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
The claims submitted on 10/15/2025 are being examined in this office action. Claims 11-14 are cancelled and claims 21-24 are newly added.
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.
Claim(s) 1-5, 7-10, 15-18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cinar et al (US PUB. 20190109029, herein Cinar) in view of Rayner (US PUB. 20120269968).
Regarding claim 1, Cinar teaches A thin-film deposition system, comprising:
one or more memories configured to store software instructions (0023 “system 100 of FIG. 1 may comprise a semiconductor device processing system 110 and various controllers described below. The semiconductor device processing system 110 may manufacture integrated circuit devices based upon data and/or instructions provided by an in-situ process controller 140 and/or a run to run controller 160. One or more of the processing steps performed by the processing system 110 may be controlled by the in-situ process controller 140 and/or the run to run controller 160. Each of the in-situ process controller 140 and the run to run controller 160 may be a workstation computer, a desktop computer, a laptop computer, a tablet computer, or any other type of computing device comprising one or more software products that are capable of controlling processes, receiving process feedback, receiving gas analysis data, performing learning cycle adjustments, performing process adjustments, etc”);
one or more processors configured to execute the software instructions to perform a process (0023), the process including:
depositing, with an atomic layer deposition process, a first portion of a layer on a structure on a semiconductor wafer (0033 “system 100 may also comprise a data analysis module 120 which is capable of receiving data from the gas analyzer 130 and the metrology tools 113. Gas resulting from the processing steps performed by a processing tool 114 (e.g., exhaust gas and/or residue gas from ALD or ALE processes) is provided to the gas analyzer 130. The processing tool 114 may be an atomic layer process tool, e.g., an atomic layer deposition tool or an atomic layer etch tool”);
generating, with a sensor, sensor data indicating one or more dynamic process conditions present while depositing the first portion of the layer (0033 “system 100 may also comprise a data analysis module 120 which is capable of receiving data from the gas analyzer 130 and the metrology tools 113. Gas resulting from the processing steps performed by a processing tool 114 (e.g., exhaust gas and/or residue gas from ALD or ALE processes) is provided to the gas analyzer 130. The processing tool 114 may be an atomic layer process tool, e.g., an atomic layer deposition tool or an atomic layer etch tool”);
generating, with the analysis model, first predicted layer data based on the [static process conditions data] and the first dynamic process conditions data (0052 “A signal comprising run to run process adjustments may be provided to the processing tool 310 for performing run to run process control adjustment. In some embodiments, the 2.sup.nd process model 375 may also provide a feedback signal comprising adjusted dosage to the dose controller 320 on a run to run basis”, 0053 “the interaction between the run to run controller 360 and the dosage controller 320 may provide a supervisory function in performing ALD and/or ALE processes. The run to run controller 360 and the dosage controller 320 may be used to perform a prediction function for predicting film growth rates for the run to run controller 360. This prediction function may be used to perform feedback adjustment for subsequent run of wafers”, 0049 “the system 300 may also comprise a data module 380 capable of receiving and processing data from the gas analyzer 330. The data module 380 is capable of collecting gas analysis data, organizing/correlating the data, and providing the data to process models. The data module 380 may comprise a plurality of memory portions capable of separately storing wafer to wafer gas analysis data on one memory portion, and storing run to run gas analysis data in another memory portion. In some embodiments, the data module 380 may convert gas exhaust data to layer thickness data relating to a deposition layer or an etch layer”);
comparing the first predicted layer data to target layer data (0054 “the 1.sup.st and 2.sup.nd process models 370, 375 comprise a calibration look-up table that may be used to perform a correlation between the dosage for an ALD or ALE process and the linear atomic mass unit intensity measurement and/or the sum of the linear sum of all measured liner atomic mass unit intensity measurements. These atomic mass unit intensity measurements may be used to determine a film thickness and/or film thickness growth rate, which in turn, may be used to perform feedback adjustments, e.g., run to run feedback adjustments”);
