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 received on 09/08/2023. The claims 1-20 are pending, of which the claim(s) 1, 8, & 15 is/are in independent form.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
I) Claim 1- 4, 15- 17, & 20 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4- 5, 7, 15, & 17- 18 (filed on 09-08-2023) of co-pending Application No. 18/244113 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other for the reasons show below.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Regarding claim 1,
Instant Application: 18/244,104
Co-pending Application: 18/244,113
1. A digital twin system for controlling a physical twin chamber configured to
process substrates, the digital twin system comprising:
1. A fleet of digital twin devices for controlling a multi-chamber system for
substrate processing, the fleet of digital twin devices comprising:
a digital twin device determining characteristics of a physical twin chamber and generating control inputs for controlling the physical twin chamber;
wherein the digital twin device comprises one or more computational models for
determining the characteristics of the physical twin and for generating the
control inputs;
a plurality of digital twin devices, wherein each digital twin device is configured to model characteristics or processes of at least one process chamber of a multi-chamber system and generating control inputs for controlling the at least one process chamber during substrate processing;
wherein each digital twin device, of the plurality of digital twin devices comprises one or more computational models
wherein the digital twin device determines a first data set associated with the
physical twin chamber;
wherein each digital twin device, of the plurality of digital twin devices, determines a first data set associated with the at least one process chamber;
wherein the first data set comprises process data collected by sensors configured to measure attributes of the physical twin chamber;
wherein the first data set comprises measurements reported by probes or sensors within the at least one process chamber;
wherein the digital twin device automatically generates a second data set based on the generated control inputs and transmits the second data set to the physical twin chamber for controlling the process performed on the substrates by the physical twin chamber; and
wherein each digital twin device, of the plurality of digital twin devices, 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 processing of substrates by the at least one process
chamber; and
wherein the second data set is automatically generated 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.
wherein the second data set is automatically generated 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.
As shown in the above table, the claim 1 of the co-pending application’113 teaches/suggests each limitation of the claim 1 of the instant application. The co-pending application recites multiple digital devices but the instant claim requires “a digital twin system” which is anticipated by the co-pending application’s multiple digital devices. Furthermore, the instant application’s claim 1 requires the second data sets based on the generated control inputs but the co-pending application’s claim 1 states the second data set already includes/comprises control inputs. Thus, in order to generate second data set in co-pending application, the control inputs are required and hence anticipates/renders obvious to the limitation of “a second data set based on the generated control inputs” of the instant claim.
Accordingly, the claim 1 of the instant application is not patentable over claim 1 of the co-pending application 18/244,113.
Regarding claim 15:
Instant Application: 18/244,104
Co-pending Application: 18/244,113
15. substrate processing system, comprising:
15. A substrate processing fleet system of digital twin devices for controlling a
multi-chamber system for substrate processing, comprising:
a digital twin device determining characteristics of a physical twin chamber and generating control inputs for controlling the physical twin chamber;
wherein the digital twin device comprises one or more computational models for
determining the characteristics of the physical twin and for generating the
control inputs;
a plurality of digital twin devices capturing and modeling characteristics and processes of process chambers of a multi-chamber system and generating
control inputs for controlling the process chambers and processes executed
by the chambers during substrate processing
wherein each digital twin device, of the plurality of digital twin devices, is associated with a corresponding process chamber, of the process chambers, and
comprises one or more computational models for modeling the characteristics
and the processes and for generating the control inputs
wherein the digital twin device comprises a processor and a memory coupled to the
processor, the memory having stored instructions executable by the processor to:
wherein the digital twin device comprises a processor and a memory coupled to the
processer, the memory having stored instructions executable by the
processor to:
determine, by the digital twin device a first data set associated with the physical twin chamber;
receive, by each digital twin device, of the plurality of digital twin devices a
first data set associated with the corresponding process chamber of
the process chambers;
wherein the first data set comprises direct measurement of physical processes collected and reported by sensors implemented in the physical twin chamber and data collected and reported by internal sensors of the digital twin device;
wherein the first data set comprises measurements reported by probes or
sensors and data provided by the digital twin device;
automatically generate, by the digital twin device, a second data set that comprises the control inputs, and transmit the second data set, by the digital twin device, to the physical twin chamber for substrate processing by the physical twin chamber; and
automatically generate, by each digital twin device, of the plurality of digital
twin devices, a second data set that comprises the control inputs, and
automatically transmit the second data set to the corresponding process chamber, of the process chambers, for controlling the processing of substrates by the corresponding process chamber of the process chambers; and
wherein the second data set is automatically generated 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.
wherein the second data set is automatically generated 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.
