DETAILED ACTION
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has been withdrawn pursuant to 37 CFR 1.114.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 02/18/2026 has been entered.
Response to Amendment
Applicant's amendments, filed 02/18/2026, are accepted.
Claims 1-20 are pending.
Claims 1-3, 5, 7, 10-11, 13, and 15-19 are amended.
Arguments filed 02/18/2026 have been fully considered but they are not persuasive. Specifically, Applicant argues (REMARKS, Page 8, Paragraph 2) that “independent claims 1, 10, and 16 are new, non-obvious and in condition for allowance.” Applicant makes no other argument with regard to previous office action. Examiner withdraws objection to Claim 3 for minor informalities based on amendments.
Examiner finds amended claim limitations contain matter not previously considered, necessitating further search and evaluation. Detailed response is presented below with new grounds of rejection.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6, 9-11,16-18, and 20 are rejected under 35 U.S.C. § 103(a) as being unpatentable over CHOI (US 20140346952 A1), in view of GUHA (US 20180082826 A1) and further in view of KANEKO (US 20180151332 A1)
With respect to independent Claims 1, 10, and 16, CHOI teaches:
A system, method comprising: a remote plasma source (CHOI is in same technical field: [0002]: “invention relates to a plasma processing system, and particularly, to a remote plasma system”; )
comprising a chamber ([0002]: “remote plasma system that performs a plasma treatment process in a process chamber”)
and a coil ([0018]: “system may further include an induction antenna coil”, or [0056] and Claim 9),
the remote plasma source configured to report operating data ([0021]: “sensor unit…generating operating state information (i.e., “operating data”) of the remote plasma generator”)
a data acquisition device connected to the remote plasma source ([0010]: “sensor unit (i.e., “data acquisition device”) including one or more voltage measurement”, [0011]:“sensor unit may include…current measurement”” or [0015]: “sensor unit may include a plasma measurement sensor”, and as above, [0021])
configured to: record the operating data from the remote plasma source ([0009]: “check operating state information (i.e., “operating data”)of a remote plasma”, and as above, [0021])
CHOI does not teach:
non-transitory, tangible, computer-readable storage medium, encoded with processor readable instructions
record the operating data from the remote plasma source over a period of time;
the remote plasma source configured to report operating data comprising an operating temperature, a coil voltage, and a coil current;
record system data comprising temperature and pressure data from system components that are external to the remote plasma source;
a machine learning component to:
determine a correlation between the operating data, the system data, and user-input data, the user-input data identifying a current condition of the chamber of the remote plasma source;
establishing, a threshold of an operating point based upon the correlations between the measurements of the one or more operating characteristics and the user input data,
the operating point comprising the measurements of the one or more operating
characteristics operating data at a particular time; and
provide a notification to perform preventative maintenance based upon the correlation.
GUHA teaches
non-transitory, tangible, computer-readable storage medium, encoded with processor readable instructions ([0127]: “controller may be defined as electronics having various integrated circuits, logic, memory, and/or software that receive instructions, issue instructions, control operation, enable cleaning operations, enable endpoint measurements”; FIG1, with [0125]: “include a processor, memory, software logic, hardware logic and input and output subsystems from communicating with, monitoring and controlling a plasma processing system”)
record the operating data from the remote plasma source over a period of time; (GUHA is in same technical field, [0001]: “methods and computer implemented processes for characterizing processing states…in a plasma reactor and using data streams collected during plasma processing” (i.e., “operating data”); and [0040]: “values read for a particular condition over time (i.e, “over a period of time”), and the changes in the values represent changes in said condition.)
the remote plasma source configured to report operating data ([0133]: “tasks are performed by remote processing devices”; comprising an operating temperature, a coil voltage, and a coil current; (FIG. 1, Table A, with [0050]: “a pressure sensor, a voltage sensor, a current sensor, a temperature sensor”; Examiner notes GUHA teaches method/system as applied to [0049]: “inductively coupled plasma (ICP) etching chambers”, where one of ordinary skill would understand use of “coil” (and measurement of voltage thereof) for ICP.)