if the first predicted layer data matches the target layer data, depositing a second portion of the layer by continuing the atomic layer deposition process with the dynamic process conditions (0031 “the semiconductor device processing system 110 may comprise additional metrology tools 113 in addition to the gas analyzer 130. In some embodiments, data from the additional metrology tools 113 may be used in conjunction with the data from the gas analyzer 130 to perform wafer to wafer feedback process adjustments and/or run to run feedback process adjustments” 0020 “performing a wafer to wafer feedback control of a deposition and/or an etch process performed on semiconductor wafers. Embodiments herein provide for utilizing a gas analyzer for acquiring data to perform a wafer to wafer feedback process adjustment during processing of semiconductor wafers”, 0021 “in an atomic layer deposition (ALD) process or in an atomic layer etch process (ALE), a precursor gas may be applied to a chamber or reactor. After a predetermined period of time, data relating to exhaust gas and/or residue precursor gas may be measured. Based on these measurements, a dosage adjustment of the precursor gas or another fluid may be performed in a wafer to wafer manner to provide a more consistent dosage application for the ALD and/or ALE processes across a plurality of wafers”);
and if the first predicted layer data does not match the target layer data, generating adjusting dynamic process conditions adjustment data and depositing the second portion of the layer by adjusting the atomic layer deposition process based on the dynamic process conditions adjustment data (0020 “performing a wafer to wafer feedback control of a deposition and/or an etch process performed on semiconductor wafers. Embodiments herein provide for utilizing a gas analyzer for acquiring data to perform a wafer to wafer feedback process adjustment during processing of semiconductor wafers”, 0021 “in an atomic layer deposition (ALD) process or in an atomic layer etch process (ALE), a precursor gas may be applied to a chamber or reactor. After a predetermined period of time, data relating to exhaust gas and/or residue precursor gas may be measured. Based on these measurements, a dosage adjustment of the precursor gas or another fluid may be performed in a wafer to wafer manner to provide a more consistent dosage application for the ALD and/or ALE processes across a plurality of wafers”).
The cited prior art do not teach providing static process conditions data to an analysis model based on characteristics of the structure.
Rayner teaches providing static process conditions data to an analysis model based on characteristics of the structure (0007 “Ideal growth is characterized by chemical adsorption of surface species that is irreversible, self-limiting, and complete. Outside of this temperature window, growth becomes non-ideal”, 0009 “Sequential precursor pulsing eliminates the potential for gas-phase reactions that result in film defects so that highly reactive precursors can be utilized. Highly reactive precursors yield dense, continuous films with low levels of residual contamination and defects at relatively low process temperatures. High quality materials demonstrated include oxides, nitrides, metals, as well as complex multi-component films and multi-layered structures”, deposition materials and substrate surface as static dynamics)
generating, with the analysis model, first predicted layer data (taught by Cinar) based on the static process conditions data (0018 “apparatus design features include real-time process monitoring and cluster tool integration capabilities. Like plasma, the integration of these features increases chamber design complexity and volume. Spectroscopic ellipsometry (SE) is a powerful and effective method for real-time process control and monitoring. SE is a non-destructive thin film characterization technique that utilizes specularly-reflected polarized light to determine properties such as film thickness and optical constants”, 0029 “a modeling system for an atomic layer deposition apparatus having a primary dispersion member”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to have modified the atomic deposition modelling teachings of Cinar with the static sensor data for modelling teachings of Rayner since Rayner teaches a means for using sequential precursor pulsing which “eliminates the potential for gas-phase reactions that result in film defects so that highly reactive precursors can be utilized” and also allows for “simplifying scale-up from research and development to production” (0009).
Regarding claim 2, the cited prior art teach The system of claim 1.
