DETAILED ACTION
Claims 1-20 are pending.
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 11/24/25, have been fully considered but are not persuasive, except where noted below.
Applicant’s arguments regarding 35 U.S.C. § 112(b) (page 9) are persuasive and these rejections are withdrawn.
Applicant argues, regarding 35 U.S.C. § 101, that ‘claim 1 is not directed to an abstract idea. The claimed method is not a mental process because the method necessarily involves technological
components-specifically, sensors integrated with an electronics manufacturing system
that processes substrates-that capture data during physical manufacturing processes’ (page 10).
It is respectfully submitted that these generic sensors are merely recited as a source from which data are gathered and are not considered significantly more, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d). Applicant’s argument is therefore not persuasive.
Applicant argues that the claims integrate a judicial exception into a practical application as ‘the practical application of the claims lies in improving the ease of use and efficiency of electronics manufacturing systems by synthesizing recipe and process data at various stages, analyzing process data, identifying anomalies, and providing data to users’ (page 10).
It is respectfully submitted that analyzing data is a mental process, see MPEP 2106.04(a), and providing data to users is not considered significantly more, see MPEP 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d). These features are therefore not considered to integrate the abstract idea into a practical application. Applicant’s argument is therefore not persuasive.
Applicant argues that ‘The Office Action recognizes that Clark does not teach or suggest that "data comprises one or more indications of an anomaly associated with a substrate". (Office Action of 8/22/2025, page 28). Accordingly, Clark does not teach or suggest at least the bolded claims above’ (page 11).
It is respectfully submitted that the office action states ‘Clark fails to clearly specify that data comprises one or more indications of an anomaly associated with a substrate processed by the processing chamber; and recommending additional analysis of the substrate based on the one or more indications of an anomaly’, i.e. Clark does not teach this entire linked group of limitations, including ‘recommending additional analysis of the substrate based on the one or more indications of an anomaly’ that is taught by Kaushal (page 29 of the last office action). Clark clearly does teach the first part of this group, i.e. ‘that data comprises one or more indications of an anomaly associated with a substrate processed by the processing chamber’ because Clark describes that measurement data/steps and metrology data/steps are referred to synonymously to generally mean data measured in accordance with the invention. The data is then processed to detect non-conformities or defects (anomalies), and a future processing step may be affected to take any necessary corrective action to address a substrate found to be out of specification or defective [0079]; and that measurement/metrology data may be captured and then utilized by the control system 522 is discussed herein for providing active interdiction during substrate processing and fabrication in order to provide corrections to the fabrication process to address data indicating that substrate layers and/or features or out of specification or to correct non-conformities or defects (anomalies) that are detected [0171]. Applicant’s argument is therefore not persuasive.
Applicant’s argument regarding claim 17 (page 11) is unpersuasive for the same reasons as for claim 1.
Applicant argues, with regard to claim 6, that ‘the combination of cited references fails to teach or suggest at least "determining that data comprises one or more indications of an anomaly with respect to a substrate" (page 12).
It is respectfully submitted that this limitation is taught by Clark as detailed above with regard to claim 1. Applicant’s argument is therefore not persuasive.
Applicant argues, with regard to claim 6, that Kaushal does not teach "determining that data comprises one or more indications of an anomaly with respect to a substrate" (pages 12-13).
It is respectfully submitted that, while this is moot given the teachings of Clark cited above, Kaushal teaches this limitation because Kaushal describes that tool failure analysis component 160 can further associate diagnostic tests (additional analysis) and/or suggested repairs with observed tool failures [0042]; Tool failure analysis component 160 can normalize spectral data (e.g., measured intensities) to account for measurement error of intensity of spectral lines in different tools and/or chambers included in fabrication tool(s) 110… . At the end of a process run, data from one or more of the spectroscope 120, tool sensors 130, device measurement equipment 140, or classifying equipment 150 can be provided to tool failure analysis component 160, which can aggregate the collected data in a tool process log for the run. A tool process log can correspond to a single semiconductor wafer processed during the run (i.e. ‘with respect to a substrate’), or a batch of semiconductors fabricated during the run. The tool process logs can then be stored for reporting or archival purposes. In an aspect, process data can be provided automatically by tool failure analysis component 160 [0033-0035]. Applicant’s argument is therefore not persuasive.
Applicant’s arguments regarding 35 U.S.C. § 103 that Ahmed, Yoshinaga, Lian and Gwinn fil to cure the alleged deficiencies of Clark (pages 13-14) are moot because, as detailed above and in the current rejection below, Clark teaches all the relevant limitations and is not deficient. Applicant’s argument is therefore not persuasive.
Applicant’s arguments regarding claim 6 and similar claim 19 (page 14) were addressed above and are not persuasive.
Applicant argues, with regard to the rejections of claims 7-9, 11-15, and 20 under 35 U.S.C. § 103, that ‘the combination of cited references fails to teach or suggest "determining that at least one of the second data or the third data comprises one or more indications of an anomaly with respect to a substrate processed by the manufacturing system" (pages 14-16).
It is respectfully submitted that Clark teaches determining that at least one of the second data or the third data comprises one or more indications of an anomaly with respect to a substrate processed by the manufacturing system because Clark describes that measurement data/steps and metrology data/steps are referred to synonymously to generally mean data measured in accordance with the invention. The data is then processed to detect non-conformities or defects (anomalies), and a future processing step may be affected to take any necessary corrective action to address a substrate found to be out of specification or defective [0079]; and that measurement/metrology data may be captured and then utilized by the control system 522 is discussed herein for providing active interdiction during substrate processing and fabrication in order to provide corrections to the fabrication process to address data indicating that substrate layers and/or features or out of specification or to correct non-conformities or defects (anomalies) that are detected [0171]. Applicant’s argument is therefore not persuasive.
Applicant’s arguments regarding the rejection of the dependent claims under 35 U.S.C. § 103 that Ahmed, Pasadyn, Yoshinaga, Nixon and Wright fail to cure the alleged deficiencies of Clark (pages 16-17) are moot because, as detailed above and in the current rejection below, Clark teaches all the relevant limitations and is not deficient. Applicant’s argument is therefore not persuasive.
For at least these reasons, the rejection of the claims is maintained.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to the abstract idea (mental process) of data analysis.
Claim 1 recites a method, i.e. a process, which is a statutory category of invention. The claim recites:
performing analysis indicative of performance of a processing chamber of the electronics manufacturing system based on the first, second, and third data; determining that the second data comprises one or more indications of an anomaly with respect to a substrate processed by the electronics manufacturing system that may be performed in the human mind, or by a human using a pen and paper. Thus the claim recites an abstract idea (mental processes), see MPEP 2106.04(a) that indicates mental processes include concepts performed in the human mind including an observation, evaluation, judgment, and opinion.
This judicial exception is not integrated into a practical application because the additional elements, i.e. a processing device (merely applying the exception with generic computer technology – see MPEP 2106.04(a)(2) III C), one or more sensors of an electronics manufacturing system with a processing chamber… operational data generated responsive to processing one or more substrates by the electronics manufacturing system using the processing recipe (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)), receiving, by a processing device, first data indicative of a processing recipe; receiving, from one or more sensors of an electronics manufacturing system, second data, wherein the second data comprises operational data generated responsive to processing one or more substrates by the electronics manufacturing system using the processing recipe (insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d)) and causing performance of a corrective action in view of the analysis (insignificant extra-solution activity — instructions to apply the exception using a technique recited at a high level of generality, see MPEP 2106.05(f)) does not impose any meaningful limits on practicing the abstract idea. The claim is therefore directed to an abstract idea.
The claims do 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, a processing device (merely applying the exception with generic computer technology – see MPEP 2106.04(a)(2) III C), one or more sensors of an electronics manufacturing system with a processing chamber… operational data generated responsive to processing one or more substrates by the electronics manufacturing system using the processing recipe (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)), receiving, by a processing device, first data indicative of a processing recipe; receiving, from one or more sensors of an electronics manufacturing system, second data, wherein the second data comprises operational data generated responsive to processing one or more substrates by the electronics manufacturing system using the processing recipe (insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d)) and causing performance of a corrective action in view of the analysis (insignificant extra-solution activity — instructions to apply the exception using a technique recited at a high level of generality, see MPEP 2106.05(f)) does not impose any meaningful limits on practicing the abstract idea and are not considered significantly more. Considering the additionally elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Thus the claim is not patent eligible.
Note that processing chambers/runs/recipes/sensors including substrate processing chambers are well-understood, routine and conventional, see for example Mui et al. U.S. Patent Publication No. 20040038139 [0033, 0049-0050, 00061], Lee et al. U.S. Patent Publication No. 20150063405 [0026], Anwar et al. U.S. Patent Publication No. 20160049917 [0019-0022], or Kim et al. U.S. Patent Publication No. 20230264350 and Funk et al. U.S. Patent Publication No. 20050187649 and the references cited below in the rejections under 35 U.S.C. § 102 and § 103. And machine learning is well-understood, routine and conventional, see Brauer U.S. Patent Publication No. 20180075594 [0032] or Kagalwala et al. U.S. Patent No. 11300948 [claim 20].
Claim 2 recites recipe checking (mental process) and equipment constant monitoring (insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d)). Thus this claim recites an abstract idea.
Claim 3 recites operating window analysis, wherein operating window analysis comprises performing a first statistical analysis of data points within a first time window, performing a second statistical analysis of data points within a second time window, and comparing a result of the first statistical analysis to first one or more thresholds and a result of the second statistical analysis to second one or more thresholds, wherein the second time window is of different duration than the first time window (mental/mathematical process). Thus this claim recites an abstract idea.
Claim 4 recites displaying a visual representation of analysis results on a graphical user interface (insignificant extra-solution activity — see MPEP 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)); and generating a code (mental process) identifying anomalous processing chamber behavior (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)). Thus this claim recites an abstract idea.
Claim 5 recites providing the second data as input to a trained machine learning model; and receiving from the trained machine learning model fourth data indicative of anomalous behavior (mental process performed with generic computer technology and a known algorithm – see MPEP 2106.04(a)(2) III C) of the processing chamber (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)). Thus this claim recites an abstract idea.
Claim 6 recites recommending additional analysis of the substrate based on the one or more indications of the anomaly (insignificant extra-solution activity — see MPEP 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)). Thus this claim recites an abstract idea.
Claim 7 recites comparing the first data to a plurality of rules associated with processing recipes, wherein the comparing is performed before the processing recipe is used in processing a substrate; performing one or more tests upon the second data, wherein the one or more tests are performed after a first number of substrates have been processed in the processing chamber using the processing recipe; and performing one or more tests upon the third data, wherein the one or more tests are performed after a second number of substrates have been processed in the processing chamber using the processing recipe, and wherein the second number is greater than the first number (mental process performed at various times and generally linking the use of the judicial exception to a particular technological environment). Thus this claim recites an abstract idea.
Claim 8 recites a method, i.e. a process, which is a statutory category of invention. The claim recites:
performing first analysis on the processing recipe, wherein the analysis comprises comparing the first data to one or more Best Known Methods (BKMs) related to the first data; performing second analysis on the second data; performing third analysis on the third data, wherein the second number is greater than the first number; determining that at least one of the second data or the third data comprises one or more indications of an anomaly with respect to a substrate processed by the electronics manufacturing system that may be performed in the human mind, or by a human using a pen and paper. Thus the claim recites an abstract idea (mental processes), see MPEP 2106.04(a).