record system data comprising temperature and pressure data from system components that are external to the remote plasma source; ([0126]: “controller is part of a system…systems may be integrated with electronics for controlling their operation before, during, and after processing of a semiconductor wafer or substrate…to control any of the processes disclosed herein, including the delivery of processing gases, temperature settings (e.g., heating and/or cooling), pressure settings…flow rate settings, fluid delivery settings”; Examiner interprets “components that are external to the remote plasma source” to mean any of a variety of components “to which an RPS is connected” (Specification [0023-24]))
a machine learning component ([0001]: “processing the data streams to make adjustments based on machine learning algorithms”)
determine a correlation between the operating data, the system data, and user-input data, ([0006]: “method includes generating a compensation vector that identifies differences (i.e. “determine a correlation”) between the current processing state values (i.e., “operating data, system data, user data”) and the desired processing state values”; and [0081]: “Compensation vectors will be accepted by machine learning if they are within bounds as defined by the user…implementation of machine learning to maintain the processing state can be done in real time, periodically on a schedule or upon user input”)
the user-input data identifying a current condition of the chamber of the remote plasma source; (As above, [0081]: “compensation vectors are defined in terms of measured sensor output characteristics that define the current processing state values…implementation of machine learning to maintain the processing state can be done in real time, periodically on a schedule or upon user input or programmed input”)
establishing, a threshold of an operating point based upon the correlations between the measurements of the one or more operating characteristics and the user input data, (As above, [0016] “machine learning engine is configured to identify current processing state values used to produce a compensation vector…vector defines differences between the desired process state values (i.e., “threshold of an operating point”) and the current processing state values.”; Examiner interprets limitation language of “threshold” to mean some limiting value of a measured quantity.)
the operating point comprising the measurements of the one or more operating
characteristics operating data at a particular time; [0064] This allows the machine learning engine 180 to determine when the desired processing state values 170 should be adjusted, as the true performance of the plasma reactor 100 is no longer matching the original desired processing state 170. As such, the machine learning engine 180 may be dynamically adjusting the desired processing state 170 based on its periodic validation operations, e.g., utilizing off-line metrology test data that is fed back to the machine learning engine 180. Additionally, machine learning engine 180 may be provided with information regarding reactor wall surface dynamics 182. This information may include data regarding the inferred characteristics of the chamber wall surfaces, as they change during processing. By way of example, this data can be inferred from historical measurements of wall characteristics, e.g., material buildup, flaking, roughness, consumable part usage, and other physical characteristics. This data can be inferred, as it may be provided by a model that predicts the type of physical changes that will occur on the reactor wall surfaces during operations over time. In some embodiments, this data can be dynamically updated from time to time and refined based on inspection of the reactor wall surfaces, e.g. when a chamber enters a wet clean cycle.
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify CHOI to include a non-transitory, tangible, computer-readable storage medium, encoded with processor readable instructions recording the operating data from the remote plasma source over a period of time, configuring a remote plasma source to report operating data comprising an operating temperature, a coil voltage, and a coil current, record system data comprising temperature and pressure data from system components that are external to the remote plasma source, and to use a a machine learning component to determine a correlation between the operating data, the system data, and user-input data, the user-input data identifying a current condition of the chamber of the remote plasma source, and establishing a threshold of an operating point based upon the correlations between the measurements of the one or more operating characteristics and the user input data, the operating point comprising the measurements, as taught by GUHA because these techniques improve the method and system disclosed by CHOI to include advanced computational methods to efficiently determine and predict when maintenance would be required in a remote plasma system. One of ordinary skill in the art and interested in optimizing a maintenance schedule for a remote plasma source would be motivated to implement a computationally-based, user-informed machine learning process utilizing current status information as taught by GUHA to improve production efficiency by eliminating interruptions for unnecessary maintenance operations. One of ordinary skill would understand the advantage of leveraging the computational methods of machine learning algorithm process taught by GUHA to improve the system and method of CHOI, both aimed at optimizing plasma system function and reliability, where in both disclosures, real-time data is used for solving an identified problem of determination of maintenance on the system.
CHOI, as modified by GUHA as taught above, does not teach:
provide a notification to perform preventative maintenance based upon the correlation.