The cited prior art teach wherein the process includes, if the first predicted thin-film data does not match the target thin-film data (Cinar 0008 “A first gas analysis of a gas in the processing tool is performed upon performing the first process. Determining a process feedback adjustment based upon a result of the first gas analysis”, 0053 “The run to run controller 360 and the dosage controller 320 may be used to perform a prediction function for predicting film growth rates for the run to run controller 360. This prediction function may be used to perform feedback adjustment for subsequent run of wafers” abstract “A first gas analysis of a gas in the processing tool is performed upon performing the first process. Determining a process feedback adjustment based upon a result of the first gas analysis. Data relating to the process feedback adjustment is provided. Performing a second process on a second semiconductor wafer based on the data relating to the process feedback adjustment”):
generating second predicted thin-film data based on the adjusted first dynamic process conditions data (Cinar 0053 “The run to run controller 360 and the dosage controller 320 may be used to perform a prediction function for predicting film growth rates for the run to run controller 360. This prediction function may be used to perform feedback adjustment for subsequent run of wafers” abstract “A first gas analysis of a gas in the processing tool is performed upon performing the first process. Determining a process feedback adjustment based upon a result of the first gas analysis. Data relating to the process feedback adjustment is provided. Performing a second process on a second semiconductor wafer based on the data relating to the process feedback adjustment”);
comparing second predicted thin-film data to the target thin-film data (Cinar 0054 “the 1.sup.st and 2.sup.nd process models 370, 375 comprise a calibration look-up table that may be used to perform a correlation between the dosage for an ALD or ALE process and the linear atomic mass unit intensity measurement and/or the sum of the linear sum of all measured liner atomic mass unit intensity measurements. These atomic mass unit intensity measurements may be used to determine a film thickness and/or film thickness growth rate, which in turn, may be used to perform feedback adjustments, e.g., run to run feedback adjustments”);
and if the second predicted thin-film data matches the target thin-film data, performing the thin-film deposition process with process conditions based on the static process conditions data (Raynar 0007 “Ideal growth is characterized by chemical adsorption of surface species that is irreversible, self-limiting, and complete. Outside of this temperature window, growth becomes non-ideal”, 0009 “Sequential precursor pulsing eliminates the potential for gas-phase reactions that result in film defects so that highly reactive precursors can be utilized. Highly reactive precursors yield dense, continuous films with low levels of residual contamination and defects at relatively low process temperatures. High quality materials demonstrated include oxides, nitrides, metals, as well as complex multi-component films and multi-layered structures”, deposition materials and substrate surface as static dynamics) and the adjusted first dynamic process conditions data (Cinar 0020 “performing a wafer to wafer feedback control of a deposition and/or an etch process performed on semiconductor wafers. Embodiments herein provide for utilizing a gas analyzer for acquiring data to perform a wafer to wafer feedback process adjustment during processing of semiconductor wafers”, 0021 “in an atomic layer deposition (ALD) process or in an atomic layer etch process (ALE), a precursor gas may be applied to a chamber or reactor. After a predetermined period of time, data relating to exhaust gas and/or residue precursor gas may be measured. Based on these measurements, a dosage adjustment of the precursor gas or another fluid may be performed in a wafer to wafer manner to provide a more consistent dosage application for the ALD and/or ALE processes across a plurality of wafers”).
Regarding claim 3, the cited prior art teach The system of claim 1.
Cinar teaches wherein the thin-film deposition process is an atomic layer deposition process (0033 “system 100 may also comprise a data analysis module 120 which is capable of receiving data from the gas analyzer 130 and the metrology tools 113. Gas resulting from the processing steps performed by a processing tool 114 (e.g., exhaust gas and/or residue gas from ALD or ALE processes) is provided to the gas analyzer 130. The processing tool 114 may be an atomic layer process tool, e.g., an atomic layer deposition tool or an atomic layer etch tool”).
Regarding claim 4, the cited prior art teach The system of claim 3.
Cinar teaches wherein performing the thin-film deposition process includes performing a first cycle of the atomic layer deposition process (abstract “A first gas analysis of a gas in the processing tool is performed upon performing the first process. Determining a process feedback adjustment based upon a result of the first gas analysis. Data relating to the process feedback adjustment is provided. Performing a second process on a second semiconductor wafer based on the data relating to the process feedback adjustment”).