This judicial exception is not integrated into a practical application because the additional elements, i.e. a processing device (merely applying the exception with generic computer technology – see MPEP 2106.04(a)(2) III C) and processing runs and one or more sensors of an electronics manufacturing system with a processing chamber… operational data generated responsive to processing one or more substrates by the electronics manufacturing system using the processing recipe (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)), receiving, by a processing device, first data, wherein the first data comprises a processing recipe; receiving, from one or more sensors of an electronics manufacturing system, second data, wherein the second data comprises first operational data generated responsive to processing one or more substrates by the electronics manufacturing system during a first number of processing runs using the processing recipe; receiving, from one or more sensors of the electronics manufacturing system, third data, wherein the third data comprises second operational data generated responsive to processing one or more substrates during a second number of processing runs using the processing recipe (insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d)) and causing performance of a corrective action based on the first analysis, second analysis, and third analysis (insignificant extra-solution activity — instructions to apply the exception using a technique recited at a high level of generality, see MPEP 2106.05(f)) does not impose any meaningful limits on practicing the abstract idea. The claim is therefore directed to an abstract idea.
The claims do 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, a processing device (merely applying the exception with generic computer technology – see MPEP 2106.04(a)(2) III C) and processing runs and one or more sensors of an electronics manufacturing system with a processing chamber… operational data generated responsive to processing one or more substrates by the electronics manufacturing system using the processing recipe (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)), receiving, by a processing device, first data, wherein the first data comprises a processing recipe; receiving, from one or more sensors of an electronics manufacturing system, second data, wherein the second data comprises first operational data generated responsive to processing one or more substrates by the electronics manufacturing system during a first number of processing runs using the processing recipe; receiving, from one or more sensors of the electronics manufacturing system, third data, wherein the third data comprises second operational data generated responsive to processing one or more substrates during a second number of processing runs using the processing recipe (insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d)) and causing performance of a corrective action based on the first analysis, second analysis, and third analysis (insignificant extra-solution activity — instructions to apply the exception using a technique recited at a high level of generality, see MPEP 2106.05(f)) does not impose any meaningful limits on practicing the abstract idea and are not considered significantly more. Considering the additionally elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Thus the claim is not patent eligible.
Claim 9 recites recipe checking (mental process) and equipment constant monitoring (insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d)). Thus this claim recites an abstract idea.
Claim 10 recites operating window analysis, wherein operating window analysis comprises: defining a first window duration; selecting a first plurality of window placements, wherein each of the first plurality of window placements is of the first window duration; determining a first plurality of statistical metrics, wherein each of the first plurality of statistical metrics is associated with data within one of the first plurality of window placements; defining a second window duration; selecting a second plurality of window placements, wherein each of the second plurality of window placements is of the second window duration; determining a second plurality of statistical metrics, wherein each of the second plurality of statistical metrics is associated with data within one of the second plurality of window placements; comparing the first plurality of statistical metrics to a first threshold value; and comparing the second plurality of statistical metrics to a second threshold value (mental/mathematical process). Thus this claim recites an abstract idea.
Claim 11 recites the operational data comprises trace sensor data (merely specifying the type of abstract data), and wherein the second analysis comprises determining whether values of the trace sensor data satisfy a threshold condition (mental process). Thus this claim recites an abstract idea.
Claim 12 recites the first operational data comprises trace sensor data (merely specifying the type of abstract data), and wherein the second operations data comprises one or more statistical metrics associated with the trace sensor data (merely specifying the type of abstract data). Thus this claim recites an abstract idea.
Claim 13 recites providing the third data to a trained machine learning model, wherein the trained machine learning model is configured to detect one or more faults based on operational data (mental process performed with generic computer technology and a known algorithm – see MPEP 2106.04(a)(2) III C); receiving output from the trained machine learning model, wherein performance of the corrective action is based on the output from the trained machine learning model (insignificant extra-solution activity — instructions to apply the exception using a technique recited at a high level of generality, see MPEP 2106.05(f)). Thus this claim recites an abstract idea.
Claim 14 recites the corrective action comprises one or more of: providing an alert to a user (insignificant extra-solution activity — see MPEP 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)); updating a processing recipe (mental process); updating an equipment constant (mental process); scheduling maintenance of manufacturing equipment (mental process); or updating a best known method associated with the first analysis, second analysis, or third analysis (mental process). Thus this claim recites an abstract idea.
Claim 15 recites providing a visualization of the first analysis, the second analysis, or the third analysis via a graphical user interface (GUI) (insignificant extra-solution activity — see MPEP 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)). Thus this claim recites an abstract idea.
Claim 16 recites the GUI further comprises a code, wherein the code may be utilized to direct a second user interface to display a visualization of the first analysis, the second analysis, or the third analysis (insignificant extra-solution activity — see MPEP 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)). Thus this claim recites an abstract idea.
Claim 17 recites a non-transitory machine-readable storage medium, storing instructions which, when executed, cause a processing device to perform operations, i.e. an article of manufacture, which is a statutory category of invention. The claim recites:
performing analysis indicative of performance of the electronics manufacturing system based on the first, second, and third data; determining that the second data comprises one or more indications of an anomaly with respect to a substrate processed by the electronics manufacturing system that may be performed in the human mind, or by a human using a pen and paper. Thus the claim recites an abstract idea (mental processes), see MPEP 2106.04(a) that indicates mental processes include concepts performed in the human mind including an observation, evaluation, judgment, and opinion.
This judicial exception is not integrated into a practical application because the additional elements, i.e. a non-transitory machine-readable storage medium and a processing device (merely applying the exception with generic computer technology – see MPEP 2106.04(a)(2) III C), one or more sensors of an electronics manufacturing system with a processing chamber… operational data generated responsive to processing one or more substrates by the electronics manufacturing system using the processing recipe (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)), receiving, by a processing device, first data indicative of a processing recipe; receiving, from one or more sensors of an electronics manufacturing system, second data, wherein the second data comprises operational data generated responsive to processing one or more substrates by the electronics manufacturing system using the processing recipe (insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d)) and causing performance of a corrective action in view of the analysis (insignificant extra-solution activity — instructions to apply the exception using a technique recited at a high level of generality, see MPEP 2106.05(f)) does not impose any meaningful limits on practicing the abstract idea. The claim is therefore directed to an abstract idea.
The claims do 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, a non-transitory machine-readable storage medium and a processing device (merely applying the exception with generic computer technology – see MPEP 2106.04(a)(2) III C), one or more sensors of an electronics manufacturing system with a processing chamber… operational data generated responsive to processing one or more substrates by the electronics manufacturing system using the processing recipe (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)), receiving, by a processing device, first data indicative of a processing recipe; receiving, from one or more sensors of an electronics manufacturing system, second data, wherein the second data comprises operational data generated responsive to processing one or more substrates by the electronics manufacturing system using the processing recipe (insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d)) and causing performance of a corrective action in view of the analysis (insignificant extra-solution activity — instructions to apply the exception using a technique recited at a high level of generality, see MPEP 2106.05(f)) does not impose any meaningful limits on practicing the abstract idea and are not considered significantly more. Considering the additionally elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Thus the claim is not patent eligible.
Claim 18 recites the corrective action comprises: displaying a visual representation of analysis results on a graphical user interface (insignificant extra-solution activity — see MPEP 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)); and generating a code associated with navigating to the visual representation of analysis results (mental process). Thus this claim recites an abstract idea.
Claim 19 recites recommending additional analysis of the substrate based on the one or more indications of the anomaly (insignificant extra-solution activity — see MPEP 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)). Thus this claim recites an abstract idea.
Claim 20 recites comparing first data to a plurality of rules associated with processing recipes; performing one or more tests upon the second data, wherein the second data is associated with a first number of substrates processed using the processing recipe; and performing one or more tests upon the third data, wherein the third data is associated with a second number of substrates processed using the processing recipe, and wherein the second number is greater than the first number (mental process). Thus this claim recites an abstract idea.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 and 17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Clark et al. U.S. Patent Publication No. 20200006100 (hereinafter Clark).
