measured CD metrology data 304 held within the metrology database 306) (Para. [0018], also refer to Para. [0015]).
11. Regarding claim 2, Cheng discloses:
The method of claim 1, wherein the manufacturing process comprises one or more manufacturing processes sequentially performed by one or more sets of processing apparatuses, respectively, one manufacturing process being performed by one set of processing apparatuses independently (e.g., For illustrating the present disclosure, the photolithography hot plate bake processing operation is used, but it is understood that the disclosed method is applicable for all processes within the manufacturing operations of a semiconductor facility) (Para. [0022]).
12. Regarding claim 3, Cheng discloses:
The method of claim 2, wherein the sensor data is detected by sensors of each of the processing apparatuses that have performed corresponding manufacturing processes on a plurality of products (e.g., The manufacture of semiconductor integrated circuits (ICs) and devices require the use of many production process steps to define and create the circuit components and circuit layouts of the product device. The numerous process steps require the use of many tools, both production and support related. Semiconductor factories remain competitive by continuously seeking new methods and practices for improving process yields, product yields, quality, reliability and lower production costs. To help accomplish these, tremendous amounts of effort have been focused upon monitoring aspects of the tools' hardware and processes to ensure and maintain stability, repeatability and yields. In-line product measurements are performed as additional checks to verify these efforts) (Para. [0002]).
13. Regarding claim 4, Cheng discloses:
The method of claim 2, wherein the measurement data comprises one or more predetermined features of each of the products after the one or more manufacturing processes have been performed on each of the products (e.g., wherein the product performance features includes predetermined product physical performance features) 9Refer to Claim 9).
14. Regarding claim 5, Cheng discloses:
The method of claim 4, further comprising between (b) and (c), upon detection of the anomaly in a target product among the products based on the measurement data, identifying one or more processing apparatuses among the processing apparatuses that have been traversed by the target product as candidate processing apparatuses (e.g., A tool fault detection, classification system (FDC) 112 may be used to independently monitor and classify specific errors and faults for the process tool 102. The FDC system 112 monitors and classifies tool faults 114 by checking the operational and processing conditions for conformance to pre-defined limits. When certain faults 114 are detected and classified, the FDC system 112 will typically react with pre-determined responses to raise alerts and/or tool, process shutdowns as required and defined by the manufacturing operations) (Para. [0005]).
15. Regarding claim 12, Cheng discloses:
The method of claim 1, wherein the one or more process parameters include at least one of a temperature, a pressure, power, or a flow rate, (e.g., The FDC 312 system monitors and classifies tool faults 314 by checking the operational and processing conditions for conformance to pre-defined limits. If the process temperature 314 of the hot plate bake tool 302 becomes an issue, the temperature fault 314 is detected and classified. The FDC system 312 then relays the temperature fault 314 information to the fault detection/classification real-time controller (FDCRTC) 316. The FDCRTC 316 analyzes the temperature fault data 314 and correlates the data to the measured CD metrology data 304 held within the metrology database 306) (Para. [0018]) and wherein the one or more predetermined features include at least one of a depth, a thickness, length, or a radius of a product (e.g., A metrology tool 304 is then used to measure a critical dimension (CD) feature of the processed product. The CD feature measured has been previously characterized via past process/tool modeling and correlations to processing parameters of the hot plate bake tool 302. The measured CD data is then sent to the metrology database 306 for storage and assimilation for review. The APC system controller 308 then judges the stored CD data to determine if undesired tool, process performance data trends and/or excursion patterns have developed. Run-based algorithms incorporated into the APC 308 may then be used to calculate and feed back processing parameter adjustments 310 for the hot plate bake tool 302, thereby attempting to re-center the measured product CD that is produced by the drifted or shifted hot plate bake tool 302.) (Para. [0017]).