Regarding claim 5, the cited prior art teach The system of claim 4.
Cinar teaches wherein the process includes, after the first cycle: identifying, with the analysis model, second dynamic process conditions data; and performing a second cycle of the atomic layer deposition process based on the static process conditions data and the second dynamic process conditions data (0052 “run to run controller 360, which may be capable of receiving growth rate (for an ALD process) and/or layer declining rate (for an ALE process) from the 2.sup.nd process model. Based on the layer-growth rate or a layer-decline rate, the run to run controller 360 may provide feedback to perform process adjustments (e.g., dosage changes, etc.). The run to run controller 360 may adjust the cycle or temperature based on measured film thickness and data from the 2.sup.nd process model 375. A signal comprising run to run process adjustments may be provided to the processing tool 310 for performing run to run process control adjustment. In some embodiments, the 2.sup.nd process model 375 may also provide a feedback signal comprising adjusted dosage to the dose controller 320 on a run to run basis”).
Regarding claim 7, the cited prior art teach The system of claim 1.
Raynar teaches wherein the static process conditions data includes one or more of: a deposition material; features of a deposition surface; and an age of deposition equipment (0007 “Ideal growth is characterized by chemical adsorption of surface species that is irreversible, self-limiting, and complete. Outside of this temperature window, growth becomes non-ideal. At temperatures below the ALD window, thermal energy at the substrate surface becomes insufficient for surface reactions and/or to prevent physical adsorption (or condensation) of precursor molecules. Thermal self-decomposition of precursor molecules and/or desorption of chemically-adsorbed surface species result in non-ideal growth at higher substrate temperatures. In many cases, the ALD window is broad enough to enable ALD of different materials (e.g., multilayer film growth) at a constant substrate temperature”, deposition material as static process condition).
Regarding claim 8, the cited prior art teach The system of claim 7.
Raynar teaches wherein the first dynamic process conditions data includes one or more of: a flow rate of the deposition material; a duration of flow of the deposition material; a pressure in a deposition chamber; a temperature in the deposition chamber; and a humidity in the deposition chamber (0044 “FIG. 11 is a chart illustrating reactant diffusion length for various flow rates during operation of one embodiment of an atomic vapor deposition apparatus according to the principles of the present invention”, flow rates).
Regarding claim 9, the cited prior art teach The system of claim 1.
Cinar teaches wherein the target thin-film data identifies a target thin-film thickness (0052 “The run to run controller 360 may adjust the cycle or temperature based on measured film thickness and data from the 2.sup.nd process model 375. A signal comprising run to run process adjustments may be provided to the processing tool 310 for performing run to run process control adjustment.”, 0054 “comprise a calibration look-up table that may be used to perform a correlation between the dosage for an ALD or ALE process and the linear atomic mass unit intensity measurement and/or the sum of the linear sum of all measured liner atomic mass unit intensity measurements. These atomic mass unit intensity measurements may be used to determine a film thickness and/or film thickness growth rate, which in turn, may be used to perform feedback adjustments, e.g., run to run feedback adjustments”).
Regarding claim 10, the cited prior art teach The system of claim 9.
Cinar teaches wherein the target thin-film data identifies a target thin-film thickness range (0052 “The run to run controller 360 may adjust the cycle or temperature based on measured film thickness and data from the 2.sup.nd process model 375. A signal comprising run to run process adjustments may be provided to the processing tool 310 for performing run to run process control adjustment.”, 0054 “comprise a calibration look-up table that may be used to perform a correlation between the dosage for an ALD or ALE process and the linear atomic mass unit intensity measurement and/or the sum of the linear sum of all measured liner atomic mass unit intensity measurements. These atomic mass unit intensity measurements may be used to determine a film thickness and/or film thickness growth rate, which in turn, may be used to perform feedback adjustments, e.g., run to run feedback adjustments”).
