DETAILED ACTION
Claims 1-16 are pending. Claim 16 is new.
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 .
Priority
Acknowledgement is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) to Japanese Patent Application No. 2020-217779 filed on 12/25/2020.
Response to Arguments
Applicant’s arguments, filed 3/13/25, have been fully considered but are not persuasive, except where noted below.
Applicant’s arguments regarding claim interpretation (page 8) are persuasive and the claims are no longer interpreted under 35 U.S.C. § 112(f).
Applicant’s arguments regarding the previous rejection under 35 U.S.C. § 112(b) (page 8) are persuasive, however, new grounds of rejection under 35 U.S.C. § 112(b) are presented below.
Applicant’s arguments regarding 35 U.S.C. § 101 (pages 8-9) are moot because the claims are no longer rejected under that statute.
Applicant argues that ‘Baseman fails to clearly specify that a processor causes each of agent units in the management device to monitor a state of the device and detect a predetermined event.’ (page 9).
It is respectfully submitted that this is moot because Basement is not cited as teaching an agent or agent unit. Applicant’s argument is therefore not persuasive.
Applicant argues that ‘Nowhere in the specification and drawings, does Song teach or suggest the feature of "a management device including a processor and a memory, agents being executed by the processor, and each of the agents including an event detection model stored in the memory, ... wherein the processor detects the predetermined event by using the event detection model’ (page 9-10).
It is respectfully submitted that this is moot because Song is not cited as teaching this complete limitation and one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., Inc., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986); see MPEP 2145 IV. Applicant’s argument is therefore not persuasive.
Applicant states that ‘a prima facie case of obviousness must establish that the asserted combination of references teaches or suggests each and every element of the claimed invention. In view of the distinction of claim 1 noted above, at least one claimed element is not present in the asserted combination of Baseman and Song’ (page 10).
It is respectfully submitted that no evidence or reasoned argument is presented to support this statement and Applicant’s statement is therefore not persuasive. Further, the rejection below under 35 U.S.C. § 103 details why this limitation is considered obvious over the combination of Baseman and Song.
Applicant’s arguments regarding the other independent and dependent claims (page 10) are not persuasive given the continued rejection of claim 1.
For at least these reasons, the rejection of the claims is maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 1-16 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
With regard to claim 1, this claim recites ‘each of the agents including an event detection model stored in the memory’ and then ‘the event detection model’ suggesting that each event detection model is identical. However, it is not clear if each claimed event detection model is identical and the specification indicates that each of the event detection models is associated with a different type of event, e.g. gas, temperature, particle, etc. [Figs. 5, 7-8].
With regard to claim 13, this claim recites similar language to claim 1 and is rejected based on the same rationale.
With regard to claim 14, this claim recites similar language to claim 1 and is rejected based on the same rationale.
The dependent claims are also rejected under 35 U.S.C. § 112 as they inherit all of the characteristics of the claim from which they depend and none of the dependent claims provide a cure for the indefiniteness of the parent claims.
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 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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) 1-2, 7-8 and 13-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baseman et al. U.S. Patent Publication No. 20140031968 (hereinafter Baseman) in view of Song et al. U.S. Patent Publication No. 20200166902 (hereinafter Song).
Regarding claim 1, Baseman teaches a management system for managing a substrate manufacturing process [0003-0006 — methods and apparatus for improving run-to-run (R2R) control in a semiconductor manufacturing process; 0049 — FIG. 4 is a block diagram depicting at least a portion of an exemplary system 400 for performing R2R control and sampling optimization, according to an embodiment of the invention. The R2R control system 400 includes a data storage unit 402, or alternative memory (e.g., embedded and/or standalone), a metrology module 404, a sampling optimization module 406, a predictive modeling module 408, and a control module 410. The R2R control system 400 receives prescribed information, including but not limited to actual metrology measurements, from manufacturing tools 412, which includes, for example, semiconductor processing equipment, metrology equipment, automated test equipment (ATE), etc.; 0061, Figs. 4-5 — The manufacturing tools 532 are used to produce wafers and generate processing variables 534, which can be used by subsequent processing steps to adjust the manufacturing process to meet prescribed parameters], comprising:
a substrate processing device that performs the substrate manufacturing process [0049 — FIG. 4 is a block diagram depicting at least a portion of an exemplary system 400 for performing R2R control and sampling optimization, according to an embodiment of the invention. The R2R control system 400 includes a data storage unit 402, or alternative memory (e.g., embedded and/or standalone), a metrology module 404, a sampling optimization module 406, a predictive modeling module 408, and a control module 410. The R2R control system 400 receives prescribed information, including but not limited to actual metrology measurements, from manufacturing tools 412, which includes, for example, semiconductor processing equipment, metrology equipment, automated test equipment (ATE), etc.; 0061, Figs. 4-5 — The manufacturing tools 532 are used to produce wafers and generate processing variables 534, which can be used by subsequent processing steps to adjust the manufacturing process to meet prescribed parameters]; and