KANEKO teaches:
provide a notification to perform preventative maintenance based upon the correlation. (KANEKO is in same technical field, [0002]: “relates to a plasma processing apparatus”; [0042]: “apparatus may further include a notification unit that outputs maintenance information (i.e., “notification to perform preventative maintenance”) corresponding to a difference between the absorption frequency and a reference absorption frequency that is acquired in advance (i.e., “based upon the correlation”)”; further, FIG. 24, element S310, 312, S314, and S316, with [0213]: “a difference determination process (S312), the notification unit 102 of the controller 100 compares the initial absorption frequency and a measured absorption frequency…determines whether or not an absolute value of a difference between the initial absorption frequency and the measured absorption frequency is equal to or less than a first threshold value”, and [0214]: “difference determination process (S312), in a case where the absolute value of the difference between the initial absorption frequency and the measured absorption frequency is not equal to or less than the first threshold value, as an information output process (S316), the notification unit 102 outputs maintenance information (determination NG) indicating that maintenance is necessary”; Examiner interprets “correlation” as above.)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify CHOI, as modified by GUHA as taught above, to include providing a notification to perform preventative maintenance based upon the correlation, such as that of KANEKO because this step would implement the efficiency of quick comparative analysis of the method taught aby CHOI as modified by GUHA to carry out a maintenance need. This quick response is an obvious way to improve the method of CHOI as modified by GUHA, because it would optimize maintenance scheduling, prevent downtime, and ensure optimized performance of a remote plasma system, taking full advantage of the real-time, user-involved monitoring of a remote plasma system as taught in the combination CHOI with GUHA, where comparative analysis is improved with implementation of GUHA’s machine learning algorithm analysis technique. One of ordinary skill in the art with interest in optimizing a maintenance schedule for a remote plasma source would be motivated to improve the system of CHOI as modified by GUHA with the notification as taught by KANEKO as a logical step in providing desired efficiency and cost saving measures.
With respect to Claims 2 and 11 (Currently Amended), CHOI, in view of GUHA, and further in view of KANEKO, teaches the limitations of Claim 1.
However, CHOI, as modified by GUHA, and further modified by KANEKO, as
GUHA further teaches:
the data acquisition device is configured to record a plurality of indications of system fault events (Abstract: “plurality of data streams are received from the plasma reactor during the processing of the substrate. The plurality of data streams are used to identify current processing state values.”)
the machine learning component (As above, Abstract) configured to determine a correlation between the operating data, the system data, the user-input data, and the plurality of indications of system fault events ([0006]: “plurality of data streams are received from the plasma reactor during the processing…used to identify current processing state values (i.e., “operating data” and “system data”)…method includes generating a compensation vector that identifies differences (i.e., “determine a correlation”)between the current processing state values and the desired processing state values”; and [0016]: “machine learning engine is configured to identify current processing state values used to produce a compensation vector…compensation vector defines differences between the desired process state values and the current processing state values…controller is further configured to execute compensation processing”; and [0038]: “Data is then analyzed to provide substantial real-time information about a plasma reactor's processing environment…it is possible to define deviations from an ideal behavior” (i.e., “indications of system faults”))
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify CHOI, as modified by GUHA and KANEKO as taught above, to include the data acquisition device is configured to record a plurality of indications of system fault events and a machine learning component configured to determine a correlation between the operating data, the system data, the user-input data, and the plurality of indications of system fault events , such as that further disclosed by GUHA because doing so would make use of all data streams and allow for a more accurate and reliable evaluation of system status. This more complete way to review and analyze data would be an obvious way to better the method of CHOI to develop an effective preventative maintenance schedule for a remote plasma system. One of ordinary skill would be motivated to include the method of GUHA to acquire multiple streams of data for use in a machine learning method in developing a more accurate model that would reliability predict when maintenance is necessary, as an improvement to the system disclosed by CHOI.
As above, KANEKO teaches:
provide the notification to perform preventative maintenance. (As above, [0042] and [0214].)
As noted above, it would be obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify CHOI, as modified by GUHA and KANEKO, as taught above, where all data streams have been considered in evaluation to determine faulty, to include a notification step, such as that disclosed by KANEKO because it would be the logical way to take advantage of the system of CHOI, modified with the machine learning and multiple data stream analysis method of GUHA to better solve the problem of optimizing a preventive maintenance schedule.
With respect to Claims 3 (Currently Amended), CHOI, in view of GUHA, and further in view of KANEKO, teaches the limitations of Claims 1.
GUHA further teaches:
the machine learning component (As above, Abstract)
configured to utilize one or more indirect measurements. ([0015]: “machine learning engine that receives the desired processing state values…receives data streams receives reactor wall surface dynamics for use by a phenomenological model that defines plasma dynamics within the processing environment(i.e., “indirect measurements”)”; Examiner interprets “indirect measurements” based on Applicant specification [0024], as analogous to reference disclosure.)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify CHOI, as modified by GUHA and KANEKO as taught above, to include one or more indirect measurements in a machine learning component, such as that further disclosed by GUHA because using indirect information from sources in addition to directly measured parameters would result in a more reliable and accurate assessment of overall plasma system function and allow for improved ability to identify potential fault conditions or need for maintenance. One of ordinary skill would see the advantage of combining the use of indirect data, which would be readily accessible in the method/system disclosed by CHOI as modified above by GUHA, in taking advantage of the full potential of a machine learning method to improve model development.
