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 .
Claims 1-8, 11-12, 14, 16-20 are pending.
Claims 9-10, 13, 15 are withdrawn.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 4-7, 12, 14, 16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Panda et al, US patent Pub US 20220214662 A1 (hereinafter Panda).
Claim 1
Panda discloses a method of seasoning a semiconductor processing chamber, the method comprising: executing a seasoning cycle using a semiconductor processing chamber (Panda, para 97 – Initiate a seasoning process in a chamber.); determining characteristics of the semiconductor processing chamber during the seasoning cycle; providing the characteristics to a trained machine-learning model that is configured to receive characteristics of semiconductor processing chambers as an input and provide outputs relating to completion of seasoning of the semiconductor processing chambers (Panda, para 98 - Processing logic receives one or more measurements/characteristics from a set of sensors of the process chamber during and/or after the process, then processes the measurements using a trained machine learning model that has been trained to determine whether seasoning is complete.); receiving output from the trained machine-learning model generated based on the characteristics; determining that seasoning of the semiconductor processing chamber is not complete based on the output (Panda, para 99 – The output of the trained machine learning model is a yes/no indication as to whether seasoning is complete.); executing an additional seasoning cycle using the semiconductor processing chamber (Panda, para 100 - Initiate another iteration of the seasoning process.); determining additional characteristics of the semiconductor processing chamber during the additional seasoning cycle (Panda, para 100, Fig. 6 refs(600-620) - Sensor measurements/characteristics associated with performance of the process on the new substrate in an additional seasoning process are received.); providing the characteristics of the semiconductor processing chamber during the additional seasoning cycle to the trained machine-learning model (Panda, para 100, Fig. 6 refs(600-620) - Sensor measurements/characteristics associated with performance of the process on the new substrate in an additional seasoning process are processed by the trained machine learning model.); receiving additional output from the trained machine-learning model; determining that seasoning of the semiconductor processing chamber is complete based on the additional output (Panda, para 100, Fig. 6 refs(600-620) - The output of the trained machine learning model is a yes/no indication as to whether the additional seasoning is complete.); and generating an indicator identifying completion of seasoning of the semiconductor processing chamber. (Panda, para 52 - Labels indicating whether or not the process chamber was ready to return to service after the seasoning process.)
Claim 4
Panda discloses all the limitations of the base claims as outlined above.
Panda further discloses controlling the semiconductor processing chamber to establish an etch condition within the semiconductor processing chamber; and controlling the semiconductor processing chamber to establish an epitaxial growth condition within the semiconductor processing chamber. (Panda, para 25-26 – Controlling the process chamber to perform etch processes and deposition processes/”epitaxial growth conditions”.)
Claim 5
Panda discloses all the limitations of the base claims as outlined above.
Panda further discloses the characteristics comprise one or more of a temperature within the semiconductor processing chamber during the seasoning cycle, a thickness of an epitaxial layer generated within the semiconductor processing chamber, a power delivered to a heater associated with the semiconductor processing chamber, a power setting for a heater associated with the semiconductor processing chamber, a growth rate within the semiconductor processing chamber during the seasoning cycle, an etch rate within the semiconductor processing chamber during the seasoning cycle, an optical condition within the semiconductor processing chamber during the seasoning cycle, a pressure within the semiconductor processing chamber during the seasoning cycle, a gas composition within the semiconductor processing chamber during the seasoning cycle, a flow rate into or out of the semiconductor processing chamber during the seasoning cycle, a change in any of these, a physical or structural parameter associated with the semiconductor processing chamber, or any combination of these. (Panda, para 25-26, 29 – Aspects/characteristics of the processing chamber include temperature of various chamber components, etch rate, deposition rate and/or target layer thickness, plasma power measurements, pressure measurements, voltage measurements, current measurements, resistance measurements, time measurements, optical measurements, gas flow rates, individual gas flows/composition, and flow rate into a chamber.)
Claim 6
Panda discloses all the limitations of the base claims as outlined above.