a management device including a processor and a memory, a unit being executed by the processor, and each unit including an event detection model stored in the memory [0058-0059, Fig. 5 — methodology 500 utilizes a plurality of prediction models 512, each prediction model being operative to generate an output result as a function of actual and/or virtual metrology data. In this illustrative embodiment, prediction models 512 includes four prediction models; namely, a single chamber based model 514, a global model across all (or multiple) chambers 516, a metrology based prediction model 518 and a metrology and error-adaptive model 520. The single chamber based model 514 is operative to receive processing variables from module 504 corresponding to one semiconductor processing chamber and construct a prediction model as a function thereof; 0005-0006, 0088-0089 — an apparatus including a memory and at least one processor that is coupled to the memory], wherein the processor causes the unit in the management device to monitor a state of the substrate processing device and detect a predetermined event [0049 — FIG. 4 is a block diagram depicting at least a portion of an exemplary system 400 for performing R2R control and sampling optimization, according to an embodiment of the invention. The R2R control system 400 includes a data storage unit 402, or alternative memory (e.g., embedded and/or standalone), a metrology module 404, a sampling optimization module 406, a predictive modeling module 408, and a control module 410. The R2R control system 400 receives prescribed information, including but not limited to actual metrology measurements, from manufacturing tools 412, which includes, for example, semiconductor processing equipment, metrology equipment, automated test equipment (ATE), etc.; 0005-0006, 0088-0089 — an apparatus including a memory and at least one processor that is coupled to the memory; 0058, Fig. 5 — At least a portion of the metrology data is used by module 510 to estimate a metrology error and determine therefrom a deviation from prescribed target values; 0064, Fig. 6 — actual measured wafer parameter is also used to determine metrology error in step 612 by comparing the actual measurement generated from the metrology in step 604 with an expected value for that wafer parameter. A discrepancy between the actual and expected results, taking in account a statistical accuracy of the metrology tools, is used to generate an output indicative of a confidence in the actual measurement. This output is provided to the R2R controller 616. Based on the amount of discrepancy between actual and expected results collected over time, an indication as to how to adjust the process variables]; and
wherein the unit derives an instruction based on information so that an index value of the substrate manufacturing process is optimized, wherein the processor controls the substrate processing device based on the derived instruction [0056-0062, Figs. 4-5 — control module 410 is coupled with the manufacturing tools 412 and the sampling optimization module 406 in a feedback arrangement for controlling certain aspects of the manufacturing tools (e.g., processing parameters) for R2R control. More particularly, the control module 410 is operative to receive the sampling policy generated by the sampling and optimization module 406 and to control the manufacturing tools 412 in accordance with the sampling policy; 006-0071, Figs. 6-7 — the R2R controller 616 is beneficially able to optimize the sampling policy (e.g., minimizing sampling frequency) while maintaining a desired level of wafer quality or alternative process metric… the sampling policy to be optimized is sampling frequency optimization 716, although the invention is not limited to optimization of sampling frequency.]
wherein the event detection model is configured to estimate whether an event requiring a change in a target value of the substrate manufacturing process occurs, using the state of the substrate processing device, and wherein the processor detects the predetermined event by using the event detection model [0058-0061, Fig. 5 — methodology 500 utilizes a plurality of prediction models 512, each prediction model being operative to generate an output result as a function of actual and/or virtual metrology data. In this illustrative embodiment, prediction models 512 includes four prediction models; namely, a single chamber based model 514, a global model across all (or multiple) chambers 516, a metrology based prediction model 518 and a metrology and error-adaptive model 520. The single chamber based model 514 is operative to receive processing variables from module 504 corresponding to one semiconductor processing chamber and construct a prediction model as a function thereof… the R2R controller 530 is operative in a feedback control arrangement to control one or more manufacturing tools 532 in accordance with the sampling policy generated by the sampling/measurement optimization module 528. The manufacturing tools 532 are used to produce wafers and generate processing variables 534, which can be used by subsequent processing steps to adjust the manufacturing process to meet prescribed parameters.; 0005-0006, 0088-0089 — an apparatus including a memory and at least one processor that is coupled to the memory].
But Baseman fails to clearly specify agents being executed by the processor, and that a processor causes each of the agents in the management device to monitor a state of the device and detect a predetermined event, wherein, in response to a first agent among the agents detecting a predetermined event, information is transmitted and received between the agents based on the detected event, wherein the first agent derives an instruction based on the information transmitted and received between the agents so that an index value of the process is optimized.
However, Song teaches agents being executed by the processor, and that a processor causes each of the agents in the management device to monitor a state of the device and detect a predetermined event [0028-0029, Figs. 3-5 — The multi-agent control system includes a plurality of devices, each of which is controlled by an agent. As depicted in FIG. 2, the devices may include any mechanical, electrical, or physical device that may be used in a flow control system. For example, the devices 203 may include one or more valves 203A-203J. Each valve 203A-203J, for example may include at least one mechanical element configured to adjust one or more flows, at least one sensor configured to detect the one or more flows, and at least one actuator configured to mechanically adjust the at least one of the mechanical element… Each agent in the multi-agent control system may include a processor configured to calculate a local optimized flow plan based on data from the at least one sensor, communicate the local optimized flow plan to at least one other component of the plurality of components to negotiate an optimized system flow plan, and implement the optimized system flow plan though the at least one actuator; 0024 — automatically detect water pipe failures; 0039 — Each agent may be configured to detect system fault and design defect]
wherein, in response to a first agent among the agents detecting a predetermined event, information is transmitted and received between the agents based on the detected event [0032-0038, Fig. 3 — each agent in the system may be configured to compute the optimal operation parameter values in parallel for the devices under the agent's control. Certain parameter values may be shared between agents. For example, two devices controlled by two different agents may share a supply. Each agent calculates parameter values for the supply independently as a function of optimizing the local environment. The agents communicate the respective optimal parameter values to their connected neighboring agents. An optimization solver algorithm may be used to calculate the parameter values for each agent. The optimization solver may also be used to negotiate a global optimized solution for the system; 0044-0048, Figs. 4-5 — The output of the decentralized multi-agent control system, e.g. a set of parameter values or instructions may be accomplish using a parallel coordination scheme in which the following steps are performed for multiple controllers: (A110) all controllers compute their optimal parameter values in parallel or sequence, (A120) controllers communicate their optimal parameter values of interconnecting input and output variables to their neighbors, (A130) controllers update their parameter values based on received values of variables and communicate them to their neighbors. A110-A130 are repeated until convergence (agreement) is reached.],