Accordingly, the co-pending application 18244113’s claim 15 teaches/suggests each limitation of the instant application’s claim 15.
Claims in Instant Application: 18/244,104
Claims in Co-Pending Application 18/244,113
2
4
3
5
4
7
16
17
17, 20
18
II) Claims 8- 10 & 13-14 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 8 & 11- 13 of co-pending Application No. 18/244,113 in view of Hilkene et al., (US 20220084842 A1). This is a provisional nonstatutory double patenting rejection.
Instant Application: 18/244,104
Co-pending Application: 18/244,113
8. A method for controlling a physical twin chamber for substrate processing, the method comprising:
8. A method for controlling, using a fleet of digital twin devices, a multi-chamber
system for substrate processing, the method comprising:
determining, by a digital twin device, a first data set associated with a physical twin chamber;
wherein the digital twin device comprises one or more computational models for determining characteristics of the physical twin and for generating the control inputs
determining, by each digital twin device, of a plurality of digital twin devices a first
data set associated with a corresponding process chamber of process chambers;
wherein the plurality of digital twin devices models characteristics and processes of the process chambers of a multi-chamber system and generates control inputs for controlling the process chambers during substrate processing;
wherein each digital twin device, of the plurality of digital twin devices, is associated with a corresponding process chamber, of the process chambers, and
comprises one or more computational models
wherein the first data set comprises direct measurement of physical processes collected and reported by sensors implemented in the physical twin chamber and
wherein the first data set comprises measurements reported by probes or sensors implemented in the corresponding process chamber and data provided from the digital twin device
automatically generating, by the digital twin device, a second data set that
comprises the control inputs,
and transmitting the second data set, by the digital twin device, to the physical twin chamber for controlling substrate
processing by the physical twin chamber; and
automatically generating, by each digital twin device, of the plurality of digital twin
devices, a second data set that comprises the control inputs, and
automatically transmitting, by the digital twin device, to the corresponding
process chamber, of the process chambers, for controlling the processing of substrates by the corresponding process chamber of the process chambers; and
wherein the second data set is automatically generated 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.
wherein the second data set is automatically generated 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.
The claim 8 of the co-pending application fails to teach the first data set comprises data collected and reported by internal sensors of the digital twin device as shown above with strikethrough emphasis.
Hilkene (US 20220084842 A1) teaches a method comprising: wherein the first data set [data provided to the “data model 420”] comprises direct measurement of physical processes collected and reported by sensors [items 466 + 467] implemented in the physical twin chamber [item 400] and data collected and reported by internal sensors [virtual sensor module 469 and its sub-components when “the virtual sensor module 468 may be implemented as part of the data model server 420”] of the digital twin device (Fig. 4B, [044- 045]).
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 claim 8 of the co-pending application’113 because they both related to controlling a physical twin chamber using a digital twin device and (2) modify the claim 8 of the co-pending application’113 to include data collected and reported by internal sensors of the digital twin device to determine a first data set (data inputted to the digital twin) associated with a physical twin as in Hilkene. Doing so would allow digital twin device of the co-pending application’113 itself to generate additional data inputs for the digital twin device that are very useful but difficult or impossible to measure with the probes or sensors implemented in the corresponding process chamber (Hilkene [034]).
Claims in Instant Application: 18/244,104
Claims in Co-pending Application: 18/244,113
9
11
10
12
13
12
14
13
III) Claims 1 & 8 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 8, & 15 of co-pending Application No. 18/244126 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other for the reasons set forth below.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Regarding claim 1,
Instant Application: 18/244,104
Co-pending Application: 18/244,126
1. A digital twin system for controlling a physical twin chamber configured to
process substrates, the digital twin system comprising:
1. A fleet of digital twin devices for controlling a multi chamber process system for substrate processing, the fleet of digital twin devices comprising:
a digital twin device determining characteristics of a physical twin chamber and generating control inputs for controlling the physical twin chamber;
wherein the digital twin device comprises one or more computational models for
determining the characteristics of the physical twin and for generating the
control inputs;
a plurality of digital twin devices that form the fleet, wherein each digital twin device is configured to model characteristics or processes of at least one process chamber of a plurality of process chambers of the multi-chamber process system and generate control inputs for controlling the at least one process chamber during substrate processing
wherein each digital twin device, of the plurality of digital twin devices, comprises
one or more computational models
wherein the digital twin device determines a first data set associated with the
physical twin chamber; wherein the first data set comprises process data collected by sensors configured to measure attributes of the physical twin chamber;
wherein the first data set comprises measurements reported by probes or
sensors within the at least one process chamber
wherein the digital twin device automatically generates a second data set based on the generated control inputs and transmits the second data set to the physical twin chamber for controlling the process performed on the substrates by the physical twin chamber; and
wherein each digital twin device, of the plurality of digital twin devices,
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 of the plurality of process chambers, for controlling substrate processing by the at least one process chamber
wherein the second data set is automatically generated 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.
