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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/30/26 has been entered.
Response to Arguments
Applicant’s arguments, filed 4/30/26, have been fully considered but are not persuasive.
Applicant’s arguments regarding 35 U.S.C. § 101 (pages 10-11) are moot because the claims are no longer rejected under that statute.
Applicant’s arguments regarding 35 U.S.C. § 102 (pages 11-12) are moot because the claims are no longer rejected under that statute.
Applicant’s arguments regarding 35 U.S.C. § 103 (pages 12-15) are moot in view of the newly cited reference, Marques.
For at least these reasons, the rejection of the claims is maintained.
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) 1, 17 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clark et al. U.S. Patent Publication No. 20200006100 (hereinafter Clark) in view of Marques et al. U.S. Patent Publication No. 20240094708 (hereinafter Marques).
Regarding claim 1, 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 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)];
causing performance of a corrective action in view of the analysis, the corrective action comprising: modifying, based on the analysis, at least one of a processing recipe, an equipment constant, or a manufacturing parameter; and processing a substrate using the modified processing recipe, equipment constant, or manufacturing parameter [0231 — 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, (iv) optimize process recipe adjustment(s) to the process recipe, including upstream or downstream process adjustments to compensate for the defect, and (v) communicate proposed recipe adjustment(s) for adoption with the process flow to correct for the out-of-spec condition… the intelligent controller can consider all corrective options from the deposition tool recipe database, and emulate the outcome based off all downstream unit process recipe combinations for the problematic substrates. Thereafter, corrective action can be executed, including passing the current process step, failing the current process step and discarding the substrate, or remediating the process step by compensating for its deficiencies either upstream and/or downstream of the current process step; 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 displaying, via a graphical user interface (GUI), a graphical machine-scannable code encoding information comprising at least one of a processing run, a tool, a recipe, an operation, or a time offset of a processing run.
However, Marques teaches displaying, via a graphical user interface (GUI), a graphical machine-scannable code encoding information comprising at least one of a processing run, a tool, a recipe, an operation, or a time offset of a processing run [0064, Fig. 6 — image generation system 100 may be executed by the processor 26 and may be utilized to generate machine-readable indicia on the display 34. In some embodiments, the image generation system 100 may receive a set of inputs 102, such as connection data 104, operation data 106, and authentication data 108, and may generate a set of outputs 110, such as image data 112… operation data 106 may include operational parameters, operational history (e.g., component installation date/time, initial startup date/time, initial configuration date/time), status information, sensor data, operational modes, alarm conditions (e.g., current alarms, historical alarms, alarm start times, alarm stop times, operators associated with the alarms), spare parts, parts orders, order numbers, and the like; 0067-0073 — image data 112 may include machine-readable indicia (e.g., barcode, QR code, and so forth) that may be displayed on the display 34… the image data 112 may provide data and/or information associated with current statuses of the industrial automation component 20 or enable the secondary device 96 to access such information… secondary device 96 may also include an image sensor 120, which may include any image acquisition circuitry such as a digital camera capable of acquiring digital images, digital videos, or the like. For example, the image sensor 120 may capture the image data 112 rendered on the display 34 of the industrial automation component 20. In certain embodiments, the image sensor 120 may transmit the image data 112 to the processor 26 and the processor 26 may analyze and/or process the image data 112 to facilitate retrieval of data and/or information associated with the industrial automation component 20. For example, the processor 26 may determine a website associated with the image data 112 and may access the website to retrieve the data. In some embodiments, the data may include the connection data 104, the operation data 106 (information comprising an operation), the authentication data 108, and the like].
Clark and Marques 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 Marques.
One of ordinary skill in the art would have been motivated to do this modification to enable technicians to better understand operations of an industrial automations systems by providing accurate and up-to-date information about components of industrial automation systems, for example, for the technicians to utilize, as taught by Marques [0003].
