DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office action is responsive to the communication filed on 08/19/2026. The claim(s) 1-7 & 21- 33 is/are pending, of which the claim(s) 1, 21, & 28 is/are in independent form.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The outstanding non-statutory and statutory double patenting rejections have been rendered moot and hence withdrawn due to abandonment of the co-pending application # 18/244113.
Response to Arguments
Applicant’s arguments, see Remarks, filed 08/19/2026 with respect to the amended limitations of the independent claims have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of discovery of new prior art as set forth below.
Claim Rejections - 35 USC § 103
Claim(s) 1-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hilkene et al. (US 20220084842 A1) in view of Roham et al. (US 20240378347 A1, Filing Date: 2022-01-10), and further in view of Hao et al. (US 20200243359 A1).
Note: Hilkene and Roham are reference cited during the Office action mailed on 03/17/2026. The combination of Hilkene, Roham, and Hao is referred to as HRH hereinafter.
Regarding claim 1, Hilkene teaches a digital twin system [Figs. 4A- 4B, “data model 520 serves as a digital twin of the physical chamber 500”] for controlling a physical twin chamber [“a chamber”/ “physical processing tool”, e.g., item 105/205/305] configured to process substrates, the digital twin system comprising: ([019, 040, 049]);
a digital twin device [a “data model server” like item “420”) that implements the “data model” or “a digital twin of the physical processing tool” and includes “virtual sensor module 468” of Fig. 4B (wherein “the data model server 420 may be external to the processing tool 400” and “the virtual sensor module 468 may be implemented as part of the data model server 420”)] determining characteristics [“output data from the various witness sensors 445 may be provided directly to the data model server 420” and information from “sensors 447 may include control loop sensors”] of a physical twin chamber [“the physical processing tool”, e.g., physical camber 500] and generating control inputs [outputs from the data model server 420 “can be used to better control, predict, find drifts” ] for controlling the physical twin chamber, wherein the digital twin device comprises one or more computational models [“data model may comprise a statistical model, a physical model …The data model represents a virtual twin of the physical processing tool.”, e.g., “a physical model 427 and a statistical model 425”, “data model 520”] for determining the characteristics of the physical twin chamber and for generating the control inputs ([023, 040-045, 052], Figs. 4A- 4B, 5A-5B);
the digital twin device determines 1a first data set [“set of process inputs (e.g., hardware parameters and/or process parameters) is provided into the physical processing tool and into the data model”] associated with the physical twin chamber, the first data set comprises process data collected by sensors [“the witness sensors (e.g., 212, 213, 216, 218, and219)”/445 and “control loop sensors 203, 217”/447 +” Temperature sensors 207] configured to measure attributes of the physical twin chamber ([033, 037, 040-041]),
the digital twin device automatically (interpreted as without requiring user inputs) generates a second data set [outputs from the “data model server 420” that can be “used to better control, predict” such as “a control effort 463”. The outputs of the data models are not based on user inputs, rather they are based on inputs provided to the data model] based on the generated control inputs [previous rounds’ control outputs (that are “used to better control”) from the server 420 have impact on future generated and outputted “control effort 463”] and transmits [Fig. 4B, “server 420 may output a control effort 463 that modifies one or more process parameters in the tool 400 in order to correct drift and bring the tool 400 back into a desired process window.”] the second data set to the physical twin chamber for controlling [“modifies one or more process parameters… to correct drift”] the process performed on the substrates [“wafers”] by the physical twin chamber ([037, 045-046], Fig. 4B),
the second data set [“a modified set of process inputs”] is automatically generated [“updated data model may then be queried to generate a modified set of process inputs that will return the process chamber 500 back to a desired process window. The new inputs are fed back into the input block 571
as indicated by branch 587”] by the digital twin device based on, at least in part, the first data set, and by executing the one or more computational models of the digital twin device ([052, 056], Figs. 4A- 4B, 5A- 5B);
the digital twin device is implemented using a local server [“data model server 420 may be integrated with the processing tool 400”] configured close
to the physical twin chamber for edge computing and updating [“the data model 520 as a learning data set to provide an updated data model 520”] of the one or more
computational models, the local server transmits processed results generated by the one or more computational models
One may argue that the “data model server” like item 420/520 may not necessarily be a device since servers are well-known to be implemented by a virtual machine. That is, the digital twins (data model servers 420/520) of Hilkene may or may not be device as claimed.