wherein the first agent derives an instruction based on the information transmitted and received between the agents so that an index value of the process is optimize [0032-0038, Fig. 3 — each agent in the system may be configured to compute the optimal operation parameter values in parallel for the devices under the agent's control. Certain parameter values may be shared between agents. For example, two devices controlled by two different agents may share a supply. Each agent calculates parameter values for the supply independently as a function of optimizing the local environment. The agents communicate the respective optimal parameter values to their connected neighboring agents. An optimization solver algorithm may be used to calculate the parameter values for each agent. The optimization solver may also be used to negotiate a global optimized solution for the system; 0044-0048, Figs. 4-5 — The output of the decentralized multi-agent control system, e.g. a set of parameter values or instructions may be accomplish using a parallel coordination scheme in which the following steps are performed for multiple controllers: (A110) all controllers compute their optimal parameter values in parallel or sequence, (A120) controllers communicate their optimal parameter values of interconnecting input and output variables to their neighbors, (A130) controllers update their parameter values based on received values of variables and communicate them to their neighbors. A110-A130 are repeated until convergence (agreement) is reached.; 0028-0029, Figs. 3-5 — The multi-agent control system includes a plurality of devices, each of which is controlled by an agent. As depicted in FIG. 2, the devices may include any mechanical, electrical, or physical device that may be used in a flow control system. For example, the devices 203 may include one or more valves 203A-203J. Each valve 203A-203J, for example may include at least one mechanical element configured to adjust one or more flows, at least one sensor configured to detect the one or more flows, and at least one actuator configured to mechanically adjust the at least one of the mechanical element… Each agent in the multi-agent control system may include a processor configured to calculate a local optimized flow plan based on data from the at least one sensor, communicate the local optimized flow plan to at least one other component of the plurality of components to negotiate an optimized system flow plan, and implement the optimized system flow plan though the at least one actuator; 0024 — automatically detect water pipe failures; 0039 — Each agent may be configured to detect system fault and design defect; 0052 — the flow control plan is implemented by the agent with the re-optimized one or more local parameter values. Implementation may include transmitting one or more commands, set points, or instructions to devices controlled by the agent.; 0018 — a process or plant is controlled by distributed components].
Baseman and Song are analogous art. They relate to industrial control systems, particularly process control systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above system, as taught by Baseman, by incorporating the above limitations, as taught by Song.
One of ordinary skill in the art would have been motivated to do this modification so that the system is scalable, fault tolerant, and efficient, as taught by Song [0017-0023] .
Regarding claim 2, the combination of Baseman and Song teaches all the limitations of the base claims as outlined above.
Further, Baseman teaches a state estimation model for estimating the state of the substrate processing device based on information obtained from the substrate manufacturing process; wherein the processor is further configured to acquire the state of the substrate processing device that is estimated by inputting the information obtained from the substrate manufacturing process into the state estimation model; and detect the predetermined event in the substrate processing device from the acquired state of the substrate processing device [0059, Fig. 5 — methodology 500 utilizes a plurality of prediction models 512, each prediction model being operative to generate an output result as a function of actual and/or virtual metrology data. In this illustrative embodiment, prediction models 512 includes four prediction models; namely, a single chamber based model 514, a global model across all (or multiple) chambers 516, a metrology based prediction model 518 and a metrology and error-adaptive model 520. The single chamber based model 514 is operative to receive processing variables from module 504 corresponding to one semiconductor processing chamber and construct a prediction model as a function thereof. The global model 516 is operative to receive parameters indicative of chamber capacity matching from module 506 and processing variables from module 504 corresponding to all, or at least a plurality, of the semiconductor processing chambers and to construct a prediction model as a function thereof. The metrology based prediction model 518 is operative to receive actual metrology measurement data from module 508 and variance curve information retrieved from a knowledge base or alternative storage element (e.g., storage unit 402 in FIG. 4) to build a prediction model based on the actual metrology measurement data and variance curve. The metrology and error-adaptive model 520 is operative to receive processing information from metrology tools (e.g., manufacturing tools 412 in FIG. 4) and metrology error and variation information from module 510, and to build a prediction model as a function thereof. Each of these prediction models is described in further detail herein above; 0003-0006 — methods and apparatus for improving run-to-run (R2R) control in a semiconductor manufacturing process; 0049 — FIG. 4 is a block diagram depicting at least a portion of an exemplary system 400 for performing R2R control and sampling optimization, according to an embodiment of the invention. The R2R control system 400 includes a data storage unit 402, or alternative memory (e.g., embedded and/or standalone), a metrology module 404, a sampling optimization module 406, a predictive modeling module 408, and a control module 410. The R2R control system 400 receives prescribed information, including but not limited to actual metrology measurements, from manufacturing tools 412, which includes, for example, semiconductor processing equipment, metrology equipment, automated test equipment (ATE), etc.; 0061, Figs. 4-5 — The manufacturing tools 532 are used to produce wafers and generate processing variables 534, which can be used by subsequent processing steps to adjust the manufacturing process to meet prescribed parameters].