Regarding claim 17, Clark teaches 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)];
causing performance of a corrective action in view of the analysis, the corrective action comprising: modifying, based on the analysis, at least one of a processing recipe, an equipment constant, or a manufacturing parameter; and processing a substrate using the modified processing recipe, equipment constant, or manufacturing parameter [0231 — 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, (iv) optimize process recipe adjustment(s) to the process recipe, including upstream or downstream process adjustments to compensate for the defect, and (v) communicate proposed recipe adjustment(s) for adoption with the process flow to correct for the out-of-spec condition… the intelligent controller can consider all corrective options from the deposition tool recipe database, and emulate the outcome based off all downstream unit process recipe combinations for the problematic substrates. Thereafter, corrective action can be executed, including passing the current process step, failing the current process step and discarding the substrate, or remediating the process step by compensating for its deficiencies either upstream and/or downstream of the current process step; 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 displaying, via a graphical user interface (GUI), a graphical machine-scannable code encoding information comprising at least one of a processing run, a tool, a recipe, an operation, or a time offset of a processing run.
However, Marques teaches displaying, via a graphical user interface (GUI), a graphical machine-scannable code encoding information comprising at least one of a processing run, a tool, a recipe, an operation, or a time offset of a processing run [0064, Fig. 6 — image generation system 100 may be executed by the processor 26 and may be utilized to generate machine-readable indicia on the display 34. In some embodiments, the image generation system 100 may receive a set of inputs 102, such as connection data 104, operation data 106, and authentication data 108, and may generate a set of outputs 110, such as image data 112… operation data 106 may include operational parameters, operational history (e.g., component installation date/time, initial startup date/time, initial configuration date/time), status information, sensor data, operational modes, alarm conditions (e.g., current alarms, historical alarms, alarm start times, alarm stop times, operators associated with the alarms), spare parts, parts orders, order numbers, and the like; 0067-0073 — image data 112 may include machine-readable indicia (e.g., barcode, QR code, and so forth) that may be displayed on the display 34… the image data 112 may provide data and/or information associated with current statuses of the industrial automation component 20 or enable the secondary device 96 to access such information… secondary device 96 may also include an image sensor 120, which may include any image acquisition circuitry such as a digital camera capable of acquiring digital images, digital videos, or the like. For example, the image sensor 120 may capture the image data 112 rendered on the display 34 of the industrial automation component 20. In certain embodiments, the image sensor 120 may transmit the image data 112 to the processor 26 and the processor 26 may analyze and/or process the image data 112 to facilitate retrieval of data and/or information associated with the industrial automation component 20. For example, the processor 26 may determine a website associated with the image data 112 and may access the website to retrieve the data. In some embodiments, the data may include the connection data 104, the operation data 106 (information comprising an operation), the authentication data 108, and the like].
Clark and Marques 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 Marques.
One of ordinary skill in the art would have been motivated to do this modification to enable technicians to better understand operations of an industrial automations systems by providing accurate and up-to-date information about components of industrial automation systems, for example, for the technicians to utilize, as taught by Marques [0003].
Regarding claim 18, the combination of Clark and Marques 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].
Further, Marques teaches the graphical machine-scannable code comprises at least one of a bar code or a quick response (QR) code [0067-0073 — image data 112 may include machine-readable indicia (e.g., barcode, QR code, and so forth) that may be displayed on the display 34… the image data 112 may provide data and/or information associated with current statuses of the industrial automation component 20 or enable the secondary device 96 to access such information].
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 Marques.
One of ordinary skill in the art would have been motivated to do this modification to enable technicians to better understand operations of an industrial automations systems by providing accurate and up-to-date information about components of industrial automation systems, for example, for the technicians to utilize, using an accepted standard type of code, as suggested by Marques [0003, 0067-0073].
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Clark and Marques in view of Ahmed et al. U.S. Patent Publication No. 20070192056 (hereinafter Ahmed).
Regarding claim 2, the combination of Clark and Marques 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 the combination of Clark and Marques 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, Marques and Ahmed 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 and Marques, 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 the combination of Clark and Marques in view of Yoshinaga U.S. Patent Publication No. 20220334030 (hereinafter Yoshinaga).