Panda further discloses before providing the characteristics to the machine-learning model, training the machine-learning model, wherein training the machine-learning model comprises: receiving training characteristics from a plurality of executions of seasoning cycles executed by one or more semiconductor processing chambers; generating training data based on the training characteristics, wherein the training data is generated using labeling information identifying completion of seasoning of the one or more semiconductor processing chambers (Panda, para 43, 48-49 - Feeding a training dataset consisting of labeled inputs including multiple data items that each include sensor values/characteristics of prior processes/”seasoning cycles”.); and executing a supervised learning algorithm to train the machine-learning model using the training data. (Panda, para 33-34 – Using supervised machine learning to train the neural network/”machine learning model” using training data.)
Claim 7
Panda discloses all the limitations of the base claims as outlined above.
Panda further discloses the one or more semiconductor processing chambers and the semiconductor processing chamber are a same chamber (Panda, para 40 - Process chambers may be the same type of process chamber.); and determining that seasoning of the semiconductor processing chamber is complete comprises comparing characteristics of the semiconductor processing chamber with previously determined characteristics of the semiconductor processing chamber. (Panda, para 52 - trained machine learning models trained to detect recovery from maintenance may be trained from a training dataset including many different measurements generated by one or more process chambers during seasoning processes to determine whether or not the process chamber was ready to return to service after the seasoning process at which the combined sensor measurements were taken was complete.)
Claim 12
Panda discloses all the limitations of the base claims as outlined above.
Panda further discloses controlling the semiconductor processing chamber to establish an etch condition within the semiconductor processing chamber; and controlling the semiconductor processing chamber to establish an epitaxial growth condition within the semiconductor processing chamber. (Panda, para 25-26 – Controlling the process chamber to perform etch processes and deposition processes/”epitaxial growth conditions”.)
Claim 14
Panda discloses all the limitations of the base claims as outlined above.
Panda further discloses one or more sensors positioned to measure characteristics of a surface of one or more of a flow module, chamber. (Panda, para 29, 42 – Sensors measuring flow rates/modules or a substrate in the processing chamber.)
Claim 16
Panda discloses all the limitations of the base claims as outlined above.
Panda further discloses the trained machine-learning model is trained to model seasoning of the semiconductor processing chamber following a preventive maintenance event associated with the semiconductor processing chamber. (Panda, para 52 - Trained machine learning models that have been trained to detect when a process chamber has recovered (e.g., when a process chamber is ready to return to service and start processing product substrates again) after preventative maintenance or other maintenance was performed on the process chamber.)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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 non-obviousness.
Claim(s) 2-3, 8, 11, 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Panda et al, US patent Pub US 20220214662 A1 (hereinafter Panda) as applied to claims 1, 4-7, 12, 14, 16 above, and in view of Kommisetti et al, US Patent Pub US 20190348312 A1 (hereinafter Kommisetti).
Claim 2
Panda discloses all the limitations of the base claims as outlined above.
But Panda fails to specify the output indicates completion of seasoning of the semiconductor processing chamber, an expected time of completion of seasoning of the semiconductor processing chamber, an expected duration of seasoning of the semiconductor processing chamber, or an expected number of additional seasoning cycles for completion of seasoning of the semiconductor processing chamber, and wherein method further comprises generating an indicator identifying one or more of an expected time of completion of seasoning of the semiconductor processing chamber, an expected duration of seasoning of the semiconductor processing chamber, or an expected number of additional seasoning cycles for completion of seasoning of the semiconductor processing chamber.
However Kommisetti teaches the output indicates completion of seasoning of the semiconductor processing chamber, an expected time of completion of seasoning of the semiconductor processing chamber, and wherein method further comprises generating an indicator identifying an expected time of completion of seasoning of the semiconductor processing chamber. (Kommisetti, para 27-28 - The seasoning progress data can include parameter values indicating a progress of the seasoning operation and to estimate an endpoint of the seasoning process at which the process chamber is fully seasoned.)
Panda and Kommisetti are analogous art because they are from the same field of endeavor. They relate to substrate processing systems.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above substrate processing system, as taught by Panda, and incorporating the above limitations, as taught by Kommisetti.
One of ordinary skill in the art would have been motivated to do this modification in order to determine when the system may begin resuming normal production processes by incorporating the above limitations, as suggested by Kommisetti (para 4).
Claim 3
Panda discloses all the limitations of the base claims as outlined above.
But Panda fails to specify generating an indicator identifying an expected time of completion of seasoning of the semiconductor processing chamber, an expected duration of seasoning of the semiconductor processing chamber, or an expected number of additional seasoning cycles for completion of seasoning of the semiconductor processing chamber.