However, implementing a data model server (like item 420/520) of Hilkene in a computing device is well-known in the art even when they are implemented external to the processing tool 400. Nevertheless, by giving the benefit of doubt, examiner takes the position that its digital twin/model may not be a “digital twin device” as claimed and further relies on Roham, see figs. 1 & 5 & associated texts.
Specifically, Roham teaches a digital twin 100 including one more computational models of a process chamber ([071- 072]). Specifically, Roham teaches a digital twin system for controlling a physical twin chamber [“a process chamber modeled by digital twin 100”] configured to process substrates, the digital twin system comprising: a digital twin device [“systems may be implemented on a single device or distributed across multiple devices.”, “FIG. 5 presents an example computer system that may be employed to implement certain embodiments described herein”, wherein the embodiments includes “a digital twin 100 of a process chamber”. The fig. 5’s computer system includes a processor 504] determining characteristics of a physical twin chamber and generating digital twin 100 can take inputs 102”] to automatically generate second data sets [“predicted wafer characteristics 104”] ([025, 036, 071-074, 0148], Fig 1).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) have combined Roham and Hilkene because they both related to executing a digital twin to receive first data set from a physical twin chamber of wafer processing to generate second data set and (2) have the “the data model server” (like item 420/520) of Hilkene implemented in a computing device with a processor 504 and a memory 508 of Roham. Roham teaches missing details for Hilkene about specific type of the computer system its “data model server” 420/520 may be implemented when the data model server 420 may be external to the processing tool 400 (Hilkene, [041] & Roham [0156]).
Hilkene in view of Roham still fails to teach the local server transmits processed results generated by the one or more computational models to a central server, and the central server aggregates the processed results with processed results received from local servers associated with other physical twin chambers to perform a fleet-level comparison or fleet-level updating of the one or more computational models as claimed.
Hao teaches analyzing time-series traces to detect mismatches between a semiconductor processing chamber using local servers 230 and central servers 220 of the monitored physical twins tools 101 (Fig. 2 & associated texts, [029-035]). Specifically, Hao teaches a digital twin system for controlling a physical twin chamber configured to process substrates comprising a local server configured close to the physical twin chamber [“connected to one or more manufacturing tools 101”] for edge computing and updating of the one or more computational models, the local server [“another server 230”] transmits processed results generated by the one or more computational models to a central server [“a server 220 which is connected via network 210 to another server 230 which is connected to one or more manufacturing tools 101”], and the central server aggregates [“data may be aggregated and analyzed at a central location, such as the server, and used to detect matches or mismatches of chambers based on time-series trace data in real-time”] the processed results with processed results received from local servers associated with other physical twin chambers to perform a fleet-level comparison [“detect matches or mismatches”] or fleet-level updating of the one or more computational models ([035-038, 063-067]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have (1) combined Hao and Hilkene in view of Roham because they both related to using a local server with machine learning models to process collected sensor data from the physical twin chamber and (2) modified the local server digital twin device of Hilkene in view of Roham to transmit processed results generated by the one or more computational models to a central server for aggregating with processed results received from local servers associated with other physical twin chambers as in Hao. Doing so would allow detecting matches or mismatches of processing results in multiple chambers of Hilkene in view of Roham based on time-series trace data in real-time and for intelligent predictions of substrate quality (Hao [036, 0101]). Accordingly, the combination of Hilkene, Roham, and Hao (HRH) teaches each limitation of the claim and renders invention thereof obvious to PHOSITA.