Regarding claim 1, Clark discloses a method [0086 , 0427— The present embodiments include methods that utilize a common manufacturing platform in which multiple process steps are performed on the common platform within a controlled environment], comprising:
receiving, by a processing device, first data indicative of a processing recipe; receiving, from one or more sensors of an electronics manufacturing system, second data, wherein the second data comprises operational data generated responsive to processing one or more substrates by the electronics manufacturing system using the processing recipe; receiving third data, wherein the third data comprises historical data associated with the processing recipe [0100-0103 — fabrication measurement or metrology data is captured after one or more of the various substrate fabrication processes as shown in FIG. 1. As used herein, the captured data from a workpiece (substrate) is referred to as measurement data or metrology data… “measurement module” will be used but that is not limiting and generally refers to measurement or metrology or sensing tools used to detect and measure attributes of a workpiece that are indicative of the processing of the workpiece; 0106-0113 — systems 200, 300 that incorporate a common platform with multiple processing modules, one or more measurement modules and one or more transfer modules coupled with an active interdiction control system. The systems enhance the yield of functional microelectronic devices produced from semiconductor fabrication (electronics manufacturing system); 0378, Figs. 21 and 26 — an example embodiment 2600 of a self-optimization component in an autonomous biologically based learning system… . As indicated above, self-optimization component functionality is to analyze the current health (e.g., performance) of a manufacturing platform/tool system 1910 and then determine if non-conformities are detected and, based on the results of the current health analysis, diagnose or rank substantially all potential causes for health deterioration of the tool system 1910 and the cause of such non-conformities, and identify a root cause of non-conformities based on learning acquired by autonomous learning system 1960 in order to provide the necessary control of the manufacturing platform to provide corrective processing.; 0391-0397, Fig. 28 — The group of autonomous tools systems 2820.sub.1-2820.sub.K can be controlled by an autonomous biologically based learning tool 1960 which receives (input) and conveys (output) information 1958 to an interface 1930 that facilitates an actor 1990 to interact with the group of autonomous tools system… the autonomous system 1960 can also construct a predictive model of group time-to-failure as a function of assets 1928 of tool group or platform 2800; e.g., group input data, group outputs, group recipes, or group maintenance activities. In an aspect, to determine a group time-to-failure, autonomous learning system 1960 can gather failure data, including time between detected (e.g., through a set of sensor components or inspection systems) failures, associated assets 2850.sub.1-2850.sub.K, outputs 2801-2860K, and maintenance activities for substantially all operation tools in the set of tools 2801-2820K (operational and historical data). (It should be appreciated that as a consequence of prior failure assessments, specific tools (e.g., tool system 2 2820.sub.1 and tool system K 2820.sub.K) in the set of tools (e.g., tools 2820.sub.1-2820.sub.K) in group 2800 can be out of operation.) Collected data can be autonomously analyzed (e.g., through a processing component 1985 in autonomous learning system 1960) to learn a predictive function for time-to-failure as a function of the group assets (e.g., inputs, recipes, . . . ), outputs, and maintenance activities… when a group performance appears degraded, individual performances associated with individual tools can be analyzed];
performing analysis indicative of performance of a processing chamber of the electronics manufacturing system based on the first, second, and third data [0378, Figs. 21 and 26 — an example embodiment 2600 of a self-optimization component in an autonomous biologically based learning system… . As indicated above, self-optimization component functionality is to analyze the current health (e.g., performance) of a manufacturing platform/tool system 1910 and then determine if non-conformities are detected and, based on the results of the current health analysis, diagnose or rank substantially all potential causes for health deterioration of the tool system 1910 and the cause of such non-conformities, and identify a root cause of non-conformities based on learning acquired by autonomous learning system 1960 in order to provide the necessary control of the manufacturing platform to provide corrective processing.; 0391-0397, Fig. 28 — The group of autonomous tools systems 2820.sub.1-2820.sub.K can be controlled by an autonomous biologically based learning tool 1960 which receives (input) and conveys (output) information 1958 to an interface 1930 that facilitates an actor 1990 to interact with the group of autonomous tools system… the autonomous system 1960 can also construct a predictive model of group time-to-failure as a function of assets 1928 of tool group or platform 2800; e.g., group input data, group outputs, group recipes, or group maintenance activities. In an aspect, to determine a group time-to-failure, autonomous learning system 1960 can gather failure data, including time between detected (e.g., through a set of sensor components or inspection systems) failures, associated assets 2850.sub.1-2850.sub.K, outputs 2801-2860K, and maintenance activities for substantially all operation tools in the set of tools 2801-2820K (operational and historical data). (It should be appreciated that as a consequence of prior failure assessments, specific tools (e.g., tool system 2 2820.sub.1 and tool system K 2820.sub.K) in the set of tools (e.g., tools 2820.sub.1-2820.sub.K) in group 2800 can be out of operation.) Collected data can be autonomously analyzed (e.g., through a processing component 1985 in autonomous learning system 1960) to learn a predictive function for time-to-failure as a function of the group assets (e.g., inputs, recipes, . . . ), outputs, and maintenance activities… when a group performance appears degraded, individual performances associated with individual tools can be analyzed; 0116-0125, Fig. 4 — system of platform 400… substrate processing chambers 420a-420d (individual tools/chambers); 0324, Fig. 20 — a deposition chamber in tool system 1910; 0106-0113 — systems 200, 300 that incorporate a common platform with multiple processing modules, one or more measurement modules and one or more transfer modules coupled with an active interdiction control system. The systems enhance the yield of functional microelectronic devices produced from semiconductor fabrication (electronics manufacturing system)];
determining that the second data comprises one or more indications of an anomaly with respect to a substrate processed by the electronics manufacturing system [0079 — measurement data/steps and metrology data/steps are referred to synonymously to generally mean data measured in accordance with the invention. The data is then processed to detect non-conformities or defects (anomalies), and a future processing step may be affected to take any necessary corrective action to address a substrate found to be out of specification or defective; 0171 — measurement/metrology data may be captured and then utilized by the control system 522 is discussed herein for providing active interdiction during substrate processing and fabrication in order to provide corrections to the fabrication process to address data indicating that substrate layers and/or features or out of specification or to correct non-conformities or defects (anomalies) that are detected; 0189 — substrates are processed through a plurality of different processing modules, that may include one or more etch modules and one or more film-forming modules in combination with one or more measurement/metrology modules to provide measurement data utilized by an active interdiction control system for controlling the overall process sequence in correcting non-conformities and defects]; and
causing performance of a corrective action in view of the analysis [0356 — Based on the level of degradation, autonomous learning system 1960 can analyze available data assets 1928 as well as information 1958 to rank the possible faults. In an aspect, in response to an excessive level of non-conformities the autonomous learning system can provide control for corrective processing through the platform. In case of a successful corrective processing as confirmed, for example, by further measurement/metrology and associated data (e.g., data assets and patterns, relationships, and substantially any other type of understanding extracted from such combination) that preceded the corrective processing activities can be retained by autonomous learning system 1960; 0382 — The combination of analytic and predictive techniques can be exploited to facilitate optimization of tool system 1910 via identification of ailing trends in specific assets, or properties, as probed by sensor component 1925, as well as information available in OKM 2610, with suitable corrective measures generated by optimization planner component 2650, and optimization autobots that can reside in component 2140; 0079 — measurement data/steps and metrology data/steps are referred to synonymously to generally mean data measured in accordance with the invention. The data is then processed to detect non-conformities or defects (anomalies), and a future processing step may be affected to take any necessary corrective action to address a substrate found to be out of specification or defective].
Regarding claim 17, Clark discloses a non-transitory machine-readable storage medium, storing instructions which, when executed, cause a processing device to perform operations [0128 — the control system 422 may be implemented as a general purpose computer system that performs a portion or all of the microprocessor based processing steps of the invention in response to a processor executing one or more sequences of one or more instructions contained in a program in memory. Such instructions may be read into the control system memory from another computer readable medium, such as a hard disk or a removable media drive. One or more processors in a multi-processing arrangement may also be employed; 0427 — a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. The term “article of manufacture” as used herein is intended to encompass a computer program accessible from any computer-readable device, carrier, or media.] comprising:
receiving first data indicative of a processing recipe; receiving, from one or more sensors of an electronics manufacturing system, second data, wherein the second data comprises operational data generated responsive to processing one or more substrates by the electronics manufacturing system using the processing recipe; receiving third data, wherein the third data comprises historical data associated with the processing recipe [0100-0103 — fabrication measurement or metrology data is captured after one or more of the various substrate fabrication processes as shown in FIG. 1. As used herein, the captured data from a workpiece (substrate) is referred to as measurement data or metrology data… “measurement module” will be used but that is not limiting and generally refers to measurement or metrology or sensing tools used to detect and measure attributes of a workpiece that are indicative of the processing of the workpiece; 0106-0113 — systems 200, 300 that incorporate a common platform with multiple processing modules, one or more measurement modules and one or more transfer modules coupled with an active interdiction control system. The systems enhance the yield of functional microelectronic devices produced from semiconductor fabrication (electronics manufacturing system); 0378, Figs. 21 and 26 — an example embodiment 2600 of a self-optimization component in an autonomous biologically based learning system… . As indicated above, self-optimization component functionality is to analyze the current health (e.g., performance) of a manufacturing platform/tool system 1910 and then determine if non-conformities are detected and, based on the results of the current health analysis, diagnose or rank substantially all potential causes for health deterioration of the tool system 1910 and the cause of such non-conformities, and identify a root cause of non-conformities based on learning acquired by autonomous learning system 1960 in order to provide the necessary control of the manufacturing platform to provide corrective processing.; 0391-0397, Fig. 28 — The group of autonomous tools systems 2820.sub.1-2820.sub.K can be controlled by an autonomous biologically based learning tool 1960 which receives (input) and conveys (output) information 1958 to an interface 1930 that facilitates an actor 1990 to interact with the group of autonomous tools system… the autonomous system 1960 can also construct a predictive model of group time-to-failure as a function of assets 1928 of tool group or platform 2800; e.g., group input data, group outputs, group recipes, or group maintenance activities. In an aspect, to determine a group time-to-failure, autonomous learning system 1960 can gather failure data, including time between detected (e.g., through a set of sensor components or inspection systems) failures, associated assets 2850.sub.1-2850.sub.K, outputs 2801-2860K, and maintenance activities for substantially all operation tools in the set of tools 2801-2820K (operational and historical data). (It should be appreciated that as a consequence of prior failure assessments, specific tools (e.g., tool system 2 2820.sub.1 and tool system K 2820.sub.K) in the set of tools (e.g., tools 2820.sub.1-2820.sub.K) in group 2800 can be out of operation.) Collected data can be autonomously analyzed (e.g., through a processing component 1985 in autonomous learning system 1960) to learn a predictive function for time-to-failure as a function of the group assets (e.g., inputs, recipes, . . . ), outputs, and maintenance activities… when a group performance appears degraded, individual performances associated with individual tools can be analyzed];
performing analysis indicative of performance of the electronics manufacturing system based on the first, second, and third data [0378, Figs. 21 and 26 — an example embodiment 2600 of a self-optimization component in an autonomous biologically based learning system… . As indicated above, self-optimization component functionality is to analyze the current health (e.g., performance) of a manufacturing platform/tool system 1910 and then determine if non-conformities are detected and, based on the results of the current health analysis, diagnose or rank substantially all potential causes for health deterioration of the tool system 1910 and the cause of such non-conformities, and identify a root cause of non-conformities based on learning acquired by autonomous learning system 1960 in order to provide the necessary control of the manufacturing platform to provide corrective processing.; 0391-0397, Fig. 28 — The group of autonomous tools systems 2820.sub.1-2820.sub.K can be controlled by an autonomous biologically based learning tool 1960 which receives (input) and conveys (output) information 1958 to an interface 1930 that facilitates an actor 1990 to interact with the group of autonomous tools system… the autonomous system 1960 can also construct a predictive model of group time-to-failure as a function of assets 1928 of tool group or platform 2800; e.g., group input data, group outputs, group recipes, or group maintenance activities. In an aspect, to determine a group time-to-failure, autonomous learning system 1960 can gather failure data, including time between detected (e.g., through a set of sensor components or inspection systems) failures, associated assets 2850.sub.1-2850.sub.K, outputs 2801-2860K, and maintenance activities for substantially all operation tools in the set of tools 2801-2820K (operational and historical data). (It should be appreciated that as a consequence of prior failure assessments, specific tools (e.g., tool system 2 2820.sub.1 and tool system K 2820.sub.K) in the set of tools (e.g., tools 2820.sub.1-2820.sub.K) in group 2800 can be out of operation.) Collected data can be autonomously analyzed (e.g., through a processing component 1985 in autonomous learning system 1960) to learn a predictive function for time-to-failure as a function of the group assets (e.g., inputs, recipes, . . . ), outputs, and maintenance activities… when a group performance appears degraded, individual performances associated with individual tools can be analyzed; 0116-0125, Fig. 4 — system of platform 400… substrate processing chambers 420a-420d (individual tools/chambers); 0324, Fig. 20 — a deposition chamber in tool system 1910; 0106-0113 — systems 200, 300 that incorporate a common platform with multiple processing modules, one or more measurement modules and one or more transfer modules coupled with an active interdiction control system. The systems enhance the yield of functional microelectronic devices produced from semiconductor fabrication (electronics manufacturing system)];
determining that the second data comprises one or more indications of an anomaly with respect to a substrate processed by the electronics manufacturing system [0079 — measurement data/steps and metrology data/steps are referred to synonymously to generally mean data measured in accordance with the invention. The data is then processed to detect non-conformities or defects (anomalies), and a future processing step may be affected to take any necessary corrective action to address a substrate found to be out of specification or defective; 0171 — measurement/metrology data may be captured and then utilized by the control system 522 is discussed herein for providing active interdiction during substrate processing and fabrication in order to provide corrections to the fabrication process to address data indicating that substrate layers and/or features or out of specification or to correct non-conformities or defects (anomalies) that are detected; 0189 — substrates are processed through a plurality of different processing modules, that may include one or more etch modules and one or more film-forming modules in combination with one or more measurement/metrology modules to provide measurement data utilized by an active interdiction control system for controlling the overall process sequence in correcting non-conformities and defects]; and
causing performance of a corrective action in view of the analysis [0356 — Based on the level of degradation, autonomous learning system 1960 can analyze available data assets 1928 as well as information 1958 to rank the possible faults. In an aspect, in response to an excessive level of non-conformities the autonomous learning system can provide control for corrective processing through the platform. In case of a successful corrective processing as confirmed, for example, by further measurement/metrology and associated data (e.g., data assets and patterns, relationships, and substantially any other type of understanding extracted from such combination) that preceded the corrective processing activities can be retained by autonomous learning system 1960; 0382 — The combination of analytic and predictive techniques can be exploited to facilitate optimization of tool system 1910 via identification of ailing trends in specific assets, or properties, as probed by sensor component 1925, as well as information available in OKM 2610, with suitable corrective measures generated by optimization planner component 2650, and optimization autobots that can reside in component 2140; 0079 — measurement data/steps and metrology data/steps are referred to synonymously to generally mean data measured in accordance with the invention. The data is then processed to detect non-conformities or defects (anomalies), and a future processing step may be affected to take any necessary corrective action to address a substrate found to be out of specification or defective].