wherein the second data set is automatically generated by the digital twin device based on, at least in part, the first data set, and by executing one or more computational models of the digital twin device.
Accordingly, the claim 1 of the co-pending application’126 teaches/suggests each limitation of the claim 1 of the instant application. The co-pending application126’s claim 1 also includes other limitations that are not required in the claim 1 of the instant application. Therefore, the claim 1 of the co-pending application’126 is a species to the claim 1 of the instant application and, therefore, the claim 1 of the instant application is not patentable over the claim 1 of the co-pending application’126.
Regarding claim 8, this claim is also anticipated/rendered obvious over claim 8 of the co-pending application 18/244,126.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 nonobviousness.
Claim(s) 1-20 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).
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 and for generating the control inputs ([023, 040-045, 052], Figs. 4A- 4B, 5A-5B);
wherein 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; wherein 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]);
wherein 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); and
wherein 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).
One may argue that the “data model server” like item 420/520 that is external to the processing tool 400 may or 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 serve (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. Hence, invention of this claim can be obvious to PHOSITA based on the disclosure of Hilkene itself.
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 relies on Roham’s figs. 1 & 5.
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) combine Roham and Hilkene because they both related to executing a digital twin to receive first data set from a physical twin chamber 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 at what 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]). Accordingly, Hilkene in view of Roham renders invention of this claim obvious to PHOSITA.
Regarding claim 2, Hilkene in view of Roham further teaches/suggests the digital twin system of claim 1, wherein the digital twin 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, Hilkene in view of Roham 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 wherein 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, Hilkene in view of Roham 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, Hilkene in view of Roham 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, Hilkene in view of Roham 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, Hilkene in view of Roham 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).
Regarding claim 8, the rejection of claim 1 is incorporated. Thus, only in summary, Hilkene a method for controlling a physical twin chamber [item 500] for substrate [“wafer”] processing, the method comprising: (Fig. 4B, 5A);
determining, by a digital twin [“data model server” like item 420/520] device, a first data set associated with a physical twin chamber; wherein the digital twin device comprises one or more computational models [“the data model server 420 may comprise one or both of a physical model 427 and a statistical model 425”] for determining characteristics of the physical twin and for generating the control inputs ([023, 041-045, 052]);
wherein the 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”] comprises direct measurement of physical processes collected and reported by sensors [items 466 and 467] implemented in the physical twin chamber and data collected and reported by internal [virtual sensor 468 can be at the serve 420 when “sensor module 468 may be implemented as part of the data model server 420”] sensors of the digital twin device ([040-045]);
automatically (interpreted as without requiring user to provide inputs to generate outputs from the model) generating, by the digital twin device, 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” by inputting information from the control loop sensor and witness sensors] that comprises the control inputs, and transmitting the second data set, by the digital twin device, to the physical twin chamber for controlling substrate processing by the physical twin chamber; and wherein the second data set is automatically generated by the digital twin device based on, at least in part, the first data set, and by executing [using model 420 to “output a control effort 463 that modifies one or more process parameters”] the one or more computational models of the digital twin device ([045-047], Fig. 4B).
One may argue that Hilkene’s data model 520/420 may or may not be in a hardware computer system (claimed “device”). Thus, Hilkene may not necessarily anticipate the invention of the claim 1.
Roham teaches a method for controlling a physical twin chamber for substrate processing, the method comprising:
determining, by a digital twin device [“computer system 500 is depicted in FIG. 5” that implements the “digital twin 100” system of fig. 1], a first data set [“inputs 102”] associated with a physical twin chamber; wherein the digital twin device comprises one or more computational models for determining characteristics of the physical twin and automatically generating, by the digital twin device, a second data set [“digital twin 100 can take inputs 102 and can generate predicted substrate characteristics 104 as an output.”] and transmitting the second data set to the physical twin chamber (Fig. 1, 5, [036, 073, 0100]).