Regarding claim 15, Cinar teaches A thin-film deposition system, comprising:
a thin-film deposition chamber (0021 “an atomic layer deposition (ALD) process or in an atomic layer etch process (ALE), a precursor gas may be applied to a chamber or reactor”);
a fluid source (108) configured to provide a fluid into the thin-film deposition chamber during a thin-film deposition process (0020 “a gas analyzer may be used to acquire feedback data relating to a fluid used for a deposition or an etch process for performing process adjustments in a wafer to wafer manner. Those skilled in the art would appreciate that the fluid may relate to a chemical in a gaseous form or in a liquid form”);
and a control system (124) configured to identify process conditions data for the thin-film deposition process based on a machine learning process and to control the first fluid source during the thin-film deposition process in accordance with the process conditions data (0021 “a dosage adjustment of the precursor gas or another fluid may be performed in a wafer to wafer manner to provide a more consistent dosage application for the ALD and/or ALE processes across a plurality of wafers”).
The cited prior art do not teach a support configured to support a substrate within the thin-film deposition chamber.
Rayner teaches a support configured to support a substrate within the thin-film deposition chamber (0051 “fixture assembly 18 is positioned in the internal volume 16 of the chamber 10 and is configured to hold or support a substrate S within the internal volume 16 of the chamber”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to have modified the atomic deposition modelling teachings of Cinar with the static sensor data for modelling teachings of Rayner since Rayner teaches a means for using sequential precursor pulsing which “eliminates the potential for gas-phase reactions that result in film defects so that highly reactive precursors can be utilized” and also allows for “simplifying scale-up from research and development to production” (0009).
Regarding claim 16, the cited prior art teach The system of claim 15.
Cinar teaches wherein the control system includes an analysis model, wherein the analysis model is configured to identify the process conditions data (0052 “A signal comprising run to run process adjustments may be provided to the processing tool 310 for performing run to run process control adjustment. In some embodiments, the 2.sup.nd process model 375 may also provide a feedback signal comprising adjusted dosage to the dose controller 320 on a run to run basis”, 0053 “the interaction between the run to run controller 360 and the dosage controller 320 may provide a supervisory function in performing ALD and/or ALE processes. The run to run controller 360 and the dosage controller 320 may be used to perform a prediction function for predicting film growth rates for the run to run controller 360. This prediction function may be used to perform feedback adjustment for subsequent run of wafers”, 0049 “the system 300 may also comprise a data module 380 capable of receiving and processing data from the gas analyzer 330. The data module 380 is capable of collecting gas analysis data, organizing/correlating the data, and providing the data to process models. The data module 380 may comprise a plurality of memory portions capable of separately storing wafer to wafer gas analysis data on one memory portion, and storing run to run gas analysis data in another memory portion. In some embodiments, the data module 380 may convert gas exhaust data to layer thickness data relating to a deposition layer or an etch layer”).
Regarding claim 17, The cited prior art teach The system of claim 16.
Cinar teaches wherein the analysis model is configured to receive target thin-film data indicating target parameters of the thin film and to identify the process conditions data by generating predicted thin-film data that complies with the target thin- film data (0054 “the 1.sup.st and 2.sup.nd process models 370, 375 comprise a calibration look-up table that may be used to perform a correlation between the dosage for an ALD or ALE process and the linear atomic mass unit intensity measurement and/or the sum of the linear sum of all measured liner atomic mass unit intensity measurements. These atomic mass unit intensity measurements may be used to determine a film thickness and/or film thickness growth rate, which in turn, may be used to perform feedback adjustments, e.g., run to run feedback adjustments”).
Regarding claim 18, the cited prior art teach The system of claim 17.