Regarding claim 3, the combination of Clark and Marques teaches all the limitations of the base claims as outlined above.
But the combination of Clark and Marques 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, Marques 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 and Marques, 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 the combination of Clark and Marques in view of Lian et al. U.S. Patent Publication No. 20150241272 (hereinafter Lian).
Regarding claim 4, the combination of Clark and Marques 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 the combination of Clark and Marques 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, Marques and Lian 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 and Marques, 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 the combination of Clark and Marques in view of Gwinn U.S. Patent Publication No. 20220351997 (hereinafter Gwinn).
Regarding claim 5, the combination of Clark and Marques 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 the combination of Clark and Marques 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, Marques and Gwinn 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 and Marques, 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 the combination of Clark and Marques in view of Kaushal et al. U.S. Patent Publication No. 20170023927 (hereinafter Kaushal).
Regarding claim 6, the combination of Clark and Marques teaches all the limitations of the base claims as outlined above.
Further, Clark teaches 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].
But the combination of Clark and Marques 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, Marques and Kaushal 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 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, the combination of Clark and Marques 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 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Clark and Marques in view of Ahmed and further in view of Pasadyn et al. U.S. Patent No. 6708129 (hereinafter Pasadyn).
Regarding claim 7, the combination of Clark and Marques 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, Marques and Ahmed 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 and Marques, 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, Marques 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, Marques, Ahmed and Pasadyn 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, Marques 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 20, the combination of Clark and Marques 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 the combination of Clark and Marques 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, Marques and Ahmed 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 the combination of Clark and Marques, 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, Marques 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, Marques, Ahmed and Pasadyn 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 the combination of Clark, Marques 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) 8-9 and 11-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Clark in view of Ahmed and further in view of Pasadyn and Marques.
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)];
causing performance of a corrective action based on the first analysis, second analysis, and third analysis, analysis, the corrective action comprising: modifying, based on the analysis, at least one of a processing recipe, an equipment constant, or a manufacturing parameter; and processing a substrate using the modified processing recipe, equipment constant, or manufacturing parameter [0231 — 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, (iv) optimize process recipe adjustment(s) to the process recipe, including upstream or downstream process adjustments to compensate for the defect, and (v) communicate proposed recipe adjustment(s) for adoption with the process flow to correct for the out-of-spec condition… the intelligent controller can consider all corrective options from the deposition tool recipe database, and emulate the outcome based off all downstream unit process recipe combinations for the problematic substrates. Thereafter, corrective action can be executed, including passing the current process step, failing the current process step and discarding the substrate, or remediating the process step by compensating for its deficiencies either upstream and/or downstream of the current process step; 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 and displaying, via a graphical user interface (GUI), a graphical machine-scannable code encoding information comprising at least one of a processing run, a tool, a recipe, an operation, or a time offset of a processing run.
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 and displaying, via a graphical user interface (GUI), a graphical machine-scannable code encoding information comprising at least one of a processing run, a tool, a recipe, an operation, or a time offset of a processing run.
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].
But the combination of Clark, Ahmed and Pasadyn fails to clearly specify displaying, via a graphical user interface (GUI), a graphical machine-scannable code encoding information comprising at least one of a processing run, a tool, a recipe, an operation, or a time offset of a processing run.