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 Roham and Hilkene because they both related to executing a digital twin to receive first data set from a physical twin chamber 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 at what 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]). Accordingly, Hilkene in view of Roham renders invention of this claim obvious to PHOSITA.
Regarding claim 9, Hilkene in view of Roham teaches/suggests the method of claim 8, wherein the digital twin generates the second data set and transmits the second data set to the physical twin contemporaneously with receiving the first data set from the physical twin chamber (Hilkene, [045] & Roham Fig. 1).
Regarding claim 10, Hilkene in view of Roham teaches/suggests the method of claim 8, 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, 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 wherein 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 [0143], Fig. 1).
Regarding claim 11, Hilkene in view of Roham teaches/suggests the method of claim 8, wherein the one or more computational models of the digital twin device include one or more of:
models of electrical, mechanical, fluid flow, or vacuum environment characteristics; wherein the one or more computations models capture corresponding chemical actions reported by subsystems; and wherein the corresponding and chemical actions include one or more of: heat transfer, transmission of electricity, electrical pulses, EM radiation, chemical reactions [e.g., “chemical vapor deposition”], material phase, erosion, or wear due to physical contact (Hilkene [0023-025] & Roham [0039] Fig. 1).
Regarding claim 12, Hilkene in view of Roham teaches/suggests the method of claim 8, 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, network models [“other suitable type of autoencoder network), neural networks”] for solving systems of electrical circuits, or derivatives of network models (Fig. 1-4B of Hilkene, Roham fig. 1, [042]);
wherein 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 wherein the digital twin models the characteristics and properties of the substrate (Hilkene [035, 039]).
Regarding claim 13, Hilkene in view of Roham teaches/suggests the method of claim 10, wherein the model is constructed empirically from measured data from the physical twin chamber (Hilkene [042] & Fig. 1 of Roham’s models).
Regarding claim 14, Hilkene in view of Roham teaches/suggests the method of claim 10, wherein the model of the digital twin device: evaluates performance of the physical twin chamber relative to its expected or historical performance as established by prior data; compares performance characteristics of the digital twin device 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 to improve performance of the physical twin chamber (Hilkene [056], Figs. 5A- 5B & associated texts).
Regarding claim 15, Hilkene in view of Roham teaches/suggests a substrate processing system for the similar reasons set forth above in claims 1 & 8. Please note that fig. 5 of Roham that is used to implement the “data model server 420” of Hilkene (fig. 4) is mapped as claimed a digital twin device comprises a processor and a memory coupled to the processor.
Regarding claim 16, Hilkene in view of Roham teaches/suggests the substrate processing system of claim 15, wherein the digital twin generates the second data set and transmits the second data set to the physical twin contemporaneously with receiving the first data set from the physical twin chamber (Hilkene, Fig. 4B, [0045-048]).
Regarding claim 17, Hilkene in view of Roham teaches/suggests the substrate processing system of claim 15, 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, direct Monte Carlo(DSMC) simulation, magneto-hydrodynamic particle-in-cell simulations, EM solvers, optical modeling tools, or direct computation of mathematical equations representing the physical twin chamber; wherein the digital twin device performs, using at least the model, real-time monitoring and controlling of the physical twin chamber; and wherein the digital twin device monitors and controls the physical twin chamber by executing one or more fast-running network models and empirically built relational data models (Hilkene [042-043] & Roham [0143]).
Regarding claim 18, Hilkene in view of Roham teaches/suggests the substrate processing system of claim 15, 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; wherein the one or more computations models capture corresponding physical and chemical actions reported by subsystems; and wherein the corresponding and chemical actions include one or more of: heat transfer, transmission of electricity, electrical pulses, EM radiation, chemical reactions, material phase, erosion, or wear due to physical contact (Hilkene [0023-025] & Roham [0039] Fig. 1).
Regarding claim 19, Hilkene in view of Roham teaches/suggests the substrate processing system of claim 15, 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, network models for solving systems of electrical circuits, or derivatives of network models; wherein 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 wherein the digital twin models the characteristics and properties of the substrate (Hilkene [023, 037-040] & Roham [054]).
Regarding claim 20, Hilkene in view of Roham teaches/suggests the substrate processing system of claim 17, wherein the model is constructed empirically from measured data from the physical twin chamber (Hilkene [042] & Fig. 1 of Roham’s models).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Mahakali (US 20230222264 A1) teaches providing a more accurate digital
twin of the processing chamber ([026]).
2) Yun et al. (US 20240274453 A1) teaches generating second data sets from the digital twin models ([038]).
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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”)