However Kommisetti teaches generating an indicator identifying an expected time of completion of seasoning of the semiconductor processing chamber. (Kommisetti, para 27-28 - The seasoning progress data can include parameter values indicating a progress of the seasoning operation and to estimate an endpoint of the seasoning process at which the process chamber is fully seasoned.)
Panda and Kommisetti are analogous art because they are from the same field of endeavor. They relate to substrate processing systems.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above substrate processing system, as taught by Panda, and incorporating the above limitations, as taught by Kommisetti.
One of ordinary skill in the art would have been motivated to do this modification in order to determine when the system may begin resuming normal production processes by incorporating the above limitations, as suggested by Kommisetti (para 4).
Claim 8
Panda discloses all the limitations of the base claims as outlined above.
But Panda fails to specify the machine learning model compares characteristics for a seasoning cycle with corresponding characteristics for an immediately previous seasoning cycle to evaluate completion of seasoning of the semiconductor processing chamber.
However Kommisetti teaches the machine learning model compares characteristics for a seasoning cycle with corresponding characteristics for an immediately previous seasoning cycle to evaluate completion of seasoning of the semiconductor processing chamber. (Kommisetti, para 27-28, 52 – A machine learning algorithm that analyzes progress data in the seasoning progress data storage is progress data for previously completed seasoning processes.)
Panda and Kommisetti are analogous art because they are from the same field of endeavor. They relate to substrate processing systems.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above substrate processing system, as taught by Panda, and incorporating the above limitations, as taught by Kommisetti.
One of ordinary skill in the art would have been motivated to do this modification in order to minimize errors by incorporating the above limitations, as suggested by Kommisetti (para 52).
Claim 11
Panda discloses all the limitations of the base claims as outlined above.
But Panda fails to specify the characteristics comprise a comparison using a first time series of data obtained from the one or more sensors during a first seasoning cycle and a second time series of data obtained from the one or more sensors during a second seasoning cycle immediately previous to the first seasoning cycle.
However Kommisetti teaches the characteristics comprise a comparison using a first time series of data obtained from the one or more sensors during a first seasoning cycle and a second time series of data obtained from the one or more sensors during a second seasoning cycle immediately previous to the first seasoning cycle. (Kommisetti, para 27-28, 52 – A machine learning algorithm that analyzes progress data in the seasoning progress data storage is progress data for previously completed seasoning processes. The seasoning progress data can include any parameter values indicating a progress of the seasoning operation at various times during the processing of each substrate of the first plurality of substrates)
Panda and Kommisetti are analogous art because they are from the same field of endeavor. They relate to substrate processing systems.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above substrate processing system, as taught by Panda, and incorporating the above limitations, as taught by Kommisetti.
One of ordinary skill in the art would have been motivated to do this modification in order to minimize errors by incorporating the above limitations, as suggested by Kommisetti (para 52).
Claim 18
Panda discloses all the limitations of the base claims as outlined above.
But Panda fails to specify output from the trained machine-learning model comprises an indication of completion or non-completion of seasoning of the semiconductor processing chamber and one or more variance values determined by comparing the characteristics of the semiconductor processing chamber during a first seasoning cycle with characteristics of the semiconductor processing chamber during a second seasoning cycle immediately previous to the first seasoning cycle.
However Kommisetti teaches output from the trained machine-learning model comprises an indication of completion or non-completion of seasoning of the semiconductor processing chamber and one or more variance values determined by comparing the characteristics of the semiconductor processing chamber during a first seasoning cycle with characteristics of the semiconductor processing chamber during a second seasoning cycle immediately previous to the first seasoning cycle. (Kommisetti, para 38-39, 52 – A machine learning algorithm that analyzes progress data in the seasoning progress data storage is progress data for previously completed seasoning processes. The process chamber is considered seasoned when a variance in the measured parameters of the predetermined process have stabilized within a threshold or range)
Panda and Kommisetti are analogous art because they are from the same field of endeavor. They relate to substrate processing systems.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above substrate processing system, as taught by Panda, and incorporating the above limitations, as taught by Kommisetti.
One of ordinary skill in the art would have been motivated to do this modification in order to minimize errors by incorporating the above limitations, as suggested by Kommisetti (para 52).