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 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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clark in view of Ahmed et al. U.S. Patent Publication No. 20070192056 (hereinafter Ahmed).
Regarding claim 2, Clark teaches all the limitations of the base claims as outlined above.
Further, Clark teaches equipment constant monitoring [0077-0078 — The common platform integrates heterogeneous equipment and processing modules with metrology or measurement modules that monitor substrate fabricator progress].
But Clark fails to clearly specify analysis comprises recipe checking.
However, Ahmed teaches analysis comprises recipe checking [0042, Figs. 2-3 — The recipe verification program may automatically receive the summary or may manually be instructed to retrieve 58 the summary file for the desired recipe. The recipe attributes trigger the program 30 to select the proper BKM 60 from the categorized BKM database and compare the recipe and BKM and display 64 the parameters, critical parameters, mismatches or other parameter subset].
Clark and Ahmed are analogous art. They relate to semiconductor manufacturing systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Clark, by incorporating the above limitations, as taught by Ahmed.
One of ordinary skill in the art would have been motivated to do this modification to facilitate recipes that are verified and run well and maintain consistency among recipes, as suggested by Ahmed [0010-0011].
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clark in view of Yoshinaga U.S. Patent Publication No. 20220334030 (hereinafter Yoshinaga).
Regarding claim 3, Clark teaches all the limitations of the base claims as outlined above.
But Clark fails to clearly specify operating window analysis, wherein operating window analysis comprises performing a first statistical analysis of data points within a first time window, performing a second statistical analysis of data points within a second time window, and comparing a result of the first statistical analysis to first one or more thresholds and a result of the second statistical analysis to second one or more thresholds, wherein the second time window is of different duration than the first time window.
However, Yoshinaga teaches operating window analysis, wherein operating window analysis comprises performing a first statistical analysis of data points within a first time window, performing a second statistical analysis of data points within a second time window, and comparing a result of the first statistical analysis to first one or more thresholds and a result of the second statistical analysis to second one or more thresholds, wherein the second time window is of different duration than the first time window [0002, 0036 — A time series data processing apparatus 10 according to the present invention is connected to a measurement target P such as a plant.; 0005, 0042-0049, 0055, 0067, Figs. 1-3, 5-6, 11 and 13 — a normal period (first/second window) in which a monitoring target is actually in a normal state and an anomalous period (first/second window) in which the monitoring target is in an anomalous state are set on the anomaly degree graph D2. Then, from the anomalous period on the anomaly degree graph D2, as shown in FIG. 2, a plurality of candidates for the threshold value can be considered, such as (threshold value A1) “a case where a degree of anomaly exceeds 30 even for a moment”, (threshold value A2) “; 0052-0057, Fig. 11 —the maximum value of the degree of anomaly is “15” as shown in FIG. 11A. Consequently, [3, 15] is extracted as the maximum coverage value that is the combination of “duration, anomaly degree”.].
Clark and Yoshinaga are analogous art. They relate to manufacturing systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Clark, by incorporating the above limitations, as taught by Yoshinaga.
One of ordinary skill in the art would have been motivated to do this modification to improve the detection of anomalies, particularly with regard to setting threshold, as suggested by Yoshinaga [0006-0007]. In addition, it would be obvious to utilize normal and abnormal time windows to identify when anomalies are taking place and to take advantage of the longest possible time windows, that may be different than shorter time windows, to obtain the best statistical data and hence the best accuracy.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clark in view of Lian et al. U.S. Patent Publication No. 20150241272 (hereinafter Lian).
Regarding claim 4, Clark teaches all the limitations of the base claims as outlined above.
Further, Clark teaches displaying a visual representation of analysis results on a graphical user interface [0126 — The controller 422 collects, provides, processes, stores, and displays data from any or all of the processing modules and tool components. The control system 422, as described further herein, can comprise a number of different programs and applications and processing engines to analyze the measured data and in-situ processing data and to implement algorithms, such as deep learning networks, machine learning algorithms, autonomous learning algorithms and other algorithms for providing the active interdiction of the invention; 0206 — Computer 1210 may also include a display as part of the HMI for providing visual output to an operator].
But Clark fails to clearly specify generating a code identifying anomalous processing chamber behavior.
However, Lian teaches generating a code identifying anomalous processing chamber behavior [0022-0029, 0032-0037, Figs. 1 and 4 — At 402 the light emitted by the pulsed plasma in the plasma process chamber is received at the detector… At 408 optionally, the mean waveform is analyzed for fault events and if any fault event is determined, then a fault code is transmitted to the chamber control tool].
Clark and Lian are analogous art. They relate to semiconductor manufacturing systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Clark, by incorporating the above limitations, as taught by Lian.
One of ordinary skill in the art would have been motivated to do this modification to identify specific types of faults thus facilitating correcting them, as suggested by Lian [0022-0029, 0032-0037].
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clark in view of Gwinn U.S. Patent Publication No. 20220351997 (hereinafter Gwinn).
Regarding claim 5, Clark teaches all the limitations of the base claims as outlined above.
Further, Clark teaches the second data [0378, Figs. 21 and 26 — an example embodiment 2600 of a self-optimization component in an autonomous biologically based learning system… . As indicated above, self-optimization component functionality is to analyze the current health (e.g., performance) of a manufacturing platform/tool system 1910 and then determine if non-conformities are detected and, based on the results of the current health analysis, diagnose or rank substantially all potential causes for health deterioration of the tool system 1910 and the cause of such non-conformities, and identify a root cause of non-conformities based on learning acquired by autonomous learning system 1960 in order to provide the necessary control of the manufacturing platform to provide corrective processing.; 0391-0397, Fig. 28 — The group of autonomous tools systems 2820.sub.1-2820.sub.K can be controlled by an autonomous biologically based learning tool 1960 which receives (input) and conveys (output) information 1958 to an interface 1930 that facilitates an actor 1990 to interact with the group of autonomous tools system… the autonomous system 1960 can also construct a predictive model of group time-to-failure as a function of assets 1928 of tool group or platform 2800; e.g., group input data, group outputs, group recipes, or group maintenance activities. In an aspect, to determine a group time-to-failure, autonomous learning system 1960 can gather failure data, including time between detected (e.g., through a set of sensor components or inspection systems) failures, associated assets 2850.sub.1-2850.sub.K, outputs 2801-2860K, and maintenance activities for substantially all operation tools in the set of tools 2801-2820K (operational and historical data). (It should be appreciated that as a consequence of prior failure assessments, specific tools (e.g., tool system 2 2820.sub.1 and tool system K 2820.sub.K) in the set of tools (e.g., tools 2820.sub.1-2820.sub.K) in group 2800 can be out of operation.) Collected data can be autonomously analyzed (e.g., through a processing component 1985 in autonomous learning system 1960) to learn a predictive function for time-to-failure as a function of the group assets (e.g., inputs, recipes, . . . ), outputs, and maintenance activities… when a group performance appears degraded, individual performances associated with individual tools can be analyzed].
But Clark fails to clearly specify providing the data as input to a trained machine learning model; and receiving from the trained machine learning model fourth data indicative of anomalous behavior of the processing chamber.
However, Gwinn teaches providing the data as input to a trained machine learning model; and receiving from the trained machine learning model fourth data indicative of anomalous behavior of the processing chamber [0020 —the semiconductor prediction system 100 may include a semiconductor processing tool 120 that is coupled to an AI based tool such as a machine learning (ML) system 130; 0078, Fig. 5 — the first neural network may be coupled to the semiconductor processing tool controller no and may be used to generate an output indicative of a fault of the tool (block 508); 0093 — the ML system may be able to detect a fault caused by an annealing chamber after a wafer exits a deposition chamber… system 130 which may halt processing of wafers when it predicts a upcoming failure and prevent wafer scrap].
Clark and Gwinn are analogous art. They relate to semiconductor manufacturing systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Clark, by incorporating the above limitations, as taught by Gwinn.
One of ordinary skill in the art would have been motivated to do this modification to predict failures and prevent scrap, as suggested by Gwinn [0093].
Claim(s) 6 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clark in view of Kaushal et al. U.S. Patent Publication No. 20170023927 (hereinafter Kaushal).
Regarding claim 6, Clark teaches all the limitations of the base claims as outlined above.
Further, Clark teaches the second data [0378, Figs. 21 and 26 — an example embodiment 2600 of a self-optimization component in an autonomous biologically based learning system… . As indicated above, self-optimization component functionality is to analyze the current health (e.g., performance) of a manufacturing platform/tool system 1910 and then determine if non-conformities are detected and, based on the results of the current health analysis, diagnose or rank substantially all potential causes for health deterioration of the tool system 1910 and the cause of such non-conformities, and identify a root cause of non-conformities based on learning acquired by autonomous learning system 1960 in order to provide the necessary control of the manufacturing platform to provide corrective processing.; 0391-0397, Fig. 28 — The group of autonomous tools systems 2820.sub.1-2820.sub.K can be controlled by an autonomous biologically based learning tool 1960 which receives (input) and conveys (output) information 1958 to an interface 1930 that facilitates an actor 1990 to interact with the group of autonomous tools system… the autonomous system 1960 can also construct a predictive model of group time-to-failure as a function of assets 1928 of tool group or platform 2800; e.g., group input data, group outputs, group recipes, or group maintenance activities. In an aspect, to determine a group time-to-failure, autonomous learning system 1960 can gather failure data, including time between detected (e.g., through a set of sensor components or inspection systems) failures, associated assets 2850.sub.1-2850.sub.K, outputs 2801-2860K, and maintenance activities for substantially all operation tools in the set of tools 2801-2820K (operational and historical data). (It should be appreciated that as a consequence of prior failure assessments, specific tools (e.g., tool system 2 2820.sub.1 and tool system K 2820.sub.K) in the set of tools (e.g., tools 2820.sub.1-2820.sub.K) in group 2800 can be out of operation.) Collected data can be autonomously analyzed (e.g., through a processing component 1985 in autonomous learning system 1960) to learn a predictive function for time-to-failure as a function of the group assets (e.g., inputs, recipes, . . . ), outputs, and maintenance activities… when a group performance appears degraded, individual performances associated with individual tools can be analyzed].