With respect to Claim 4, CHOI, in view of GUHA, and further in view of KANEKO, teaches the limitations of Claim 3.
GUHA further teaches:
the one or more indirect measurements comprises one or more of: a component of a plasma and chamber impedance; and a characteristic of a wall of a chamber of the remote plasma source. (As above, [0015]: “receives data streams receives reactor wall surface dynamics for use by a phenomenological model that defines plasma dynamics within the processing environment”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify CHOI, as modified by GUHA and KANEKO, as taught above, to have one or more of the indirect measurements comprise one or more of: a component of a plasma and chamber impedance; and a characteristic of a wall of a chamber of the remote plasma source, such as that further disclosed by GUHA because acquiring multiple data types, particularly data related to indirect characteristics within plasma chamber would have a reasonable expectation of success to result in a more accurate and reliable evaluation of overall system status and performance.
With respect to Claims 5 and 17, CHOI, in view of GUHA, and further in view of KANEKO, teaches the limitations of Claims 1 and 16.
CHOI further teaches:
the operating data comprises a phase between the coil voltage and coil current and an impedance. ([0039]: “a control circuit for impedance matching”; and [0053]: “voltage and the current measured by the voltage measurement sensors…and the current measurement sensors…are detected as a predetermined phase difference…voltage measurement sensors…and the current measurement sensors…varies in phase difference”)
With respect to Claims 6 and 8 (Currently Amended), CHOI, in view of GUHA, and further in view of KANEKO, teaches the limitations of Claims 1 and 16.
GUHA further teaches:
the machine learning component resides on a server that is remote from the remote plasma source. (FIG. 5, element 150 “multivariate processor” located separately from element 100 “plasma reactor”, with [0111]: “multivariate processor will include a machine learning engine”; and [0128]: “controller may be in the “cloud” or all or a part of a fab host computer system, which can allow for remote access…may enable remote access to the system…a remote computer”; Examiner interpretation of claim limitation language “remote from remote plasma source” to mean generally that a machine learning component is located and placed in a separate location from the plasma source, analogous to reference. )
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify CHOI, as modified by GUHA and KANEKO, as taught above, to have a machine learning component residing on a server that to remote from the remote plasma source, such as that further disclosed by GUHA because it is a logical arrangement for using a more advanced computational processing and control method/system for a remote plasma chamber system. This would be seen as having the advantage of both convenience and safety, since a computer using coded algorithms for machine learning would be conveniently and safely housed in a location away from a plasma chamber. One of ordinary skill would understand the benefit of including a machine learning component as a separate location from the plasma source/chamber as a practical matter that would still allow real-time monitoring and efficient response to a plasma system.
With respect to Claim 9 and 20, CHOI, in view of GUHA, and further in view of KANEKO, teaches the limitations of Claims 1 and 16.
GUHA further teaches:
the machine learning component is configured to automatically develop an algorithm to set a threshold ([0016] “machine learning engine is configured (i.e., “automatically”) to identify current processing state values used to produce a compensation vector…compensation vector defines differences between the desired process state values and the current processing state values” (i.e., “set a threshold”))
a pending system fault event is probable to a defined degree of confidence within a specified window of time. ([0037] “methods and systems are provided to address the complexity of tuning plasma reactors during processing to achieve desired processing performance and maintain this performance over time”; Examiner notes that the dynamic nature of a plasma source would be understood by one of ordinary skill in the art.)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify CHOI, as modified by GUHA and KANEKO, as taught above, to include that the machine learning component is configured to automatically develop an algorithm to set a threshold and to include the concept of a pending system fault event is probable to a defined degree of confidence within a specified window of time, such is further disclosed by GUHA because it would efficient to have a pre-determined comparative value for identifying desired function and performance of a plasma source, and necessary to define a time window for evaluation and assessment of data due to the complex nature of a plasma system and sensitivity to changes in multiple parameters. One of ordinary skill would understand that implementation of a machine learning method to develop an optimized maintenance schedule based on real-time data would logically include automated processes and would see this as an advantage and motivation for combining the disclosed method of
Claims 7-8, and 19 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over CHOI (US 20140346952 A1), in view of GUHA (US 20180082826 A1) and KANEKO (US 20180151332 A1), as applied to claims 1 and 16, and further in view of DAVLIN (US 20080177397 A1).