Regarding claim 2, HRH further teaches/suggests the digital twin system of claim 1, wherein the digital twin device generates the second data set and transmits the second data set to the physical twin chamber contemporaneously [as the data model receives inputs, it can process them to generate the output to be provided to the tool to perform “better control, predict, find drifts”] with receiving the first data set from the physical twin chamber (Hilkene, [045] & Roham Fig. 1).
Regarding claim 3, HRH further teaches/suggests the digital twin system of claim 1, wherein the one or more computation models of the digital twin device comprise a model of the physical twin chamber, wherein the model is configured to model one or more of: fluid dynamics [“a chemistry model of a gap between the showerhead and a pedestal, a Computational Fluid Dynamics (CFD) model of the gap”], direct Monte Carlo (DSMC) simulation, magneto-hydrodynamic particle-in-cell simulations, EM solvers, optical modeling tools, or direct computation of mathematical equations representing an attribute of the physical twin chamber, and the digital twin device performs, using at least the model, real-time monitoring [“smart monitoring and control”] and controlling of the physical twin chamber (Hilkene [042-043] & Roham [015, 051, 0143]).
Regarding claim 4, HRH further teaches/suggests the digital twin system of claim 1, wherein the one or more computational models of the digital twin device include one or more of: models of electrical, mechanical [“the physical processing tool”], fluid flow, or vacuum environment characteristics (Hilkene [0023-025]).
Regarding claim 5, HRH further teaches/suggests the digital twin system of claim 1, wherein the digital twin device models the characteristics and the processes of the physical twin chamber using models that include one or more of:
a lumped parameter system modeling networking tools [“semiconductor processing tools”], network models for solving systems of electrical circuits, or derivatives of network models, the first data set includes characteristics and properties of the substrate including responses of the substrate to processing performed by components of the physical twin chamber; and the digital twin models the characteristics and properties of the substrate [“wafer”] (Hilkene [023, 037-040] & Roham [051, 054]).
Regarding claim 6, HRH further teaches/suggests the digital twin system of claim 3, wherein the model is constructed empirically [Physical models like items 427 are well-known to be constructed through empirical observation of the historical/stored data] from measured data from the physical twin chamber (Hilkene [042] & Fig. 1 of Roham’s models).
Regarding claim 7, HRH further teaches/suggests the digital twin system of claim 3, wherein the model of the digital twin device:
evaluates performance of the physical twin chamber [item 500] relative to its expected or historical performance as established by prior data, compares [“At block 574, the one or more outputs of the physical chamber and one or more outputs of the data model 520 are compared”] performance characteristics of the digital twin device [data model 520] and the physical twin chamber to evaluate accuracy of the model to results of the physical twin chamber, and uses evaluation of the data from both the physical twin chamber and the digital twin device to create actionable insights [“If the physical metrology data 582 differs from the virtual metrology data 581, then branch 586 is taken, and the physical metrology data 582 is fed back into the data model 520 as a learning data”] to improve performance of the physical twin chamber (Hilkene [056], Figs. 5A- 5B & associated texts).
Claim(s) 21-33 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roham et al. (US 20240378347 A1, Filing Date: 2022-01-10) in view of Hilkene et al. (US 20220084842 A1), and further in view of Hao et al. (US 20200243359 A1). Roham and Hilkene are references of the record.