But Clark fails to clearly specify recommending additional analysis of the substrate based on the one or more indications of the anomaly.
However, Kaushal teaches recommending additional analysis of the substrate based on the one or more indications of an anomaly [0042 — The tool failure analysis component 160 can further associate diagnostic tests (additional analysis) and/or suggested repairs with observed tool failures.; 0033-0035 — Tool failure analysis component 160 can normalize spectral data (e.g., measured intensities) to account for measurement error of intensity of spectral lines in different tools and/or chambers included in fabrication tool(s) 110… . At the end of a process run, data from one or more of the spectroscope 120, tool sensors 130, device measurement equipment 140, or classifying equipment 150 can be provided to tool failure analysis component 160, which can aggregate the collected data in a tool process log for the run. A tool process log can correspond to a single semiconductor wafer processed during the run, or a batch of semiconductors fabricated during the run. The tool process logs can then be stored for reporting or archival purposes. In an aspect, process data can be provided automatically by tool failure analysis component 160 ].
Clark and Kaushal are analogous art. They relate to semiconductor manufacturing systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Clark, by incorporating the above limitations, as taught by Kaushal.
One of ordinary skill in the art would have been motivated to do this modification to facilitate more easily diagnosing a processing system failure, as suggested by Kaushal [0025-0026, 0042].
Regarding claim 19, Clark teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 6.
Claim(s) 7-9, 11-15 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clark in view of Ahmed and further in view of Pasadyn et al. U.S. Patent No. 6708129 (hereinafter Pasadyn).
Regarding claim 7, Clark teaches all the limitations of the base claims as outlined above.
Further, Clark teaches performing analysis indicative of performance of the processing chamber based on the first, second, and third data [0378, Figs. 21 and 26 — an example embodiment 2600 of a self-optimization component in an autonomous biologically based learning system… . As indicated above, self-optimization component functionality is to analyze the current health (e.g., performance) of a manufacturing platform/tool system 1910 and then determine if non-conformities are detected and, based on the results of the current health analysis, diagnose or rank substantially all potential causes for health deterioration of the tool system 1910 and the cause of such non-conformities, and identify a root cause of non-conformities based on learning acquired by autonomous learning system 1960 in order to provide the necessary control of the manufacturing platform to provide corrective processing.; 0391-0397, Fig. 28 — The group of autonomous tools systems 2820.sub.1-2820.sub.K can be controlled by an autonomous biologically based learning tool 1960 which receives (input) and conveys (output) information 1958 to an interface 1930 that facilitates an actor 1990 to interact with the group of autonomous tools system… the autonomous system 1960 can also construct a predictive model of group time-to-failure as a function of assets 1928 of tool group or platform 2800; e.g., group input data, group outputs, group recipes, or group maintenance activities. In an aspect, to determine a group time-to-failure, autonomous learning system 1960 can gather failure data, including time between detected (e.g., through a set of sensor components or inspection systems) failures, associated assets 2850.sub.1-2850.sub.K, outputs 2801-2860K, and maintenance activities for substantially all operation tools in the set of tools 2801-2820K (operational and historical data). (It should be appreciated that as a consequence of prior failure assessments, specific tools (e.g., tool system 2 2820.sub.1 and tool system K 2820.sub.K) in the set of tools (e.g., tools 2820.sub.1-2820.sub.K) in group 2800 can be out of operation.) Collected data can be autonomously analyzed (e.g., through a processing component 1985 in autonomous learning system 1960) to learn a predictive function for time-to-failure as a function of the group assets (e.g., inputs, recipes, . . . ), outputs, and maintenance activities… when a group performance appears degraded, individual performances associated with individual tools can be analyzed; 0116-0125, Fig. 4 — system of platform 400… substrate processing chambers 420a-420d (individual tools/chambers); 0324, Fig. 20 — a deposition chamber in tool system 1910] and processing substrates using recipes [0124-0130 — the control system 422 may be utilized to activate the inputs to the various processing systems and transfer systems according to a process recipe… substrate processing system implanted on platform 500].
But Clark fails to clearly specify comparing the first data to a plurality of rules associated with processing recipes, wherein the comparing is performed before the processing recipe is used in processing a substrate; performing one or more tests upon the second data, wherein the one or more tests are performed after a first number of substrates have been processed in the processing chamber using the processing recipe; and performing one or more tests upon the third data, wherein the one or more tests are performed after a second number of substrates have been processed in the processing chamber using the processing recipe, and wherein the second number is greater than the first number.
However, Ahmed teaches comparing the first data to a plurality of rules associated with processing recipes, wherein the comparing is performed before the processing recipe is used in processing a substrate [0042, Figs. 2-3 — The recipe verification program may automatically receive the summary or may manually be instructed to retrieve 58 the summary file for the desired recipe. The recipe attributes trigger the program 30 to select the proper BKM 60 from the categorized BKM database and compare the recipe and BKM and display 64 the parameters, critical parameters, mismatches or other parameter subset].
Clark and Ahmed are analogous art. They relate to semiconductor manufacturing systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Clark, by incorporating the above limitations, as taught by Ahmed.
One of ordinary skill in the art would have been motivated to do this modification to facilitate recipes that are verified and run well and maintain consistency among recipes, as suggested by Ahmed [0010-0011].
But the combination of Clark and Ahmed fails to clearly specify performing one or more tests upon the second data, wherein the one or more tests are performed after a first number of substrates have been processed in the processing chamber using the processing recipe; and performing one or more tests upon the third data, wherein the one or more tests are performed after a second number of substrates have been processed in the processing chamber using the processing recipe, and wherein the second number is greater than the first number.
However, Pasadyn teaches performing one or more tests upon the second data, wherein the one or more tests are performed after a first number of substrates have been processed in the processing chamber using the processing recipe; and performing one or more tests upon the third data, wherein the one or more tests are performed after a second number of substrates have been processed in the processing chamber using the processing recipe, and wherein the second number is greater than the first number [col. 7 lines 26-33 — computer system 530 employs a manufacturing model 540 to generate control input signals on the line 523. In one embodiment, the manufacturing model 540 contains a manufacturing recipe that determines a plurality of control input parameters that are sent on the line 523 to the processing tools 510a, 510b; col. 10 line 49 – col. 11 line 54 — processing tool 510 also informs the system 300 of the number of sites examined on the sample wafers 105 (block 920). Generally, the higher the number of sites on a particular sample wafer 105 that are analyzed, the more accurate and reliable the metrology data. In certain situations, the system 300 may decide that a smaller number of sites can be analyzed by the integrated metrology tool 310 due to the time constraints because of the increased number of wafers 105 that are sampled. Generally, the smaller the sampling rate (i.e., the number of wafers 105 analyzed by the integrated metrology tool 310), the larger the desired site number. The system 300 can use the number of sites as an indication of the reliability of the metrology data. The sampling rate and the number of sites are used by the system 300 to determine the weight of the metrology data. For example, when the sampling rate is 67% (e.g., analyzing every two of three wafers that are processed) relatively high and a large number of sites are examined, the weight of the metrology data is higher than when the sampling rated is 50% and a smaller number of sites are examined — Performing tests/metrology on groups of wafers that comprise different numbers of wafers — tests/metrology are performed after the wafers are processed (Figs. 7 and 9); col. 9 lines 27-42, Fig. 7 — While manufacturing the semiconductor wafers 105, the system 300 performs a partial measurement data acquisition process (block 720). A more detailed illustration, and accompanying description of performing the partial measurement data acquisition process indicated in block 720 are provided below. Once the system 300 performs a partial measurement data acquisition process, the system 300 uses the partial measurement data to calculate feedback and/or feed-forward adjustments (block 730).].
Clark, Ahmed and Pasadyn are analogous art. They relate to semiconductor manufacturing systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Clark and Ahmed, by incorporating the above limitations, as taught by Pasadyn.
One of ordinary skill in the art would have been motivated to do this modification in order to continuously manufacture devices and account for processes change while adjusting the number of sampled wafers to maintain accuracy and reliability, as suggested by the teachings of Pasadyn [col. 10 line 49 – col. 11 line 54].