However, Marques teaches displaying, via a graphical user interface (GUI), a graphical machine-scannable code encoding information comprising at least one of a processing run, a tool, a recipe, an operation, or a time offset of a processing run [0064, Fig. 6 — image generation system 100 may be executed by the processor 26 and may be utilized to generate machine-readable indicia on the display 34. In some embodiments, the image generation system 100 may receive a set of inputs 102, such as connection data 104, operation data 106, and authentication data 108, and may generate a set of outputs 110, such as image data 112… operation data 106 may include operational parameters, operational history (e.g., component installation date/time, initial startup date/time, initial configuration date/time), status information, sensor data, operational modes, alarm conditions (e.g., current alarms, historical alarms, alarm start times, alarm stop times, operators associated with the alarms), spare parts, parts orders, order numbers, and the like; 0067-0073 — image data 112 may include machine-readable indicia (e.g., barcode, QR code, and so forth) that may be displayed on the display 34… the image data 112 may provide data and/or information associated with current statuses of the industrial automation component 20 or enable the secondary device 96 to access such information… secondary device 96 may also include an image sensor 120, which may include any image acquisition circuitry such as a digital camera capable of acquiring digital images, digital videos, or the like. For example, the image sensor 120 may capture the image data 112 rendered on the display 34 of the industrial automation component 20. In certain embodiments, the image sensor 120 may transmit the image data 112 to the processor 26 and the processor 26 may analyze and/or process the image data 112 to facilitate retrieval of data and/or information associated with the industrial automation component 20. For example, the processor 26 may determine a website associated with the image data 112 and may access the website to retrieve the data. In some embodiments, the data may include the connection data 104, the operation data 106 (information comprising an operation), the authentication data 108, and the like].
Clark, Ahmed, Pasadyn and Marques 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 Marques.
One of ordinary skill in the art would have been motivated to do this modification to enable technicians to better understand operations of an industrial automations systems by providing accurate and up-to-date information about components of industrial automation systems, for example, for the technicians to utilize, as taught by Marques [0003].
Regarding claim 9, the combination of Clark, Ahmed, Pasadyn and Marques 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, Pasadyn and Marques, 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, Pasadyn and Marques 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, Pasadyn and Marques 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, Pasadyn and Marques 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, Pasadyn and Marques 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, Pasadyn and Marques 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 the 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 16, the combination of Clark, Ahmed, Pasadyn and Marques 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].
Further, Marques teaches the machine-scannable code may be utilized to direct a second user interface to display the data [0064, Fig. 6 — image generation system 100 may be executed by the processor 26 and may be utilized to generate machine-readable indicia on the display 34. In some embodiments, the image generation system 100 may receive a set of inputs 102, such as connection data 104, operation data 106, and authentication data 108, and may generate a set of outputs 110, such as image data 112… operation data 106 may include operational parameters, operational history (e.g., component installation date/time, initial startup date/time, initial configuration date/time), status information, sensor data, operational modes, alarm conditions (e.g., current alarms, historical alarms, alarm start times, alarm stop times, operators associated with the alarms), spare parts, parts orders, order numbers, and the like; 0067-0073 — image data 112 may include machine-readable indicia (e.g., barcode, QR code, and so forth) that may be displayed on the display 34… the image data 112 may provide data and/or information associated with current statuses of the industrial automation component 20 or enable the secondary device 96 to access such information… secondary device 96 may also include an image sensor 120, which may include any image acquisition circuitry such as a digital camera capable of acquiring digital images, digital videos, or the like. For example, the image sensor 120 may capture the image data 112 rendered on the display 34 of the industrial automation component 20. In certain embodiments, the image sensor 120 may transmit the image data 112 to the processor 26 and the processor 26 may analyze and/or process the image data 112 to facilitate retrieval of data and/or information associated with the industrial automation component 20. For example, the processor 26 may determine a website associated with the image data 112 and may access the website to retrieve the data. In some embodiments, the data may include the connection data 104, the operation data 106 (information comprising an operation), the authentication data 108, and the like].
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 Marques.
One of ordinary skill in the art would have been motivated to do this modification to enable technicians to better understand operations of an industrial automations systems by providing accurate and up-to-date information about components of industrial automation systems, for example, for the technicians to utilize, as taught by Marques [0003].
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Clark, Ahmed, Pasadyn and Marques in view of Yoshinaga.
Regarding claim 10, the combination of Clark, Ahmed, Pasadyn and Marques teaches all the limitations of the base claims as outlined above.
But the combination of Clark, Ahmed, Pasadyn and Marques 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, Marques 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, Pasadyn and Marques, 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.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Van Gorp et al. U.S. Patent Publication No. 20160104310 discloses a system and method for communicating at least one human-readable element and at least one fiduciary marker that may include at least one of a quick response code and a one-dimensional bar code.
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
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
Prosecution Insights