Regarding claim 21, Roham teaches a fleet of digital twin devices [Computing devices that implement multiple digital twins2 like items 100s (that each receive inputs 102 to generate outputs 104) for multiple chambers of “using digital twins of process chambers” for the “digital twin 100 of a process chamber”. Here, the chamber is interpreted as to include equipment because “manufacturing equipment or components…systems or sub-systems of a chamber” in para. 061]
for controlling a multi-chamber system for substrate processing, the fleet of digital twin devices comprising: ([025, 071]);
a plurality of digital twin devices [digital twins 100s in “generating digital twins of process chambers is provided”], wherein each digital twin device is configured to model characteristics or processes of at least one process chamber [“Physical chamber 220” of fig. 2] of a multi-chamber system and generate predicted wafer characteristics 104 can be used for any suitable purposes, such as: 1) design validation; 2) process validation; and/or 3) predictive maintenance”] for controlling the at least one process chamber during substrate processing ([011, 025, 076-081, 099]), wherein
each digital twin device, of the plurality of digital twin devices comprises one or more computational models [“digital twin 100 can include individual models of different locations of the process chamber”] ([089]),
each digital twin device, of the plurality of digital twin devices, determines a first data set [reading of the “physical sensor data”] associated with the at least one process chamber, the first data set comprises measurements reported by probes or sensors within the at least one process chamber, each digital twin device, of the plurality of digital twin devices, automatically [“In some embodiments, digital twin 100 can take inputs 102 and can generate predicted substrate characteristics 104 as an output”. Hence, the outputs 104s are generated by the digital twin 100 itself by processing of the inputs 102s via the models like 108 -130s] generates a second data set [At 452,…“generate predicted wafer characteristics using the digital twin”] that comprises the
While Roham teaches the digital twin devices 100 receiving and processing of the first data of the sensors of the chambers to generate second data sets for the chambers, it still fails to teach the second data sets (i.e., item 104 of fig. 1) to include control data as claimed. Furthermore, Roham fails to teach each digital twin device is implemented using a respective local server configured close to the at least one process chamber for edge computing and updating of the one or more computational models, the respective local servers transmit processed results generated by the one or more computational models chambers to a central server, and the central server aggregates the processed results received from the respective local servers to perform a fleet-level comparison or fleet-level.
Hilkene relates to a digital twin providing control inputs to a process chamber [“semiconductor processing tool 100 may comprise a chamber 105”] ([006, 019]). More specifically, Hilkene teaches a digital twin device [“exemplary computer system” shown in fig. 6 that implements the “data model server 420”] comprising one or more computational models for a chamber [chamber 105] configured to: (Figs. 5A- 5B, [019, 040-042, 047]);
model characteristics or processes of at least one process chamber of a multi-chamber system and generating control inputs [“model server 420 may output a control effort 463 that modifies one or more process parameters”] for controlling the at least one process chamber during substrate processing ([040-041]);
automatically generates a second data set that comprises the control inputs, and automatically transmits the second data set to the at least one process chamber for controlling [“data model server 420 may output a control effort 463 that modifies one or more process parameters in the tool 400 in order to correct drift and bring the tool 400 back into a desired process window”] processing of substrates by the at least one process chamber ([044-045, 052-053]);
each digital twin device is implemented using a respective local server [“data model server 420may be integrated with the processing tool 400”] configured close to the at least one process chamber for edge computing and updating of the one or more computational models ([041-042]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Hilkene and Roham because they both related to a digital twin device with various models for a process chamber to process wafers/substrates and (2) modify the each digital twin devices of the Roham to have the outputs from the digital twins 100 to include control inputs to use to control the processing of the substrates and implement the digital twin device using a respective local server as in Hilkene to significantly better control the process of Roham (Hilkene [045-46]). Furthermore, doing so would improve (e.g., by correcting the drift of the digital twins and bringing the tools back into a desired process window) the operating of the process chambers in Roham (Hilkene [0045]).
Roham in view of Hilkene fails to teach respective local servers transmit processed results generated by the one or more computational models chambers to a central server, and the central server aggregates the processed results received from the respective local servers to perform a fleet-level comparison or fleet-level updating of the one or more computational models.