Regarding claim 8, Clark teaches a method [0086 , 0427— The present embodiments include methods that utilize a common manufacturing platform in which multiple process steps are performed on the common platform within a controlled environment], comprising:
receiving, by a processing device, first data, wherein the first data comprises a processing recipe [0378, Figs. 21 and 26 — an example embodiment 2600 of a self-optimization component in an autonomous biologically based learning system… . As indicated above, self-optimization component functionality is to analyze the current health (e.g., performance) of a manufacturing platform/tool system 1910 and then determine if non-conformities are detected and, based on the results of the current health analysis, diagnose or rank substantially all potential causes for health deterioration of the tool system 1910 and the cause of such non-conformities, and identify a root cause of non-conformities based on learning acquired by autonomous learning system 1960 in order to provide the necessary control of the manufacturing platform to provide corrective processing.; 0391-0397, Fig. 28 — The group of autonomous tools systems 2820.sub.1-2820.sub.K can be controlled by an autonomous biologically based learning tool 1960 which receives (input) and conveys (output) information 1958 to an interface 1930 that facilitates an actor 1990 to interact with the group of autonomous tools system… the autonomous system 1960 can also construct a predictive model of group time-to-failure as a function of assets 1928 of tool group or platform 2800; e.g., group input data, group outputs, group recipes, or group maintenance activities. In an aspect, to determine a group time-to-failure, autonomous learning system 1960 can gather failure data, including time between detected (e.g., through a set of sensor components or inspection systems) failures, associated assets 2850.sub.1-2850.sub.K, outputs 2801-2860K, and maintenance activities for substantially all operation tools in the set of tools 2801-2820K (operational and historical data). (It should be appreciated that as a consequence of prior failure assessments, specific tools (e.g., tool system 2 2820.sub.1 and tool system K 2820.sub.K) in the set of tools (e.g., tools 2820.sub.1-2820.sub.K) in group 2800 can be out of operation.) Collected data can be autonomously analyzed (e.g., through a processing component 1985 in autonomous learning system 1960) to learn a predictive function for time-to-failure as a function of the group assets (e.g., inputs, recipes, . . . ), outputs, and maintenance activities… when a group performance appears degraded, individual performances associated with individual tools can be analyzed]; performing first analysis on the processing recipe [0378, Figs. 21 and 26 — an example embodiment 2600 of a self-optimization component in an autonomous biologically based learning system… . As indicated above, self-optimization component functionality is to analyze the current health (e.g., performance) of a manufacturing platform/tool system 1910 and then determine if non-conformities are detected and, based on the results of the current health analysis, diagnose or rank substantially all potential causes for health deterioration of the tool system 1910 and the cause of such non-conformities, and identify a root cause of non-conformities based on learning acquired by autonomous learning system 1960 in order to provide the necessary control of the manufacturing platform to provide corrective processing.; 0391-0397, Fig. 28 — The group of autonomous tools systems 2820.sub.1-2820.sub.K can be controlled by an autonomous biologically based learning tool 1960 which receives (input) and conveys (output) information 1958 to an interface 1930 that facilitates an actor 1990 to interact with the group of autonomous tools system… the autonomous system 1960 can also construct a predictive model of group time-to-failure as a function of assets 1928 of tool group or platform 2800; e.g., group input data, group outputs, group recipes, or group maintenance activities. In an aspect, to determine a group time-to-failure, autonomous learning system 1960 can gather failure data, including time between detected (e.g., through a set of sensor components or inspection systems) failures, associated assets 2850.sub.1-2850.sub.K, outputs 2801-2860K, and maintenance activities for substantially all operation tools in the set of tools 2801-2820K (operational and historical data). (It should be appreciated that as a consequence of prior failure assessments, specific tools (e.g., tool system 2 2820.sub.1 and tool system K 2820.sub.K) in the set of tools (e.g., tools 2820.sub.1-2820.sub.K) in group 2800 can be out of operation.) Collected data can be autonomously analyzed (e.g., through a processing component 1985 in autonomous learning system 1960) to learn a predictive function for time-to-failure as a function of the group assets (e.g., inputs, recipes, . . . ), outputs, and maintenance activities… when a group performance appears degraded, individual performances associated with individual tools can be analyzed; 0116-0125, Fig. 4 — system of platform 400… substrate processing chambers 420a-420d (individual tools/chambers); 0324, Fig. 20 — a deposition chamber in tool system 1910];
receiving, from one or more sensors of an electronics manufacturing system, second data, wherein the second data comprises first operational data generated responsive to processing one or more substrates by the electronics manufacturing system during a first number of processing runs using the processing recipe; performing second analysis on the second data; receiving, from one or more sensors of the electronics manufacturing system, third data, wherein the third data comprises second operational data generated responsive to processing one or more substrates during a second number of processing runs using the processing recipe; performing third analysis on the third data [0100-0103 — fabrication measurement or metrology data is captured after one or more of the various substrate fabrication processes as shown in FIG. 1. As used herein, the captured data from a workpiece (substrate) is referred to as measurement data or metrology data… “measurement module” will be used but that is not limiting and generally refers to measurement or metrology or sensing tools used to detect and measure attributes of a workpiece that are indicative of the processing of the workpiece; 0106-0113 — systems 200, 300 that incorporate a common platform with multiple processing modules, one or more measurement modules and one or more transfer modules coupled with an active interdiction control system. The systems enhance the yield of functional microelectronic devices produced from semiconductor fabrication (electronics manufacturing system); 0378, Figs. 21 and 26 — an example embodiment 2600 of a self-optimization component in an autonomous biologically based learning system… . As indicated above, self-optimization component functionality is to analyze the current health (e.g., performance) of a manufacturing platform/tool system 1910 and then determine if non-conformities are detected and, based on the results of the current health analysis, diagnose or rank substantially all potential causes for health deterioration of the tool system 1910 and the cause of such non-conformities, and identify a root cause of non-conformities based on learning acquired by autonomous learning system 1960 in order to provide the necessary control of the manufacturing platform to provide corrective processing.; 0391-0397, Fig. 28 — The group of autonomous tools systems 2820.sub.1-2820.sub.K can be controlled by an autonomous biologically based learning tool 1960 which receives (input) and conveys (output) information 1958 to an interface 1930 that facilitates an actor 1990 to interact with the group of autonomous tools system… the autonomous system 1960 can also construct a predictive model of group time-to-failure as a function of assets 1928 of tool group or platform 2800; e.g., group input data, group outputs, group recipes, or group maintenance activities. In an aspect, to determine a group time-to-failure, autonomous learning system 1960 can gather failure data, including time between detected (e.g., through a set of sensor components or inspection systems) failures, associated assets 2850.sub.1-2850.sub.K, outputs 2801-2860K, and maintenance activities for substantially all operation tools in the set of tools 2801-2820K (operational and historical data). (It should be appreciated that as a consequence of prior failure assessments, specific tools (e.g., tool system 2 2820.sub.1 and tool system K 2820.sub.K) in the set of tools (e.g., tools 2820.sub.1-2820.sub.K) in group 2800 can be out of operation.) Collected data can be autonomously analyzed (e.g., through a processing component 1985 in autonomous learning system 1960) to learn a predictive function for time-to-failure as a function of the group assets (e.g., inputs, recipes, . . . ), outputs, and maintenance activities… when a group performance appears degraded, individual performances associated with individual tools can be analyzed; 0356 — the autonomous learning system can provide control for corrective processing through the platform. In case of a successful corrective processing as confirmed, for example, by further measurement/metrology and associated data (e.g., data assets and patterns, relationships, and substantially any other type of understanding extracted from such combination; 0124-0130 — the control system 422 may be utilized to activate the inputs to the various processing systems and transfer systems according to a process recipe… substrate processing system implanted on platform 500; 0106-0113 — systems 200, 300 that incorporate a common platform with multiple processing modules, one or more measurement modules and one or more transfer modules coupled with an active interdiction control system. The systems enhance the yield of functional microelectronic devices produced from semiconductor fabrication (electronics manufacturing system)];
determining that at least one of the second data or the third data comprises one or more indications of an anomaly with respect to a substrate processed by the electronics manufacturing system [0079 — measurement data/steps and metrology data/steps are referred to synonymously to generally mean data measured in accordance with the invention. The data is then processed to detect non-conformities or defects (anomalies), and a future processing step may be affected to take any necessary corrective action to address a substrate found to be out of specification or defective; 0171 — measurement/metrology data may be captured and then utilized by the control system 522 is discussed herein for providing active interdiction during substrate processing and fabrication in order to provide corrections to the fabrication process to address data indicating that substrate layers and/or features or out of specification or to correct non-conformities or defects (anomalies) that are detected; 0189 — substrates are processed through a plurality of different processing modules, that may include one or more etch modules and one or more film-forming modules in combination with one or more measurement/metrology modules to provide measurement data utilized by an active interdiction control system for controlling the overall process sequence in correcting non-conformities and defects]; and
causing performance of a corrective action based on the first analysis, second analysis, and third analysis [0356 — Based on the level of degradation, autonomous learning system 1960 can analyze available data assets 1928 as well as information 1958 to rank the possible faults. In an aspect, in response to an excessive level of non-conformities the autonomous learning system can provide control for corrective processing through the platform. In case of a successful corrective processing as confirmed, for example, by further measurement/metrology and associated data (e.g., data assets and patterns, relationships, and substantially any other type of understanding extracted from such combination) that preceded the corrective processing activities can be retained by autonomous learning system 1960; 0382 — The combination of analytic and predictive techniques can be exploited to facilitate optimization of tool system 1910 via identification of ailing trends in specific assets, or properties, as probed by sensor component 1925, as well as information available in OKM 2610, with suitable corrective measures generated by optimization planner component 2650, and optimization autobots that can reside in component 2140; 0079 — measurement data/steps and metrology data/steps are referred to synonymously to generally mean data measured in accordance with the invention. The data is then processed to detect non-conformities or defects (anomalies), and a future processing step may be affected to take any necessary corrective action to address a substrate found to be out of specification or defective].
But Clark fails to clearly specify the analysis comprises comparing the first data to one or more Best Known Methods (BKMs) related to the first data; and operational data generated from a first number of processing runs using the processing recipe; second operational data generated from a second number of processing runs using the processing recipe; wherein the second number is greater than the first number.
However, Ahmed teaches comparing the first data to one or more Best Known Methods (BKMs) related to the first data [0042, Figs. 2-3 — The recipe verification program may automatically receive the summary or may manually be instructed to retrieve 58 the summary file for the desired recipe. The recipe attributes trigger the program 30 to select the proper BKM 60 from the categorized BKM database and compare the recipe and BKM and display 64 the parameters, critical parameters, mismatches or other parameter subset].
Clark and Ahmed are analogous art. They relate to semiconductor manufacturing systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Clark, by incorporating the above limitations, as taught by Ahmed.
One of ordinary skill in the art would have been motivated to do this modification to facilitate recipes that are verified and run well and maintain consistency among recipes, as suggested by Ahmed [0010-0011].
But the combination of Clark and Ahmed fails to clearly specify operational data generated from a first number of processing runs using the processing recipe; second operational data generated from a second number of processing runs using the processing recipe; wherein the second number is greater than the first number.
However, Pasadyn teaches operational data generated from a first number of processing runs using the processing recipe; second operational data generated from a second number of processing runs using the processing recipe; wherein the second number is greater than the first number [col. 7 lines 26-33 — computer system 530 employs a manufacturing model 540 to generate control input signals on the line 523. In one embodiment, the manufacturing model 540 contains a manufacturing recipe that determines a plurality of control input parameters that are sent on the line 523 to the processing tools 510a, 510b; col. 10 line 49 – col. 11 line 54 — processing tool 510 also informs the system 300 of the number of sites examined on the sample wafers 105 (block 920). Generally, the higher the number of sites on a particular sample wafer 105 that are analyzed, the more accurate and reliable the metrology data. In certain situations, the system 300 may decide that a smaller number of sites can be analyzed by the integrated metrology tool 310 due to the time constraints because of the increased number of wafers 105 that are sampled. Generally, the smaller the sampling rate (i.e., the number of wafers 105 analyzed by the integrated metrology tool 310), the larger the desired site number. The system 300 can use the number of sites as an indication of the reliability of the metrology data. The sampling rate and the number of sites are used by the system 300 to determine the weight of the metrology data. For example, when the sampling rate is 67% (e.g., analyzing every two of three wafers that are processed) relatively high and a large number of sites are examined, the weight of the metrology data is higher than when the sampling rated is 50% and a smaller number of sites are examined — Performing tests/metrology on groups of wafers that comprise different numbers of wafers — tests/metrology are performed after the wafers are processed (Figs. 7 and 9); col. 9 lines 27-42, Fig. 7 — While manufacturing the semiconductor wafers 105, the system 300 performs a partial measurement data acquisition process (block 720). A more detailed illustration, and accompanying description of performing the partial measurement data acquisition process indicated in block 720 are provided below. Once the system 300 performs a partial measurement data acquisition process, the system 300 uses the partial measurement data to calculate feedback and/or feed-forward adjustments (block 730).].
Clark, Ahmed and Pasadyn are analogous art. They relate to semiconductor manufacturing systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Clark and Ahmed, by incorporating the above limitations, as taught by Pasadyn.
One of ordinary skill in the art would have been motivated to do this modification in order to continuously manufacture devices and account for processes change while adjusting the number of sampled wafers to maintain accuracy and reliability, as suggested by the teachings of Pasadyn [col. 10 line 49 – col. 11 line 54].
Regarding claim 9, the combination of Clark, Ahmed and Pasadyn teaches all the limitations of the base claims as outlined above.
Further, Clark teaches equipment constant monitoring [0077-0078 — The common platform integrates heterogeneous equipment and processing modules with metrology or measurement modules that monitor substrate fabricator progress].