And Baseman teaches a model storage configured in the memory to store model related information [0059 — metrology based prediction model 518 is operative to receive actual metrology measurement data from module 508 and variance curve information retrieved from a knowledge base or alternative storage element (e.g., storage unit 402 in FIG. 4) to build a prediction model based on the actual metrology measurement data and variance curve; 0005-0006, 0088-0089 — an apparatus including a memory and at least one processor that is coupled to the memory].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above system, as taught by the combination of Baseman and Song, by incorporating a model storage unit configured to store a state estimation model.
One of ordinary skill in the art would have been motivated to do this modification in order to be able to retrieve and utilize the stored estimation model at any time it may be required.
Regarding claim 7, the combination of Baseman and Song teaches all the limitations of the base claims as outlined above.
Further, Song teaches each of the agents is connected to all other agents or some other agents among the agents [0030, Fig. 3 — each agent may only communicate with other agents that include shared resources. For example, in the communication connections depicted above the agents by the lines 209, Agent A, for example, communicates with Agents B and C. Agent B communicates with Agent A and D. Agent C communicates with Agent A and D. Agent D communicates with Agent A and C. Other communications arrangements may be used, such as one agent communicating with only one other agent or communicating with all other agents].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above system, as taught by the combination of Baseman and Song, by incorporating the above limitations, as taught by Song.
One of ordinary skill in the art would have been motivated to do this modification so that each agent may access other agents as needed and the system is scalable, fault tolerant, and efficient, as taught by Song [0017-0023, 0030].
Regarding claim 8, the combination of Baseman and Song teaches all the limitations of the base claims as outlined above.
Further, Song teaches a direction in which each of the agents transmits and receives information to and from a second agent among the agents is defined in advance [0030, Fig. 3 — each agent may only communicate with other agents that include shared resources. For example, in the communication connections depicted above the agents by the lines 209, Agent A, for example, communicates with Agents B and C. Agent B communicates with Agent A and D. Agent C communicates with Agent A and D. Agent D communicates with Agent A and C. Other communications arrangements may be used, such as one agent communicating with only one other agent or communicating with all other agents — transmission directions are shown indicated by arrows; 0038 — The failure or fault may then be communicated to other agents when determining consensus. In the example of a failure at pump 201B, Agent B first optimizes the local environment, then transmits values of shared parameter values to other agents. In this scenario, Agent B would inform Agent A that there is no allocation of flow forthcoming from pump 201B. Agent A would then adjust its environment using the data from Agent B].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above system, as taught by the combination of Baseman and Song, by incorporating the above limitations, as taught by Song.
One of ordinary skill in the art would have been motivated to do this modification so that each agent may access other agents and the system is scalable, fault tolerant, and efficient, as taught by Song [0017-0023, 0030].
Regarding claim 13, Baseman teaches a management method of managing a substrate manufacturing process [0003-0006 — methods and apparatus for improving run-to-run (R2R) control in a semiconductor manufacturing process; 0049 — FIG. 4 is a block diagram depicting at least a portion of an exemplary system 400 for performing R2R control and sampling optimization, according to an embodiment of the invention. The R2R control system 400 includes a data storage unit 402, or alternative memory (e.g., embedded and/or standalone), a metrology module 404, a sampling optimization module 406, a predictive modeling module 408, and a control module 410. The R2R control system 400 receives prescribed information, including but not limited to actual metrology measurements, from manufacturing tools 412, which includes, for example, semiconductor processing equipment, metrology equipment, automated test equipment (ATE), etc.; 0061, Figs. 4-5 — The manufacturing tools 532 are used to produce wafers and generate processing variables 534, which can be used by subsequent processing steps to adjust the manufacturing process to meet prescribed parameters], comprising:
detecting, by a unit, a predetermined event by monitoring a state of a substrate processing device that performs the substrate manufacturing process [0049 — FIG. 4 is a block diagram depicting at least a portion of an exemplary system 400 for performing R2R control and sampling optimization, according to an embodiment of the invention. The R2R control system 400 includes a data storage unit 402, or alternative memory (e.g., embedded and/or standalone), a metrology module 404, a sampling optimization module 406, a predictive modeling module 408, and a control module 410. The R2R control system 400 receives prescribed information, including but not limited to actual metrology measurements, from manufacturing tools 412, which includes, for example, semiconductor processing equipment, metrology equipment, automated test equipment (ATE), etc.; 0005-0006, 0088-0089 — an apparatus including a memory and at least one processor that is coupled to the memory; 0058, Fig. 5 — At least a portion of the metrology data is used by module 510 to estimate a metrology error and determine therefrom a deviation from prescribed target values; 0064, Fig. 6 — actual measured wafer parameter is also used to determine metrology error in step 612 by comparing the actual measurement generated from the metrology in step 604 with an expected value for that wafer parameter. A discrepancy between the actual and expected results, taking in account a statistical accuracy of the metrology tools, is used to generate an output indicative of a confidence in the actual measurement. This output is provided to the R2R controller 616. Based on the amount of discrepancy between actual and expected results collected over time, an indication as to how to adjust the process variables; 0061, Figs. 4-5 — The manufacturing tools 532 are used to produce wafers and generate processing variables 534, which can be used by subsequent processing steps to adjust the manufacturing process to meet prescribed parameters], the unit being executed by the processor, and each unit including an event detection model stored in a memory [0058-0059, Fig. 5 — methodology 500 utilizes a plurality of prediction models 512, each prediction model being operative to generate an output result as a function of actual and/or virtual metrology data. In this illustrative embodiment, prediction models 512 includes four prediction models; namely, a single chamber based model 514, a global model across all (or multiple) chambers 516, a metrology based prediction model 518 and a metrology and error-adaptive model 520. The single chamber based model 514 is operative to receive processing variables from module 504 corresponding to one semiconductor processing chamber and construct a prediction model as a function thereof; 0005-0006, 0088-0089 — an apparatus including a memory and at least one processor that is coupled to the memory]; and
wherein the unit derives an instruction based on the information transmitted and received so that an index value of the substrate manufacturing process is optimized, wherein the management method comprises controlling the substrate processing device based on the derived instruction [0056-0062, Figs. 4-5 — control module 410 is coupled with the manufacturing tools 412 and the sampling optimization module 406 in a feedback arrangement for controlling certain aspects of the manufacturing tools (e.g., processing parameters) for R2R control. More particularly, the control module 410 is operative to receive the sampling policy generated by the sampling and optimization module 406 and to control the manufacturing tools 412 in accordance with the sampling policy; 006-0071, Figs. 6-7 — the R2R controller 616 is beneficially able to optimize the sampling policy (e.g., minimizing sampling frequency) while maintaining a desired level of wafer quality or alternative process metric… the sampling policy to be optimized is sampling frequency optimization 716, although the invention is not limited to optimization of sampling frequency],