Hao teaches a fleet of digital twin devices for controlling a multi-chamber
system for substrate processing, the fleet of digital twin devices comprising a plurality of digital twin devices, wherein each digital twin device is implemented using a respective local server configured close to the at least one process chamber for edge computing and updating of the one or more computational models, the respective local servers transmit processed results generated by the one or more computational models chambers to a central server [“a central location, such as the server” like item 220], and the central server aggregates [“the data may be aggregated and analyzed at a central location, such as the server, and used to detect matches or mismatches of chambers based on time-series trace data in real-time”] the processed results received from the respective local servers to perform a fleet-level comparison or fleet-level updating of the one or more computational models (Fig. 2, [029-037]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have (1) combined Hao and Roham in view of Hilkene because they both related to controlling multi-chamber system for substrate processing at central location and (2) modified the system of Roham in view of Hilkene to include missing limitations from Hao. Doing so would allow detecting matches or mismatches of processing results in multiple chambers based on time-series trace data in real-time and for intelligent predictions of substrate quality in the system of Roham in view of Hilkene (Hao [036, 0101]). Accordingly, the combination of Roham, Hilkene, and Hao (RHH) teaches each limitation of the claim and renders invention thereof obvious to PHOSITA.
Regarding claim 22, RHH teaches/suggests the fleet of digital twin devices of claim 21, wherein the first data set [“and bring the tools back into a desired process windows in”] includes characteristics or properties of the substrates processed in the at least one process chamber, and the one or more computational models used by the digital twin device is configured to model a characteristic or property of the substrates (Roham, [055], Fig. 1; Hilkene [033]).
Regarding claim 23, RHH teaches/suggests the fleet of digital twin devices of claim 22, wherein the digital twin devices, of the plurality of digital twin devices, control and monitor [“monitoring and predicting a health status of manufacturing equipment or components of manufacturing equipment”] interactions between the process chambers [“processing tool 100 may comprise a chamber 105”] of the multi-chamber system, and the digital twin devices, of the plurality of digital twin devices, control and monitor a plurality of tasks that are executed by the process chambers of the multi-chamber system, while the process chambers are processing substrates (Roham [011, 061, 0129] & Hilkene [019, 040-041]).
Regarding claim 24, RHH teaches/suggests the fleet of digital twin devices of claim 21, wherein each digital twin device, of the plurality of digital twin devices, automatically generates the second data set and transmits the second data set to the at least one process chamber for controlling substrate processing by the at least one process chamber contemporaneously with receiving the first data set from the at least one process chamber (Roham, Fig. 1, [081] & Hilkene [045], Fig. 4B).
Regarding claim 25, RHH teaches/suggests the fleet of digital twin devices of claim 21, wherein the one or more computation models of the each digital twin device, of the plurality of digital twin devices, comprise a virtual model of the at least one process chamber (Roham [051] & Hilkene [049]),
the virtual model is configured to model one or more of: fluid dynamics [“a Computational Fluid Dynamics (CFD) model of the gap between the showerhead and the pedestal, etc. In some embodiments, each model that makes up the digital twin can be one of: 1) a closed-form solution; 2) an AI/ML model; and 3) an HFS model”], direct Monte Carlo (DSMC) simulation, EM solvers, optical modeling tools, or direct computation of mathematical equations representing a physical process chamber of the process chambers, and the digital twin device performs, using at least the virtual model, real-time monitoring and controlling of the physical process chamber of the process chambers (Roham [051, 061, 093], Fig. 1; Hilkene [042]).
Regarding claim 26, RHH teaches/suggests the fleet of digital twin devices of claim 25, wherein the virtual model [these models are stored in the computational device 500 of fig. 5 that implements digital twins 100s] of a digital twin device of the plurality of digital twin devices: evaluates performance of the corresponding process chamber, of the process chambers, relative to its expected or historical performance as established by prior data; compares [“the one or more outputs of the physical chamber and one or more outputs of the data model 520 are compared”] performance characteristics of the digital twin device and the corresponding process chamber, of the process chambers, to evaluate the accuracy of the virtual model to results of the corresponding process chamber; and uses evaluation of the data from both the at least one process chamber and the digital twin device to create actionable insights to improve performance of the at least one process chamber (Roham, Fig. 1, 5; Hilkene [051-052], Fig. 5A).