Raynar teaches wherein the analysis model is configured to receive static process conditions data and to identify the process conditions data based on the static process conditions data and the target thin-film data (0007 “Ideal growth is characterized by chemical adsorption of surface species that is irreversible, self-limiting, and complete. Outside of this temperature window, growth becomes non-ideal”, 0009 “Sequential precursor pulsing eliminates the potential for gas-phase reactions that result in film defects so that highly reactive precursors can be utilized. Highly reactive precursors yield dense, continuous films with low levels of residual contamination and defects at relatively low process temperatures. High quality materials demonstrated include oxides, nitrides, metals, as well as complex multi-component films and multi-layered structures”, deposition materials and substrate surface as static dynamics).
Regarding claim 20, the cited prior art teach The system of claim 15.
Cinar teaches wherein the thin-film deposition process is an atomic layer deposition process (0033 “system 100 may also comprise a data analysis module 120 which is capable of receiving data from the gas analyzer 130 and the metrology tools 113. Gas resulting from the processing steps performed by a processing tool 114 (e.g., exhaust gas and/or residue gas from ALD or ALE processes) is provided to the gas analyzer 130. The processing tool 114 may be an atomic layer process tool, e.g., an atomic layer deposition tool or an atomic layer etch tool”).
Claim(s) 6 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cinar et al (US PUB. 20190109029, herein Cinar) in view of Rayner (US PUB. 20120269968) in further view of Chau et al (US PUB. 20220171373, herein Chau).
Regarding claim 6, the cited prior art teach The system of claim 1.
The cited prior art do not teach wherein the analysis model includes a neural network.
Chau teaches wherein the analysis model includes a neural network (0031 “the instructions are configured to train the model using a machine learning method including an artificial neural network and support vector regression”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to have modified the teachings of Cinar and Rayner with the model based substrate processing systems of Chau since Chau teaches a means for using neural network based models trained using machine learning which allows for improving accuracy of scheduler pacing and achieves highest tool/fleet utilization, shortest wait times and fastest throughput (abstract).
Regarding claim 19, the cited prior art teach The system of claim 15.
The cited prior art do not teach wherein an analysis model includes a neural network.
Chau teaches wherein an analysis model includes a neural network (0031 “the instructions are configured to train the model using a machine learning method including an artificial neural network and support vector regression”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to have modified the teachings of Cinar and Rayner with the model based substrate processing systems of Chau since Chau teaches a means for using neural network based models trained using machine learning which allows for improving accuracy of scheduler pacing and achieves highest tool/fleet utilization, shortest wait times and fastest throughput (abstract).
Claim(s) 21-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cinar et al (US PUB. 20190109029, herein Cinar) in view of Chau et al (US PUB. 20220171373, herein Chau).
Regarding claim 21, Cinar teaches A control system for thin-film deposition, comprising:
and the analysis model, configured to: receive target thin-film data (0054 “the 1.sup.st and 2.sup.nd process models 370, 375 comprise a calibration look-up table that may be used to perform a correlation between the dosage for an ALD or ALE process and the linear atomic mass unit intensity measurement and/or the sum of the linear sum of all measured liner atomic mass unit intensity measurements. These atomic mass unit intensity measurements may be used to determine a film thickness and/or film thickness growth rate, which in turn, may be used to perform feedback adjustments, e.g., run to run feedback adjustments”);
and identify process conditions data that results in predicted thin-film data that complies with the target thin-film data, wherein the control system is configured to control a thin-film deposition process on a semiconductor wafer with deposition process conditions in accordance with the process conditions data (0020 “performing a wafer to wafer feedback control of a deposition and/or an etch process performed on semiconductor wafers. Embodiments herein provide for utilizing a gas analyzer for acquiring data to perform a wafer to wafer feedback process adjustment during processing of semiconductor wafers”, 0021 “Based on these measurements, a dosage adjustment of the precursor gas or another fluid may be performed in a wafer to wafer manner to provide a more consistent dosage application for the ALD and/or ALE processes across a plurality of wafers”, 0052 “A signal comprising run to run process adjustments may be provided to the processing tool 310 for performing run to run process control adjustment. In some embodiments, the 2.sup.nd process model 375 may also provide a feedback signal comprising adjusted dosage to the dose controller 320 on a run to run basis”, 0053 “the interaction between the run to run controller 360 and the dosage controller 320 may provide a supervisory function in performing ALD and/or ALE processes. The run to run controller 360 and the dosage controller 320 may be used to perform a prediction function for predicting film growth rates for the run to run controller 360. This prediction function may be used to perform feedback adjustment for subsequent run of wafers”).