wherein the event detection model is configured to estimate whether an event requiring a change in a target value of the substrate manufacturing process occurs, using the state of the substrate processing device, and wherein the processor detects the predetermined event by using the event detection model [0058-0061, Fig. 5 — methodology 500 utilizes a plurality of prediction models 512, each prediction model being operative to generate an output result as a function of actual and/or virtual metrology data. In this illustrative embodiment, prediction models 512 includes four prediction models; namely, a single chamber based model 514, a global model across all (or multiple) chambers 516, a metrology based prediction model 518 and a metrology and error-adaptive model 520. The single chamber based model 514 is operative to receive processing variables from module 504 corresponding to one semiconductor processing chamber and construct a prediction model as a function thereof… the R2R controller 530 is operative in a feedback control arrangement to control one or more manufacturing tools 532 in accordance with the sampling policy generated by the sampling/measurement optimization module 528. The manufacturing tools 532 are used to produce wafers and generate processing variables 534, which can be used by subsequent processing steps to adjust the manufacturing process to meet prescribed parameters.; 0005-0006, 0088-0089 — an apparatus including a memory and at least one processor that is coupled to the memory].
But Baseman fails to clearly specify agents being executed by the processor , detecting, by agents, a predetermined event by monitoring a state of a device that performs the process; and in response to a first agent among the agents detecting a predetermined event, transmitting and receiving information between the agents based on the detected event, wherein the first agent derives an instruction to the device based on the information transmitted and received so that an index value of the process is optimized.
However, Song teaches agents being executed by the processor, detecting, by agents, a predetermined event by monitoring a state of a device that performs the process [0028-0029, Figs. 3-5 — The multi-agent control system includes a plurality of devices, each of which is controlled by an agent. As depicted in FIG. 2, the devices may include any mechanical, electrical, or physical device that may be used in a flow control system. For example, the devices 203 may include one or more valves 203A-203J. Each valve 203A-203J, for example may include at least one mechanical element configured to adjust one or more flows, at least one sensor configured to detect the one or more flows, and at least one actuator configured to mechanically adjust the at least one of the mechanical element… Each agent in the multi-agent control system may include a processor configured to calculate a local optimized flow plan based on data from the at least one sensor, communicate the local optimized flow plan to at least one other component of the plurality of components to negotiate an optimized system flow plan, and implement the optimized system flow plan though the at least one actuator; 0024 — automatically detect water pipe failures; 0039 — Each agent may be configured to detect system fault and design defect] and
in response to a first agent among the agents detecting a predetermined event, transmitting and receiving information between the agents based on the detected event [0032-0038, Fig. 3 — each agent in the system may be configured to compute the optimal operation parameter values in parallel for the devices under the agent's control. Certain parameter values may be shared between agents. For example, two devices controlled by two different agents may share a supply. Each agent calculates parameter values for the supply independently as a function of optimizing the local environment. The agents communicate the respective optimal parameter values to their connected neighboring agents. An optimization solver algorithm may be used to calculate the parameter values for each agent. The optimization solver may also be used to negotiate a global optimized solution for the system; 0044-0048, Figs. 4-5 — The output of the decentralized multi-agent control system, e.g. a set of parameter values or instructions may be accomplish using a parallel coordination scheme in which the following steps are performed for multiple controllers: (A110) all controllers compute their optimal parameter values in parallel or sequence, (A120) controllers communicate their optimal parameter values of interconnecting input and output variables to their neighbors, (A130) controllers update their parameter values based on received values of variables and communicate them to their neighbors. A110-A130 are repeated until convergence (agreement) is reached.],
wherein the first agent derives an instruction to the device based on the information transmitted and received so that an index value of the process is optimized [0032-0038, Fig. 3 — each agent in the system may be configured to compute the optimal operation parameter values in parallel for the devices under the agent's control. Certain parameter values may be shared between agents. For example, two devices controlled by two different agents may share a supply. Each agent calculates parameter values for the supply independently as a function of optimizing the local environment. The agents communicate the respective optimal parameter values to their connected neighboring agents. An optimization solver algorithm may be used to calculate the parameter values for each agent. The optimization solver may also be used to negotiate a global optimized solution for the system; 0044-0048, Figs. 4-5 — The output of the decentralized multi-agent control system, e.g. a set of parameter values or instructions may be accomplish using a parallel coordination scheme in which the following steps are performed for multiple controllers: (A110) all controllers compute their optimal parameter values in parallel or sequence, (A120) controllers communicate their optimal parameter values of interconnecting input and output variables to their neighbors, (A130) controllers update their parameter values based on received values of variables and communicate them to their neighbors. A110-A130 are repeated until convergence (agreement) is reached.; 0028-0029, Figs. 3-5 — The multi-agent control system includes a plurality of devices, each of which is controlled by an agent. As depicted in FIG. 2, the devices may include any mechanical, electrical, or physical device that may be used in a flow control system. For example, the devices 203 may include one or more valves 203A-203J. Each valve 203A-203J, for example may include at least one mechanical element configured to adjust one or more flows, at least one sensor configured to detect the one or more flows, and at least one actuator configured to mechanically adjust the at least one of the mechanical element… Each agent in the multi-agent control system may include a processor configured to calculate a local optimized flow plan based on data from the at least one sensor, communicate the local optimized flow plan to at least one other component of the plurality of components to negotiate an optimized system flow plan, and implement the optimized system flow plan though the at least one actuator; 0024 — automatically detect water pipe failures; 0039 — Each agent may be configured to detect system fault and design defect; 0052 — the flow control plan is implemented by the agent with the re-optimized one or more local parameter values. Implementation may include transmitting one or more commands, set points, or instructions to devices controlled by the agent.; 0018 — a process or plant is controlled by distributed components].