Further, Ahmed teaches analysis comprises recipe checking [0042, Figs. 2-3 — The recipe verification program may automatically receive the summary or may manually be instructed to retrieve 58 the summary file for the desired recipe. The recipe attributes trigger the program 30 to select the proper BKM 60 from the categorized BKM database and compare the recipe and BKM and display 64 the parameters, critical parameters, mismatches or other parameter subset].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Clark, Ahmed and Pasadyn, by incorporating the above limitations, as taught by Ahmed.
One of ordinary skill in the art would have been motivated to do this modification to facilitate recipes that are verified and run well and maintain consistency among recipes, as suggested by Ahmed [0010-0011].
Regarding claim 11, the combination of Clark, Ahmed and Pasadyn teaches all the limitations of the base claims as outlined above.
Further, Clark teaches operational data comprises trace sensor data, and wherein the second analysis comprises determining whether values of the trace sensor data satisfy a threshold condition [0363, 0382-0384, 0389 — Self-conceptualization component 2160 flags a failure in comparator 2720 when the average difference between predicted pressure values and collected pressure data (e.g., as reported by a pressure sensor residing in sensor component) fails to remain within user-specified bounds—e.g., average difference is to remain within 5% of predicted pressure].
Regarding claim 12, the combination of Clark, Ahmed and Pasadyn teaches all the limitations of the base claims as outlined above.
Further, Clark teaches first operational data comprises trace sensor data, and wherein the second operations data comprises one or more statistical metrics associated with the trace sensor data [0363, 0382-0384, 0389 — The data can be data for a specific sensor during a single specific operation step of a tool system 1910, a set of parameters during a single specific step, a single parameter average for a run].
Regarding claim 13, the combination of Clark, Ahmed and Pasadyn teaches all the limitations of the base claims as outlined above.
Further, Clark teaches providing the third data to a trained machine learning model, wherein the trained machine learning model is configured to detect one or more faults based on operational data; and receiving output from the trained machine learning model, wherein performance of the corrective action is based on the output from the trained machine learning model [0210, Fig. 11 — s pattern recognition engine 1122 that is operable to extract and classify data patterns from the measured and predict whether or not a non-conformity exists based on the measured data… pattern recognition engine 1122 may implement a deep learning architecture or engine 1124 as shown that might use one or more neural networks and supervised or unsupervised learning (machine learning models) for implementing the pattern recognition…and determine a possible cause for use to do corrective processing].
Regarding claim 14, the combination of Clark, Ahmed and Pasadyn teaches all the limitations of the base claims as outlined above.
Further, Clark teaches the corrective action comprises one or more of: providing an alert to a user; updating a processing recipe; updating an equipment constant; scheduling maintenance of manufacturing equipment; or updating a best known method associated with the first analysis, second analysis, or third analysis [0078 — The invention can use the data collected for providing virtual metrology (VM), run-to-run (R2R) control to monitor and control process variations, statistical process control (SPC) to alert operators that equipment and/or process is operating outside control limits, advanced process control (APC), fault detection and classification (FDC)].
Regarding claim 15, the combination of Clark, Ahmed and Pasadyn teaches all the limitations of the base claims as outlined above.
Further, Clark teaches providing a visualization of the first analysis, the second analysis, or the third analysis via a graphical user interface (GUI) [0231 — The controller can (i) identify process steps producing substrate results outside target specification, (ii) extract data, e.g., workpiece measurement and metrology data, etc., for the out-of-spec process step, emulate the impact of the out-of-spec condition on downstream process steps, (iii) display the data or portions of the data; 0378, Figs. 21 and 26 — an example embodiment 2600 of a self-optimization component in an autonomous biologically based learning system… . As indicated above, self-optimization component functionality is to analyze the current health (e.g., performance) of a manufacturing platform/tool system 1910 and then determine if non-conformities are detected and, based on the results of the current health analysis, diagnose or rank substantially all potential causes for health deterioration of the tool system 1910 and the cause of such non-conformities, and identify a root cause of non-conformities based on learning acquired by autonomous learning system 1960 in order to provide the necessary control of the manufacturing platform to provide corrective processing.; 0391-0397, Fig. 28 — The group of autonomous tools systems 2820.sub.1-2820.sub.K can be controlled by an autonomous biologically based learning tool 1960 which receives (input) and conveys (output) information 1958 to an interface 1930 that facilitates an actor 1990 to interact with the group of autonomous tools system… the autonomous system 1960 can also construct a predictive model of group time-to-failure as a function of assets 1928 of tool group or platform 2800; e.g., group input data, group outputs, group recipes, or group maintenance activities. In an aspect, to determine a group time-to-failure, autonomous learning system 1960 can gather failure data, including time between detected (e.g., through a set of sensor components or inspection systems) failures, associated assets 2850.sub.1-2850.sub.K, outputs 2801-2860K, and maintenance activities for substantially all operation tools in the set of tools 2801-2820K (operational and historical data). (It should be appreciated that as a consequence of prior failure assessments, specific tools (e.g., tool system 2 2820.sub.1 and tool system K 2820.sub.K) in the set of tools (e.g., tools 2820.sub.1-2820.sub.K) in group 2800 can be out of operation.) Collected data can be autonomously analyzed (e.g., through a processing component 1985 in autonomous learning system 1960) to learn a predictive function for time-to-failure as a function of the group assets (e.g., inputs, recipes, . . . ), outputs, and maintenance activities… when a group performance appears degraded, individual performances associated with individual tools can be analyzed].
Regarding claim 20, Clark teaches all the limitations of the base claims as outlined above.
Further, Clark teaches performing analysis indicative of performance of the electronics manufacturing system using the first, second, and third data [0378, Figs. 21 and 26 — an example embodiment 2600 of a self-optimization component in an autonomous biologically based learning system… . As indicated above, self-optimization component functionality is to analyze the current health (e.g., performance) of a manufacturing platform/tool system 1910 and then determine if non-conformities are detected and, based on the results of the current health analysis, diagnose or rank substantially all potential causes for health deterioration of the tool system 1910 and the cause of such non-conformities, and identify a root cause of non-conformities based on learning acquired by autonomous learning system 1960 in order to provide the necessary control of the manufacturing platform to provide corrective processing.; 0391-0397, Fig. 28 — The group of autonomous tools systems 2820.sub.1-2820.sub.K can be controlled by an autonomous biologically based learning tool 1960 which receives (input) and conveys (output) information 1958 to an interface 1930 that facilitates an actor 1990 to interact with the group of autonomous tools system… the autonomous system 1960 can also construct a predictive model of group time-to-failure as a function of assets 1928 of tool group or platform 2800; e.g., group input data, group outputs, group recipes, or group maintenance activities. In an aspect, to determine a group time-to-failure, autonomous learning system 1960 can gather failure data, including time between detected (e.g., through a set of sensor components or inspection systems) failures, associated assets 2850.sub.1-2850.sub.K, outputs 2801-2860K, and maintenance activities for substantially all operation tools in the set of tools 2801-2820K (operational and historical data). (It should be appreciated that as a consequence of prior failure assessments, specific tools (e.g., tool system 2 2820.sub.1 and tool system K 2820.sub.K) in the set of tools (e.g., tools 2820.sub.1-2820.sub.K) in group 2800 can be out of operation.) Collected data can be autonomously analyzed (e.g., through a processing component 1985 in autonomous learning system 1960) to learn a predictive function for time-to-failure as a function of the group assets (e.g., inputs, recipes, . . . ), outputs, and maintenance activities… when a group performance appears degraded, individual performances associated with individual tools can be analyzed; 0116-0125, Fig. 4 — system of platform 400… substrate processing chambers 420a-420d (individual tools/chambers); 0324, Fig. 20 — a deposition chamber in tool system 1910] and processing substrates using recipes [0124-0130 — the control system 422 may be utilized to activate the inputs to the various processing systems and transfer systems according to a process recipe… substrate processing system implanted on platform 500; 0106-0113 — systems 200, 300 that incorporate a common platform with multiple processing modules, one or more measurement modules and one or more transfer modules coupled with an active interdiction control system. The systems enhance the yield of functional microelectronic devices produced from semiconductor fabrication (electronics manufacturing system)].
But Clark fails to clearly specify comparing first data to a plurality of rules associated with processing recipes; performing one or more tests upon the second data, wherein the second data is associated with a first number of substrates processed using the processing recipe; and performing one or more tests upon the third data, wherein the third data is associated with a second number of substrates processed using the processing recipe, and wherein the second number is greater than the first number.
However, Ahmed teaches comparing first data to a plurality of rules associated with processing recipes [0042, Figs. 2-3 — The recipe verification program may automatically receive the summary or may manually be instructed to retrieve 58 the summary file for the desired recipe. The recipe attributes trigger the program 30 to select the proper BKM 60 from the categorized BKM database and compare the recipe and BKM and display 64 the parameters, critical parameters, mismatches or other parameter subset].
Clark and Ahmed are analogous art. They relate to semiconductor manufacturing systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above non-transitory machine-readable storage medium, as taught by Clark, by incorporating the above limitations, as taught by Ahmed.
One of ordinary skill in the art would have been motivated to do this modification to facilitate recipes that are verified and run well and maintain consistency among recipes, as suggested by Ahmed [0010-0011].
But the combination of Clark and Ahmed fails to clearly specify performing one or more tests upon the second data, wherein the second data is associated with a first number of substrates processed using the processing recipe; and performing one or more tests upon the third data, wherein the third data is associated with a second number of substrates processed using the processing recipe, and wherein the second number is greater than the first number.
However, Pasadyn teaches performing one or more tests upon the second data, wherein the second data is associated with a first number of substrates processed using the processing recipe; and performing one or more tests upon the third data, wherein the third data is associated with a second number of substrates processed using the processing recipe, and wherein the second number is greater than the first number [col. 7 lines 26-33 — computer system 530 employs a manufacturing model 540 to generate control input signals on the line 523. In one embodiment, the manufacturing model 540 contains a manufacturing recipe that determines a plurality of control input parameters that are sent on the line 523 to the processing tools 510a, 510b; col. 10 line 49 – col. 11 line 54 — processing tool 510 also informs the system 300 of the number of sites examined on the sample wafers 105 (block 920). Generally, the higher the number of sites on a particular sample wafer 105 that are analyzed, the more accurate and reliable the metrology data. In certain situations, the system 300 may decide that a smaller number of sites can be analyzed by the integrated metrology tool 310 due to the time constraints because of the increased number of wafers 105 that are sampled. Generally, the smaller the sampling rate (i.e., the number of wafers 105 analyzed by the integrated metrology tool 310), the larger the desired site number. The system 300 can use the number of sites as an indication of the reliability of the metrology data. The sampling rate and the number of sites are used by the system 300 to determine the weight of the metrology data. For example, when the sampling rate is 67% (e.g., analyzing every two of three wafers that are processed) relatively high and a large number of sites are examined, the weight of the metrology data is higher than when the sampling rated is 50% and a smaller number of sites are examined — Performing tests/metrology on groups of wafers that comprise different numbers of wafers — tests/metrology are performed after the wafers are processed (Figs. 7 and 9); col. 9 lines 27-42, Fig. 7 — While manufacturing the semiconductor wafers 105, the system 300 performs a partial measurement data acquisition process (block 720). A more detailed illustration, and accompanying description of performing the partial measurement data acquisition process indicated in block 720 are provided below. Once the system 300 performs a partial measurement data acquisition process, the system 300 uses the partial measurement data to calculate feedback and/or feed-forward adjustments (block 730).].