The cited prior art do not teach a training module configured to train an analysis model with a machine learning process to predict characteristics of thin films.
Chau teaches a training module configured to train an analysis model with a machine learning process to predict characteristics of thin films (0031 “the instructions are configured to train the model using a machine learning method including an artificial neural network and support vector regression”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to have modified the teachings of Cinar with the model based substrate processing systems of Chau since Chau teaches a means for using neural network based models trained using machine learning which allows for improving accuracy of scheduler pacing and achieves highest tool/fleet utilization, shortest wait times and fastest throughput (abstract).
Regarding claim 22, the cited prior art teach The control system of claim 21.
Chau teaches further comprising: a storage device that stores training set data, wherein the training module is configured to train the analysis model using the training set data (0027 “a system for processing semiconductor substrates in a tool comprising a plurality of processing chambers configured to process the semiconductor substrates according to a recipe, comprises a processor and memory storing instructions for execution by the processor”, 0032 “the instructions are configured to analyze the historical data received from the tool and data generated by simulating the plurality of processing scenarios for the tool; detect, based on the analysis, patterns regarding preventive maintenance operations, wafer-less auto clean times, wait times, recipe times, and throughput for the tool; and train the model based on the detected patterns”).
Regarding claim 23, the cited prior art teach The control system of claim 22.
The cited prior art teach wherein the training set data includes historical thin-film data identifying characteristics of previously deposited thin films (Cinar 0054 “the 1.sup.st and 2.sup.nd process models 370, 375 comprise a calibration look-up table that may be used to perform a correlation between the dosage for an ALD or ALE process and the linear atomic mass unit intensity measurement and/or the sum of the linear sum of all measured liner atomic mass unit intensity measurements. These atomic mass unit intensity measurements may be used to determine a film thickness and/or film thickness growth rate, which in turn, may be used to perform feedback adjustments, e.g., run to run feedback adjustments”), wherein the training set data includes historical process conditions data identifying historical process conditions associated with the previously deposited thin films (Chau 0032 “the instructions are configured to analyze the historical data received from the tool and data generated by simulating the plurality of processing scenarios for the tool; detect, based on the analysis, patterns regarding preventive maintenance operations, wafer-less auto clean times, wait times, recipe times, and throughput for the tool; and train the model based on the detected patterns”).
Regarding claim 24, the cited prior art teach The control system of claim 23.
The cited prior art teach wherein the control system is configured to perform a data mining (Chau 0142 “the data analyzer 406 detects patterns regarding PMs, WAC times, wait times, recipe times, and throughput for the tool(s) based on the analysis of the collected data. At 588, the data analyzer 406 detects tool-to-tool variations and also same tool variations described above. At 590, the data analyzer 406 provides the detected patterns and variations to the model generator 408 for use in model training using machine learning”) process on a thin-film deposition database to obtain the training set data (Cinar 0054 “the 1.sup.st and 2.sup.nd process models 370, 375 comprise a calibration look-up table that may be used to perform a correlation between the dosage for an ALD or ALE process and the linear atomic mass unit intensity measurement and/or the sum of the linear sum of all measured liner atomic mass unit intensity measurements. These atomic mass unit intensity measurements may be used to determine a film thickness and/or film thickness growth rate, which in turn, may be used to perform feedback adjustments, e.g., run to run feedback adjustments”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAMEEM SIDDIQUEE whose telephone number is (571)272-1627. The examiner can normally be reached M-F 8:00-4:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamini Shah can be reached at 571-272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/TAMEEM D SIDDIQUEE/
Primary Examiner
Art Unit 2116