Regarding claim 27, RHH teaches/suggests the fleet of digital twin devices of claim 21, wherein the one or more computational models of the digital twin device include one or more of: models of electrical delivery, models of mechanical delivery, models of fluid delivery, or models of vacuum systems, the one or more computations models capture corresponding chemical actions reported by subsystems, and the corresponding and chemical actions include one or more of: a heat transfer, transmission of electricity, electrical pulses, EM radiation, chemical reactions, material phase, erosion, or wear due to physical contact (Roham [051, 097, 0104]; Hilkene [002]).
Regarding claim 28, the rejection of claim 21 is incorporated. Therefore, the combination of Roham, Hilkene, and Hao (RHH) teaches each element of the claim for the similar reasons set forth above in claim 21. Please note that summary, Roham teaches a fleet of digital twin devices [computers that implement digital twins of the “generating digital twins of process chambers is provide”] for controlling a multi-chamber process system for substrate [“surface of a wafer fabricated by the process chamber.”] processing, the fleet of digital twin devices comprising: (Figs.1-2B, [008]);
a plurality of digital twin devices [computers like system 500 for each of the chambers of the “digital twins of process chambers is provide”] capturing, wherein each digital twin device is configured to model characteristics or processes of at least one process chamber of a multi-chamber process system and generating wafer characteristics 104”] for digital twin of manufacturing equipment”, e.g., item 100 of fig. 1 for a chamber], of the plurality of digital twin devices, determines a first data set [reading of the data 102 of fig. 1-2] associated with the at least one process chamber of a plurality of chamber processes, and the corresponding processes for processing a plurality of substrates, the first data set comprises measurements reported by probes or sensors within the at least one chamber process, or data collected and reported by internal sensors of the digital twin device (Figs. 1, 5, [011, 053, 074, 0129]),
each digital twin device, of the plurality of digital twin devices, automatically generates a second data set [“digital twin 100 can take inputs 102 and can generate predicted substrate characteristics 104 as an output”] that comprises the
Roham fails to teach each digital twin device is implemented using a respective local server configured close to the at least one process chamber for edge computing and updating of the one or more computational models, the respective local servers transmit processed results generated by the one or more computational models chambers, to a central server and the central server aggregates the processed results received from the respective local servers to perform a fleet-level comparison or fleet-level updating of the one or more computational models.
Hilkene teaches limitations of (1) control/controlling as part of the “second data set” ([045, 052] & Figs. 4A, 5A -5B); and
(2) each digital twin device is implemented using a respective local server configured close to the at least one process chamber for edge computing and updating of the one or more computational models ([041-042]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Hilkene and Roham because they both related to a digital twin device include a digital twin with various models for a process chamber to process wafers/substrates and (2) modify the each digital twin devices of the Roham to have the outputs from the digital twins 100 to include control inputs to use to control the processing of the substrates and implement each digital twin device using a respective local server as in Hilkene. Doing so would allow to improve (e.g., by correcting the drift of the digital twins and bring the tools back into a desired process window) the operating of the process chambers in Roham (Hilkene [0045]).
Roham in view of Hilkene fails to teach: the respective local servers transmit processed results generated by the one or more computational models chambers, to a central server the central server aggregates the processed results received from the
respective local servers to perform a fleet-level comparison or fleet-level updating
of the one or more computational models.