Baseman and Song are analogous art. They relate to industrial control systems, particularly process control systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Baseman, by incorporating the above limitations, as taught by Song.
One of ordinary skill in the art would have been motivated to do this modification so that the method is scalable, fault tolerant, and efficient, as suggested by Song [0017-0023].
Regarding claim 14, Baseman teaches a non-transitory computer-readable recording medium having stored therein a management program for causing a computer of a management system for managing a substrate manufacturing process to perform a process [0090-0092 — a machine-readable medium or computer-readable medium containing instructions; 0003-0006 — methods and apparatus for improving run-to-run (R2R) control in a semiconductor manufacturing process; 0049 — FIG. 4 is a block diagram depicting at least a portion of an exemplary system 400 for performing R2R control and sampling optimization, according to an embodiment of the invention. The R2R control system 400 includes a data storage unit 402, or alternative memory (e.g., embedded and/or standalone), a metrology module 404, a sampling optimization module 406, a predictive modeling module 408, and a control module 410. The R2R control system 400 receives prescribed information, including but not limited to actual metrology measurements, from manufacturing tools 412, which includes, for example, semiconductor processing equipment, metrology equipment, automated test equipment (ATE), etc.; 0061, Figs. 4-5 — The manufacturing tools 532 are used to produce wafers and generate processing variables 534, which can be used by subsequent processing steps to adjust the manufacturing process to meet prescribed parameters] comprising:
detecting, by a unit, a predetermined event by monitoring a state of a substrate processing device that performs the substrate manufacturing process [0049 — FIG. 4 is a block diagram depicting at least a portion of an exemplary system 400 for performing R2R control and sampling optimization, according to an embodiment of the invention. The R2R control system 400 includes a data storage unit 402, or alternative memory (e.g., embedded and/or standalone), a metrology module 404, a sampling optimization module 406, a predictive modeling module 408, and a control module 410. The R2R control system 400 receives prescribed information, including but not limited to actual metrology measurements, from manufacturing tools 412, which includes, for example, semiconductor processing equipment, metrology equipment, automated test equipment (ATE), etc.; 0005-0006, 0088-0089 — an apparatus including a memory and at least one processor that is coupled to the memory; 0058, Fig. 5 — At least a portion of the metrology data is used by module 510 to estimate a metrology error and determine therefrom a deviation from prescribed target values; 0064, Fig. 6 — actual measured wafer parameter is also used to determine metrology error in step 612 by comparing the actual measurement generated from the metrology in step 604 with an expected value for that wafer parameter. A discrepancy between the actual and expected results, taking in account a statistical accuracy of the metrology tools, is used to generate an output indicative of a confidence in the actual measurement. This output is provided to the R2R controller 616. Based on the amount of discrepancy between actual and expected results collected over time, an indication as to how to adjust the process variables; 0061, Figs. 4-5 — The manufacturing tools 532 are used to produce wafers and generate processing variables 534, which can be used by subsequent processing steps to adjust the manufacturing process to meet prescribed parameters], the unit being executed by the processor, and each unit including an event detection model stored in a memory [0058-0059, Fig. 5 — methodology 500 utilizes a plurality of prediction models 512, each prediction model being operative to generate an output result as a function of actual and/or virtual metrology data. In this illustrative embodiment, prediction models 512 includes four prediction models; namely, a single chamber based model 514, a global model across all (or multiple) chambers 516, a metrology based prediction model 518 and a metrology and error-adaptive model 520. The single chamber based model 514 is operative to receive processing variables from module 504 corresponding to one semiconductor processing chamber and construct a prediction model as a function thereof; 0005-0006, 0088-0089 — an apparatus including a memory and at least one processor that is coupled to the memory]; and
wherein the unit derives an instruction to the substrate processing device based on information transmitted and received so that an index value of the substrate manufacturing process is optimized, wherein the management method comprises controlling the substrate processing device based on the derived instruction [0056-0062, Figs. 4-5 — control module 410 is coupled with the manufacturing tools 412 and the sampling optimization module 406 in a feedback arrangement for controlling certain aspects of the manufacturing tools (e.g., processing parameters) for R2R control. More particularly, the control module 410 is operative to receive the sampling policy generated by the sampling and optimization module 406 and to control the manufacturing tools 412 in accordance with the sampling policy; 006-0071, Figs. 6-7 — the R2R controller 616 is beneficially able to optimize the sampling policy (e.g., minimizing sampling frequency) while maintaining a desired level of wafer quality or alternative process metric… the sampling policy to be optimized is sampling frequency optimization 716, although the invention is not limited to optimization of sampling frequency],
wherein the event detection model is configured to estimate whether an event requiring a change in a target value of the substrate manufacturing process occurs, using the state of the substrate processing device, and wherein the processor detects the predetermined event by using the event detection model [0058-0061, Fig. 5 — methodology 500 utilizes a plurality of prediction models 512, each prediction model being operative to generate an output result as a function of actual and/or virtual metrology data. In this illustrative embodiment, prediction models 512 includes four prediction models; namely, a single chamber based model 514, a global model across all (or multiple) chambers 516, a metrology based prediction model 518 and a metrology and error-adaptive model 520. The single chamber based model 514 is operative to receive processing variables from module 504 corresponding to one semiconductor processing chamber and construct a prediction model as a function thereof… the R2R controller 530 is operative in a feedback control arrangement to control one or more manufacturing tools 532 in accordance with the sampling policy generated by the sampling/measurement optimization module 528. The manufacturing tools 532 are used to produce wafers and generate processing variables 534, which can be used by subsequent processing steps to adjust the manufacturing process to meet prescribed parameters.; 0005-0006, 0088-0089 — an apparatus including a memory and at least one processor that is coupled to the memory].