Claim 19
Panda discloses all the limitations of the base claims as outlined above.
But Panda fails to specify determining that seasoning of the semiconductor processing chamber is complete based on the additional output comprises: deriving a variance value between the output and the additional output; and determining that the variance value is less than a target variance value.
However Kommisetti teaches determining that seasoning of the semiconductor processing chamber is complete based on the additional output comprises: deriving a variance value between the output and the additional output; and determining that the variance value is less than a target variance value. (Kommisetti, para 38-39, 52 – A machine learning algorithm that analyzes progress data in the seasoning progress data storage is progress data for previously completed seasoning processes. The process chamber is considered not seasoned when a variance in the measured parameters of the predetermined process is not within a threshold or range.)
Panda and Kommisetti are analogous art because they are from the same field of endeavor. They relate to substrate processing systems.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above substrate processing system, as taught by Panda, and incorporating the above limitations, as taught by Kommisetti.
One of ordinary skill in the art would have been motivated to do this modification in order to minimize errors by incorporating the above limitations, as suggested by Kommisetti (para 52).
Claim 20
Panda discloses all the limitations of the base claims as outlined above.
Kommisetti further teaches receiving input corresponding to a tolerance to the trained machine-learning model that indicates an allowed deviation from the target variance value. (Kommisetti, para 38-39, 52 – A machine learning algorithm that analyzes progress data in the seasoning progress data storage is progress data for previously completed seasoning processes. The process chamber is considered not seasoned when a variance in the measured parameters of the predetermined process is not within a predetermined/input threshold or range/”allowed deviation from the target variance value”.)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above substrate processing system, as taught by Panda and Kommisetti, and incorporating the above limitations, as taught by Kommisetti.
One of ordinary skill in the art would have been motivated to do this modification in order to minimize errors by incorporating the above limitations, as suggested by Kommisetti (para 52).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Panda et al, US patent Pub US 20220214662 A1 (hereinafter Panda) as applied to claims 1, 4-7, 12, 14, 16 above, and in view of Funk et al, US Patent Pub US 20050159911 A1 (hereinafter Funk).
Claim 17
Panda discloses all the limitations of the base claims as outlined above.
But Panda fails to specify obtaining user inputs corresponding to one or more of identification of a duration of the chamber open event or the preventive maintenance event, a change in components of the semiconductor processing chamber during the chamber open event or the preventive maintenance event, environmental conditions during the chamber open event or the preventive maintenance event, maintenance procedures or protocols applied to the semiconductor processing chamber during the chamber open event or the preventive maintenance event, or a severity rating for the chamber open event or the preventive maintenance event, wherein the trained machine-learning model is further configured to use the user inputs when generating the outputs relating to completion of seasoning of the semiconductor processing chambers.
However Funk teaches obtaining user inputs corresponding to maintenance procedures or protocols applied to the semiconductor processing chamber during the preventive maintenance event. (Funk, para 124 – User inputs relating to chamber cleaning or maintenance procedures.)
Panda and Funk are analogous art because they are from the same field of endeavor. They relate to substrate processing systems.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above substrate processing system, as taught by Panda, and incorporating the above limitations, as taught by Funk.
One of ordinary skill in the art would have been motivated to do this modification in order to allow the user is able to perform the desired configuration and setup tasks with as little input as possible by incorporating the above limitations, as suggested by Funk (abstract).
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Guha et al US Patent Pub US 20180082826 A1 relates to claims regarding controlling a processing state of a plasma reactor to initiate processing of production substrates, to determine a ready state of a reactor after the reactor has been cleaned and needs to be seasoned for subsequent production wafer processing, and sensor detected operational conditions of the plasma reactor.
Choi et al, US Patent Pub US 20100332010 A1 relates to claims regarding seasoning a plasma processing chamber, and parameter values derived from signals sensed by sensors.
Guha et al, US Patent Pub US 20180247798 A1 relates to claims regarding controlling a processing state of a plasma process, a plurality of sensors configured to produce a data stream of information during operation of the plasma reactor, and machine learning engine that receives as inputs the desired processing state values and data streams from the plurality of sensors during processing of the plasma process.
Conclusion
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/DAVID EARL OGG/
Primary Examiner, Art Unit 2119
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