However, Hao teaches each digital twin device is implemented using a respective local server [item 230] configured close to the at least one process chamber [tool 110] for edge computing and updating of the one or more computational models, the respective local servers transmit processed results generated by the one or more computational models chambers, to a central server [item 220] and the central server aggregates [“the data may be aggregated and analyzed at a central location, such as the server, and used to detect matches or mismatches of chambers based on time-series trace data in real-time.”] the processed results received from the respective local servers to perform a fleet-level comparison or fleet-level updating of the one or more computational models ([030-036] Figs. 2).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have (1) combined Hao and Roham in view of Hilkene because they both related to controlling multi-chamber system for substrate processing at central location and (2) modified the system of Roham in view of Hilkene to include remaining limitations from Hao. Doing so would allow detecting matches or mismatches of processing results in multiple chambers based on time-series trace data in real-time and for intelligent predictions of substrate quality in system of Roham in view of Hilkene (Hao [036, 0101]). Accordingly, the combination of Roham, Hilkene, and Hao (RHH) teaches each limitation of the claim and renders invention thereof obvious to PHOSITA.
Regarding claim 29, RHH teaches/suggests the fleet of digital twin devices of claim 28, wherein the first data set includes characteristics or properties of the one or more substrates processed in the at least one process chamber, and the one or more computational models used by the digital twin device is configured to model a characteristic or property of the substrates (Roham, fig. 1, [011, 055] & Hilkene [053]).
Regarding claim 30, RHH teaches/suggests the fleet of digital twin devices of claim 29, wherein the digital twin devices, of the plurality of digital twin devices, control and monitor interactions between the chamber processes of a multi-chamber process system, and the digital twin devices, of the plurality of digital twin devices, control and monitor tasks that are executed by the chamber processes of the multi-chamber process system, while physical chambers process substrates (Roham [0061, 093]; Hilkene [0042]).
Regarding claim 31, RHH teaches/suggests the fleet of digital twin devices of claim 28, wherein each digital twin device, of the plurality of digital twin devices, automatically generates the second data [At 452,…“generate predicted wafer characteristics using the digital twin”] and transmits the second data set to the at least one process chamber for controlling [“data model server 420 may output a control effort 463 that modifies one or more process parameters in the tool 400 in order to correct drift and bring the tool 400 back into a desired process window”] substrate processing by the at least one process chamber contemporaneously with receiving the first data set from the at least one process chamber (Roham [073, 0104], Hilkene [040- 045, 052- 053]).
Regarding claim 32, RHH teaches/suggests the fleet of digital twin devices of claim 28, wherein the one or more computation models of the each digital twin device, of the plurality of digital twin devices, comprise a virtual model of the at least one process chamber, the virtual model is configured to model one or more of: fluid dynamics, direct Monte Carlo (DSMC) simulation, EM solvers, optical modeling tools, or direct computation of mathematical equations [“closed-form physics equations in a situation”] representing a physical process chamber of the plurality of process chambers, and the digital twin device performs, using at least the virtual model, real-time monitoring and controlling of the physical process chamber of the plurality of process chambers (Roham [051, 0061, 093]; Hilkene [0042]).
Regarding claim 33, RHH teaches/suggests the fleet of digital twin devices of claim 32, wherein the virtual model of a digital twin device of the plurality of digital twin devices: evaluates performance of the corresponding process chamber, of the plurality of process chambers, relative to its expected or historical performance as established by prior data; compares performance characteristics of the digital twin device and the corresponding process chamber, of the plurality of process chambers, to evaluate the accuracy of the virtual model to results of the corresponding process chamber; and uses evaluation of the data from both the at least one process chamber and the digital twin device to create actionable insights to improve performance of the at least one process chamber (Hilkene [051-052], Fig. 5A).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANTOSH R. POUDEL whose telephone number is (571)272-2347. The examiner can normally be reached Monday - Friday (8:30 am - 5:00 pm).
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/SANTOSH R POUDEL/ Primary Examiner, Art Unit 2115
1 See Spec, para. 053 states input data to the model as claimed “first data set” (“the first data set corresponds to the input to an application executed by the digital twin device”)
2 See paras. 051-052, “digital twin device may include one or more processors and one or
more memory units”
Prosecution Insights