But Baseman fails to clearly specify agents being executed by the processor, detecting, by agents, a predetermined event by monitoring a state of a device that performs the process; and in response to a first agent among the agents detecting a predetermined event, transmitting and receiving information between the agents based on the detected event, wherein the first agent derives an instruction to the device based on the information transmitted and received so that an index value of the process is optimized.
However, Song teaches agents being executed by the processor, detecting, by agents, a predetermined event by monitoring a state of a device that performs the process [0028-0029, Figs. 3-5 — The multi-agent control system includes a plurality of devices, each of which is controlled by an agent. As depicted in FIG. 2, the devices may include any mechanical, electrical, or physical device that may be used in a flow control system. For example, the devices 203 may include one or more valves 203A-203J. Each valve 203A-203J, for example may include at least one mechanical element configured to adjust one or more flows, at least one sensor configured to detect the one or more flows, and at least one actuator configured to mechanically adjust the at least one of the mechanical element… Each agent in the multi-agent control system may include a processor configured to calculate a local optimized flow plan based on data from the at least one sensor, communicate the local optimized flow plan to at least one other component of the plurality of components to negotiate an optimized system flow plan, and implement the optimized system flow plan though the at least one actuator; 0024 — automatically detect water pipe failures; 0039 — Each agent may be configured to detect system fault and design defect] and
in response to a first agent among the agents detecting a predetermined event, transmitting and receiving information between the agents based on the detected event [0032-0038, Fig. 3 — each agent in the system may be configured to compute the optimal operation parameter values in parallel for the devices under the agent's control. Certain parameter values may be shared between agents. For example, two devices controlled by two different agents may share a supply. Each agent calculates parameter values for the supply independently as a function of optimizing the local environment. The agents communicate the respective optimal parameter values to their connected neighboring agents. An optimization solver algorithm may be used to calculate the parameter values for each agent. The optimization solver may also be used to negotiate a global optimized solution for the system; 0044-0048, Figs. 4-5 — The output of the decentralized multi-agent control system, e.g. a set of parameter values or instructions may be accomplish using a parallel coordination scheme in which the following steps are performed for multiple controllers: (A110) all controllers compute their optimal parameter values in parallel or sequence, (A120) controllers communicate their optimal parameter values of interconnecting input and output variables to their neighbors, (A130) controllers update their parameter values based on received values of variables and communicate them to their neighbors. A110-A130 are repeated until convergence (agreement) is reached.],
wherein the first agent derives an instruction to the device based on the information transmitted and received so that an index value of the process is optimized [0032-0038, Fig. 3 — each agent in the system may be configured to compute the optimal operation parameter values in parallel for the devices under the agent's control. Certain parameter values may be shared between agents. For example, two devices controlled by two different agents may share a supply. Each agent calculates parameter values for the supply independently as a function of optimizing the local environment. The agents communicate the respective optimal parameter values to their connected neighboring agents. An optimization solver algorithm may be used to calculate the parameter values for each agent. The optimization solver may also be used to negotiate a global optimized solution for the system; 0044-0048, Figs. 4-5 — The output of the decentralized multi-agent control system, e.g. a set of parameter values or instructions may be accomplish using a parallel coordination scheme in which the following steps are performed for multiple controllers: (A110) all controllers compute their optimal parameter values in parallel or sequence, (A120) controllers communicate their optimal parameter values of interconnecting input and output variables to their neighbors, (A130) controllers update their parameter values based on received values of variables and communicate them to their neighbors. A110-A130 are repeated until convergence (agreement) is reached.; 0028-0029, Figs. 3-5 — The multi-agent control system includes a plurality of devices, each of which is controlled by an agent. As depicted in FIG. 2, the devices may include any mechanical, electrical, or physical device that may be used in a flow control system. For example, the devices 203 may include one or more valves 203A-203J. Each valve 203A-203J, for example may include at least one mechanical element configured to adjust one or more flows, at least one sensor configured to detect the one or more flows, and at least one actuator configured to mechanically adjust the at least one of the mechanical element… Each agent in the multi-agent control system may include a processor configured to calculate a local optimized flow plan based on data from the at least one sensor, communicate the local optimized flow plan to at least one other component of the plurality of components to negotiate an optimized system flow plan, and implement the optimized system flow plan though the at least one actuator; 0024 — automatically detect water pipe failures; 0039 — Each agent may be configured to detect system fault and design defect; 0052 — the flow control plan is implemented by the agent with the re-optimized one or more local parameter values. Implementation may include transmitting one or more commands, set points, or instructions to devices controlled by the agent.; 0018 — a process or plant is controlled by distributed components].