With respect to Claims 7 and 19, CHOI, in view of GUHA and further in view of KANEKO, teaches the limitations of Claims 1 and 16.
CHOI, as modified by GUHA and KANEKO as taught above, does not teach
plurality of additional remote plasma sources;
a plurality of additional data acquisition devices,
operating data further comprises:
additional measurements from each of the plurality of additional remote plasma
sources;
additional indications of system faults from each of the remote plasma sources.
DAVLIN teaches:
plurality of additional remote plasma sources; (DAVLIN is in same technical field, [0001]: “invention relates to systems and methods utilizing inputs and outputs for purposes of controlling equipment (e.g., semiconductor process equipment), industrial machinery, processing lines and the like”; FIG.4 with [0027]:”illustrates an application of certain preferred embodiments of the present invention…may be processing equipment such as for semiconductors…and may include…plasma or reactive ion or other etching system, plasma enhanced or high temperature or other chemical vapor deposition system”; FIG. 4, with [0005]: “controllers communicate to a plurality of input/output (I/O) modules that are distributed in appropriate and desired locations in the equipment, line, environment”)
plurality of additional data acquisition devices, ([0028]: “Main controller 24 communicates with a plurality of I/O modules 26”, Examiner points to description therein of a wide range of sensor data input handled by the centralized control system)
the data further comprises: additional measurements from each of the plurality of
additional remote plasma sources; (As above, FIG. 4 with [0028])
additional indications of system faults from each of the remote plasma sources.
(FIG. 8 with [0055] “step 260, a test preferably is performed in order to detect…modules may again be polled…may be programmed to report back to the main controller that "all is ok."…step 262, a determination is made as to whether errors were detected…step 268, a determination is made as to whether any errors were detected”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify CHOI, as modified by GUHA and KANEKO, as taught above, to include a plurality fo additional remote plasma sources, and a plurality of additional data acquisition devices, and to acquire additional measurements from each of the plurality of additional remote plasma sources and to compute additional indications of system faults from each of the remote plasma sources, such as that of DAVLIN because the would allow for a more realistic manufacturing application, where multiple deposition chambers would typically be used, and expand the application from a single plasma source system as taught by CHOI, modified to include more advanced computational methods of GUHA to have the advantage of monitoring and controlling additional resources with a central controller. This would make for a more efficient and useful system and would be motivated by potential cost reduction suggested by the combination of these disclosures for a system to control and assess multiple resources.
With respect to Claim 8 CHOI, in view of GUHA and KANEKO, and further in view of DAVLIN, teaches the limitations of Claim 7.
DAVLIN further teaches:
at least some of the plurality of additional remote plasma sources are in different geographical locations. (FIG.4, with [0005]: “controllers communicate to a plurality of input/output (I/O) modules that are distributed in appropriate and desired locations in the equipment, line, environment, etc.” or [0027]: “controller 24 positioned in a location physically remote from host computer”; Examiner notes broadest reasonable interpretation of “different geographical locations” to be analogous to depiction of various equipment as located in various non-adjacent spaces, as taught by reference.)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify CHOI, as modified by GUHA and KANEKO, as taught above, to include at least some of the plurality of additional remote plasma sources are in different geographical locations, such as that of DAVLIN because it be an advantage to expand the single plasma source system of CHOI to include additional resources that would be controlled by a central controller. This would be motivated be the cost savings of a centralized computational facility in for monitoring and control of many instruments, by being above to communicate and diagnose resources that are placed in locations where they are more practically used. Using a central control system for the management of those resources is way to improve efficiency and reduce overall costs of system management. One of ordinary skill would be motivated to combine the multi-resource control method of DAVLIN with the system and method for plasma resource management of CHOI, as modified by GUHA and KANEKO to take advantage of the cost saving benefits, as suggested by DAVLIN, of centralized control, particularly when a machine learning method is used for control and analysis of a multi-resource system.
Claims 12 - 15 are rejected under 35 U.S.C. § 103 as being unpatentable over CHOI (US 20140346952 A1), in view of GUHA (US 20180082826 A1) and KANEKO (US 20180151332 A1), as applied to Claim 11 above, and further in view of SON* (KR 20150001250).
(*Examiner notes translated copy provided in action dated 06/23/2025 is used below)
With respect to Claim 12, CHOI, in view of GUHA and KANEKO, teaches the limitations of Claim 11,
CHOI, as modified by GUHA, and further modified by KANEKO, as taught above, does not teach
the threshold signifies a pending system fault event is probable to a defined degree of confidence within a specified window of time.