Clark, Ahmed and Pasadyn are analogous art. They relate to semiconductor manufacturing systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above non-transitory machine-readable storage medium, as taught by the combination of Clark and Ahmed, by incorporating the above limitations, as taught by Pasadyn.
One of ordinary skill in the art would have been motivated to do this modification in order to continuously manufacture devices and account for processes change while adjusting the number of sampled wafers to maintain accuracy and reliability, as suggested by the teachings of Pasadyn [col. 10 line 49 – col. 11 line 54].
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Clark, Ahmed and Pasadyn in view of Yoshinaga.
Regarding claim 10, the combination of Clark, Ahmed and Pasadyn teaches all the limitations of the base claims as outlined above.
But the combination of Clark, Ahmed and Pasadyn fails to clearly specify operating window analysis, wherein operating window analysis comprises: defining a first window duration; selecting a first plurality of window placements, wherein each of the first plurality of window placements is of the first window duration; determining a first plurality of statistical metrics, wherein each of the first plurality of statistical metrics is associated with data within one of the first plurality of window placements; defining a second window duration; selecting a second plurality of window placements, wherein each of the second plurality of window placements is of the second window duration; determining a second plurality of statistical metrics, wherein each of the second plurality of statistical metrics is associated with data within one of the second plurality of window placements; comparing the first plurality of statistical metrics to a first threshold value; and comparing the second plurality of statistical metrics to a second threshold value.
However, Yoshinaga teaches operating window analysis, wherein operating window analysis comprises: defining a first window duration; selecting a first plurality of window placements, wherein each of the first plurality of window placements is of the first window duration; determining a first plurality of statistical metrics, wherein each of the first plurality of statistical metrics is associated with data within one of the first plurality of window placements; defining a second window duration; selecting a second plurality of window placements, wherein each of the second plurality of window placements is of the second window duration; determining a second plurality of statistical metrics, wherein each of the second plurality of statistical metrics is associated with data within one of the second plurality of window placements; comparing the first plurality of statistical metrics to a first threshold value; and comparing the second plurality of statistical metrics to a second threshold value [0002, 0036 — A time series data processing apparatus 10 according to the present invention is connected to a measurement target P such as a plant.; 0005, 0042-0049, 0055, 0067, Figs. 1-3, 5-6, 11 and 13 — a normal period (first/second window durations) in which a monitoring target is actually in a normal state and an anomalous period (first/second window durations) in which the monitoring target is in an anomalous state are set on the anomaly degree graph D2. Then, from the anomalous period on the anomaly degree graph D2, as shown in FIG. 2, a plurality of candidates for the threshold value can be considered, such as (threshold value A1) “a case where a degree of anomaly exceeds 30 even for a moment”, (threshold value A2) “… the extracting unit 12 first sets a window W of a duration having the minimum value “1”, and obtains the maximum value of the degree of anomaly while sliding the window W (plurality of window placements) on the anomaly degree graph as shown by an arrow in FIG. 6; 0052-0057, Fig. 11 —the maximum value of the degree of anomaly is “15” as shown in FIG. 11A. Consequently, [3, 15] is extracted as the maximum coverage value that is the combination of “duration, anomaly degree”.].
Clark, Ahmed, Pasadyn and Yoshinaga are analogous art. They relate to manufacturing systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Clark, Ahmed and Pasadyn, by incorporating the above limitations, as taught by Yoshinaga.
One of ordinary skill in the art would have been motivated to do this modification to improve the detection of anomalies, particularly with regard to setting threshold, as suggested by Yoshinaga [0006-0007]. In addition, it would be obvious to utilize normal and abnormal time windows to identify when anomalies are taking place and to take advantage of the longest possible time windows, that may be different than shorter time windows, to obtain the best statistical data and hence the best accuracy.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Clark, Ahmed and Pasadyn in view of Nixon et al. U.S. Patent Publication No. 20180107188 (hereinafter Nixon).
Regarding claim 16, the combination of Clark, Ahmed and Pasadyn teaches all the limitations of the base claims as outlined above.
Further, Clark teaches providing a visualization of the first analysis, the second analysis, or the third analysis via a graphical user interface (GUI) [0231 — The controller can (i) identify process steps producing substrate results outside target specification, (ii) extract data, e.g., workpiece measurement and metrology data, etc., for the out-of-spec process step, emulate the impact of the out-of-spec condition on downstream process steps, (iii) display the data or portions of the data; 0378, Figs. 21 and 26 — an example embodiment 2600 of a self-optimization component in an autonomous biologically based learning system… . As indicated above, self-optimization component functionality is to analyze the current health (e.g., performance) of a manufacturing platform/tool system 1910 and then determine if non-conformities are detected and, based on the results of the current health analysis, diagnose or rank substantially all potential causes for health deterioration of the tool system 1910 and the cause of such non-conformities, and identify a root cause of non-conformities based on learning acquired by autonomous learning system 1960 in order to provide the necessary control of the manufacturing platform to provide corrective processing.; 0391-0397, Fig. 28 — The group of autonomous tools systems 2820.sub.1-2820.sub.K can be controlled by an autonomous biologically based learning tool 1960 which receives (input) and conveys (output) information 1958 to an interface 1930 that facilitates an actor 1990 to interact with the group of autonomous tools system… the autonomous system 1960 can also construct a predictive model of group time-to-failure as a function of assets 1928 of tool group or platform 2800; e.g., group input data, group outputs, group recipes, or group maintenance activities. In an aspect, to determine a group time-to-failure, autonomous learning system 1960 can gather failure data, including time between detected (e.g., through a set of sensor components or inspection systems) failures, associated assets 2850.sub.1-2850.sub.K, outputs 2801-2860K, and maintenance activities for substantially all operation tools in the set of tools 2801-2820K (operational and historical data). (It should be appreciated that as a consequence of prior failure assessments, specific tools (e.g., tool system 2 2820.sub.1 and tool system K 2820.sub.K) in the set of tools (e.g., tools 2820.sub.1-2820.sub.K) in group 2800 can be out of operation.) Collected data can be autonomously analyzed (e.g., through a processing component 1985 in autonomous learning system 1960) to learn a predictive function for time-to-failure as a function of the group assets (e.g., inputs, recipes, . . . ), outputs, and maintenance activities… when a group performance appears degraded, individual performances associated with individual tools can be analyzed].
But the combination of Clark, Ahmed and Pasadyn fails to clearly specify the GUI further comprises a code, wherein the code may be utilized to direct a second user interface to display a visualization of the data.
However, Nixon teaches the GUI further comprises a code, wherein the code may be utilized to direct a second user interface to display a visualization of the data [0253, Fig. 4 — any of the displays depicted in FIGS. 4A to 4J may include a control (not shown) in the form of a button or other link that may allow a user to transmit a notification to another mobile device. The notification sent to the second mobile device may enable the user of the device on which the control is activated to share the view currently displayed. That is, a user of a first device may desire to share the view currently depicted on the mobile device with another user of a second mobile device. By activating the “share view” control, the user of the first device may cause the first device to send to the second device a notification including a link or other indicator that, when activated by the user of the second device, causes the second device to request from the mobile sever a display associated with the link. The display associated with the link may be the same display as was depicted on the first device at the time the control was activated, thereby allowing the second user to see what the first user was seeing at the time the control was activated, which may include a notifications screen, an alarm detail, a watch list, etc.].
Clark, Ahmed, Pasadyn and Nixon are analogous art. They relate to manufacturing systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Clark, Ahmed and Pasadyn, by incorporating the above limitations, as taught by Nixon.
One of ordinary skill in the art would have been motivated to do this modification to enable remote users to have timely and easily access to data, as suggested by Nixon [0008-0009].
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clark in view of Wright et al. U.S. Patent Publication No. 20070055782 (hereinafter Wright).
Regarding claim 18, Clark teaches all the limitations of the base claims as outlined above.
Further, Clark teaches displaying a visual representation of analysis results on a graphical user interface [0231 — The controller can (i) identify process steps producing substrate results outside target specification, (ii) extract data, e.g., workpiece measurement and metrology data, etc., for the out-of-spec process step, emulate the impact of the out-of-spec condition on downstream process steps, (iii) display the data or portions of the data; 0378, Figs. 21 and 26 — an example embodiment 2600 of a self-optimization component in an autonomous biologically based learning system… . As indicated above, self-optimization component functionality is to analyze the current health (e.g., performance) of a manufacturing platform/tool system 1910 and then determine if non-conformities are detected and, based on the results of the current health analysis, diagnose or rank substantially all potential causes for health deterioration of the tool system 1910 and the cause of such non-conformities, and identify a root cause of non-conformities based on learning acquired by autonomous learning system 1960 in order to provide the necessary control of the manufacturing platform to provide corrective processing.; 0391-0397, Fig. 28 — The group of autonomous tools systems 2820.sub.1-2820.sub.K can be controlled by an autonomous biologically based learning tool 1960 which receives (input) and conveys (output) information 1958 to an interface 1930 that facilitates an actor 1990 to interact with the group of autonomous tools system… the autonomous system 1960 can also construct a predictive model of group time-to-failure as a function of assets 1928 of tool group or platform 2800; e.g., group input data, group outputs, group recipes, or group maintenance activities. In an aspect, to determine a group time-to-failure, autonomous learning system 1960 can gather failure data, including time between detected (e.g., through a set of sensor components or inspection systems) failures, associated assets 2850.sub.1-2850.sub.K, outputs 2801-2860K, and maintenance activities for substantially all operation tools in the set of tools 2801-2820K (operational and historical data). (It should be appreciated that as a consequence of prior failure assessments, specific tools (e.g., tool system 2 2820.sub.1 and tool system K 2820.sub.K) in the set of tools (e.g., tools 2820.sub.1-2820.sub.K) in group 2800 can be out of operation.) Collected data can be autonomously analyzed (e.g., through a processing component 1985 in autonomous learning system 1960) to learn a predictive function for time-to-failure as a function of the group assets (e.g., inputs, recipes, . . . ), outputs, and maintenance activities… when a group performance appears degraded, individual performances associated with individual tools can be analyzed].
But Clark fails to clearly specify generating a code associated with navigating to the visual representation of analysis results.
However, Wright teaches generating a code associated with navigating to the visual representation of analysis results [0003, 0213, Figs. 1, 3, 27 — the link to the associated detailed analysis could be represented on the subsequently displayed screenshot image as a hyperlink to the associated detailed analysis, as desired].
Clark and Wright are analogous art. They relate to manufacturing systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above non-transitory machine-readable storage medium, as taught by Clark, by incorporating the above limitations, as taught by Wright.
One of ordinary skill in the art would have been motivated to do this modification to improve analysis and interpretation of data by simplifying and uncluttering visual presentation, as suggested by Wright [0006-0007].
Note that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Conclusion
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 BERNARD G. LINDSAY whose telephone number is (571)270-0665. The examiner can normally be reached Monday through Friday from 8:30 AM to 5:30 PM EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached on (571)272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant may call the examiner or use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/BERNARD G LINDSAY/
Primary Examiner, Art Unit 2119