Baseman and Song are analogous art. They relate to industrial control systems, particularly process control systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above non-transitory computer-readable recording medium, as taught by Baseman, by incorporating the above limitations, as taught by Song.
One of ordinary skill in the art would have been motivated to do this modification so that the method on the a non-transitory computer-readable recording medium is scalable, fault tolerant, and efficient, as suggested by Song [0017-0023].
Regarding claim 15, the combination of Baseman and Song teaches all the limitations of the base claims as outlined above.
Further, Song teaches that each of the agents includes an analysis model configured to output a target value based on the information transmitted and received between the agents, the information including an allowable target value or a constraint condition [0042 — Each agent may also include an optimization layer. The optimization layer includes one or more modules for optimizing the control system. The optimization layer may include a problem formulator module that inputs the information from the configuration layer and then formulates the problem (objective function and constraints) using a math modeling language module; 0032-0038, Fig. 3 — each agent in the system may be configured to compute the optimal operation parameter values in parallel for the devices under the agent's control. Certain parameter values may be shared between agents. For example, two devices controlled by two different agents may share a supply. Each agent calculates parameter values for the supply independently as a function of optimizing the local environment. The agents communicate the respective optimal parameter values to their connected neighboring agents. An optimization solver algorithm may be used to calculate the parameter values for each agent. The optimization solver may also be used to negotiate a global optimized solution for the system; 0044-0048, Figs. 4-5 — The output of the decentralized multi-agent control system, e.g. a set of parameter values or instructions may be accomplish using a parallel coordination scheme in which the following steps are performed for multiple controllers: (A110) all controllers compute their optimal parameter values in parallel or sequence, (A120) controllers communicate their optimal parameter values of interconnecting input and output variables to their neighbors, (A130) controllers update their parameter values based on received values of variables and communicate them to their neighbors. A110-A130 are repeated until convergence (agreement) is reached.].
Regarding claim 16, the combination of Baseman and Song teaches all the limitations of the base claims as outlined above.
Further, Song teaches that the substrate processing device includes an actuator and the processor controls the actuator of the substrate processing device based on the derived instruction [0032-0038, Fig. 3 — each agent in the system may be configured to compute the optimal operation parameter values in parallel for the devices under the agent's control. Certain parameter values may be shared between agents. For example, two devices controlled by two different agents may share a supply. Each agent calculates parameter values for the supply independently as a function of optimizing the local environment. The agents communicate the respective optimal parameter values to their connected neighboring agents. An optimization solver algorithm may be used to calculate the parameter values for each agent. The optimization solver may also be used to negotiate a global optimized solution for the system; 0044-0048, Figs. 4-5 — The output of the decentralized multi-agent control system, e.g. a set of parameter values or instructions may be accomplish using a parallel coordination scheme in which the following steps are performed for multiple controllers: (A110) all controllers compute their optimal parameter values in parallel or sequence, (A120) controllers communicate their optimal parameter values of interconnecting input and output variables to their neighbors, (A130) controllers update their parameter values based on received values of variables and communicate them to their neighbors. A110-A130 are repeated until convergence (agreement) is reached.; 0028-0029, Figs. 3-5 — The multi-agent control system includes a plurality of devices, each of which is controlled by an agent. As depicted in FIG. 2, the devices may include any mechanical, electrical, or physical device that may be used in a flow control system. For example, the devices 203 may include one or more valves 203A-203J. Each valve 203A-203J, for example may include at least one mechanical element configured to adjust one or more flows, at least one sensor configured to detect the one or more flows, and at least one actuator configured to mechanically adjust the at least one of the mechanical element… Each agent in the multi-agent control system may include a processor configured to calculate a local optimized flow plan based on data from the at least one sensor, communicate the local optimized flow plan to at least one other component of the plurality of components to negotiate an optimized system flow plan, and implement the optimized system flow plan though the at least one actuator; 0024 — automatically detect water pipe failures; 0039 — Each agent may be configured to detect system fault and design defect; 0052 — the flow control plan is implemented by the agent with the re-optimized one or more local parameter values. Implementation may include transmitting one or more commands, set points, or instructions to devices controlled by the agent.; 0018-0020 — a process or plant is controlled by distributed components… centralized control algorithm relies on the system controller 101 to collect data from all the devices and compute the optimal configuration and control actions for the actuators; 0028-0029 — at least one actuator configured to mechanically adjust the at least one of the mechanical element…implement the optimized system flow plan though the at least one actuator.].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above system, as taught by Baseman, by incorporating the above limitations, as taught by Song.
One of ordinary skill in the art would have been motivated to do this modification in order to actually control the manufacturing process, as suggested by Song [0017-0023, 0028-0029].
Note that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
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 BERNARD G. LINDSAY whose telephone number is (571)270-0665. The examiner can normally be reached Monday through Friday from 8:30 AM to 5:30 PM EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached on (571)272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BERNARD G LINDSAY/
Primary Examiner, Art Unit 2119
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