SON teaches:
the threshold signifies a pending system fault event is probable to a defined degree of confidence within a specified window of time. (SON is in sam technical field, Abstract: “invention relates to a system for monitoring and controlling an operation of a remote plasma generator” and “receiving measurement values of a current sensor…voltage sensor…in real time…sensing and determining whether an operation has a fault”; Pg4, bottom of page, “function of detecting in real time whether or not an operation abnormality has occurred.”; Pg4para4: “error detection and classification control system 200 of the present invention is connected to the main controller 160 to detect an operation abnormality in the main controller 160…function of stopping the operation of the process facility 300. Accordingly, when a current or voltage exceeding the operating range determined by the main power module (i.e., “threshold”)120 is detected, the operation of the process facility 300 is immediately stopped and the main power module 120 is checked”(i.e., “faulty event is probable to a defined degree”); Pg 4, bottom of page-Pg 5, top of page, “has a function of detecting in real time whether or not an operation abnormality has occurred”)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify CHOI, as modified by GUHA and KANEKO as taught above, to include wherein the threshold signifies a pending system fault event is probable to a defined degree of confidence within a specified window of time, such as that of SON.
One of ordinary skill would be motivated invention to further modify CHOI, as modified by GUHA and KANEKO as taught above, to include wherein the threshold signifies a pending system fault event is probable to a defined degree of confidence within a specified window of time, as taught by SON because it would be understood as a way to improve the ability to accurately determine a fault event. In this way, one of ordinary skill would be motivated to incorporate the teaching of SON for careful use of thresholding values to improve the system and method of CHOI as modified by GUHA and KANEKO.
With respect to Claim 13, CHOI, in view of GUHA and KANEKO, and further in view of SON, teaches the limitations of Claim 12,
GUHA further teaches:
calculating one or more indirect measurements of the one or more operating characteristics, wherein the determining is based on one or more calculated indirect measurements. (limitation taught above, parallel limitations in Claim 3, [0015])
As reasoned in Claim 3, it would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify CHOI, as modified by GUHA and KANEKO and further modified by SON, as taught above, to include calculating one or more indirect measurements of the one or more operating characteristics, wherein the determining is based on one or more calculated indirect measurements, such as that further disclosed by GUHA because using indirect information from sources in addition to directly measured parameters would result in a more reliable and accurate assessment of overall plasma system function and allow for improved ability to identify potential fault conditions or need for maintenance. One of ordinary skill would see the advantage of combining the use of indirect data, which would be readily accessible in the method/system disclosed by CHOI as modified above, in taking advantage of the full potential of a machine learning method to improve model development.
With respect to Claim 14, CHOI, in view of GUHA and KANEKO, and further in and SON, teaches the limitations of Claim 13.
GUHA further teaches:
the one or more indirect measurements comprises one or more of: a component of a plasma and chamber impedance; and a characteristic of a chamber wall. (As above, parallel limitation for Claim 4, [0015]: “receives data streams receives reactor wall surface dynamics for use by a phenomenological model that defines plasma dynamics within the processing environment”)
As resonated above for Claim 4, it would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify CHOI, as modified by GUHA and KANEKO, and further modified by DAVLIN and SON, as taught above, to have one or more of the indirect measurements comprise one or more of: a component of a plasma and chamber impedance; and a characteristic of a wall of a chamber of the remote plasma source, such as that further disclosed by GUHA because acquiring multiple data types, particularly data related to indirect characteristics within plasma chamber would have a reasonable expectation of success to result in a more accurate and reliable evaluation of overall system status and performance.
With respect to Claim 15, CHOI, in view of GUHA and KANEKO, and further in view of SON, teaches the limitations of Claim 12.
CHOI further teaches:
operating data comprises a phase between the coil voltage and the coil current, and an impedance. (As above, parallel limitations in Claim 5, [0039]: “a control circuit for impedance matching”; and [0053]: “voltage and the current measured by the voltage measurement sensors…and the current measurement sensors…are detected as a predetermined phase difference…voltage measurement sensors…and the current measurement sensors…varies in phase difference”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure as included on previous office actions.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TONI D SAUNCY whose telephone number is (703)756-4589. The examiner can normally be reached Monday - Friday 8:30 a.m. - 5:30 p.m. ET.
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/TONI D SAUNCY/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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