16. Claim 11, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Cheng in view of Iskandar (Pub: 2023/0259585).
17. Regarding claim 11, Cheng teaches the method of claim 1, wherein the manufacturing process comprises one or more semiconductor manufacturing processes (e.g., For illustrating the present disclosure, the photolithography hot plate bake processing operation is used, but it is understood that the disclosed method is applicable for all processes within the manufacturing operations of a semiconductor facility) (Para. [0022]), [wherein the processing apparatuses comprise one or more processing chambers], and wherein the products comprise one or more semiconductor wafers (e.g., A method and system are disclosed for configuring manufacturing tools in a semiconductor manufacturing flow) (Para. [0008]).
Cheng does not specifically teach wherein the processing apparatuses comprise one or more processing chambers.
Iskandar wherein the processing apparatuses comprise one or more processing chambers (e.g., A large number of sensors that provide data about multiple wafers being processed in multiple chambers often requires many human operators.) (Para. [0016]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Cheng and Iskandar before him/her, to modify the teachings of Cheng to include the teaching of Iskandar in order to controlling quality of processing and product yield in systems used in electronic device manufacturing, such as various processing chambers (Para. [0002]).
18. Regarding claim 17, Cheng teaches the method of claim 1 but does not specifically teach wherein the determining in (c) comprises: (i) obtaining an anomaly score for each of the processing apparatuses; (ii) applying a trained machine learning model to each anomaly score to determine that at least one anomaly score is indicative of a processing apparatus being the source of the anomaly; and (iii) generating, based at least on the one anomaly score, one or more recommendations to correct the source of the anomaly.
Iskandar teaches wherein the determining in (c) comprises: (i) obtaining an anomaly score for each of the processing apparatuses (e.g., The method further includes processing the plurality of outlier scores using a detector neural network to generate an anomaly score indicative of a likelihood of an anomaly associated with the manufacturing operation.) (Para. [0011]); (ii) applying a trained machine learning model to each anomaly score to determine that at least one anomaly score is indicative of a processing apparatus being the source of the anomaly (e.g., For every training input 274 in the training dataset, the training engine 272 may cause the neural networks 220 and 250 to generate outputs (predicted anomaly scores for a set of training sensor statistics). The training engine 272 may compare the observed output of the neural networks 220 and 250 with the target training output 276. The resulting error, e.g., the difference between the target training output and the actual output of the neural networks, may be propagated back through the neural networks 220 and 250, and the weights and biases in the neural networks may be adjusted to make the actual outputs closer to the training outputs) (Para. [0031]); and (iii) generating, based at least on the one anomaly score, one or more recommendations to correct the source of the anomaly (e.g., This adjustment may be repeated until the output error for a particular training input 274 satisfies a predetermined condition (e.g., falls below a predetermined value). Subsequently, a different training input 274 may be selected, a new output generated, a new series of adjustments implemented, until the neural networks are trained to an acceptable degree of accuracy.) (Para. [0031]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Cheng and Iskandar before him/her, to modify the teachings of Cheng to include the teaching of Iskandar in order to controlling quality of processing and product yield in systems used in electronic device manufacturing, such as various processing chambers (Para. [0002]).
Allowable Subject Matter
Claim 6-7, 9, and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and when 35 U.S.C 101 abstract idea rejection is overcome.
Claim 8 and 10, 13-16 are also objected due to their direct/indirect dependency over the claim 6-7 and 9, respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Vaid (Pub: 2014/0273299) disclose the systems and methods for fabricating semiconductor devices wherein the systems and methods determine measurements of physical features, dimensions, or other attributes of semiconductor device structures using measurements obtained from different metrology tools in combination with available data and/or information pertaining to the fabrication of those features, dimensions, or other attributes of the semiconductor device structure (Para. [0001]).
Noda (Pub: 2016/0202693) disclose an anomaly diagnosis system diagnosing a state of a machine facility (Para. [0009]).
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/JIGNESHKUMAR C PATEL/Primary Examiner, Art Unit 2116
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