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 .
Responsive to the communication dated 12/18/2025
Claims 1, 3-8, 10-14, and 21-26 are presented for examination
Information Disclosure Statement
The IDS dated 07/31/2025 has been reviewed. See attached.
Drawings
The drawings dated 10/06/2021 have been reviewed. They are accepted.
Specification
The abstract of the disclosure has been reviewed. It contains 117 words and 9 lines and no legal phraseology. It is accepted.
Finality
THIS ACTION IS MADE FINAL. 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 extension fee 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.
Response to Arguments- 35 USC § 101
Applicant’s arguments, see Pages 7-8, filed 12/18/2025, with respect to the rejection of claims 1, 3-8, 10-14, and 21-26 under 35 USC § 101 have been fully considered and are persuasive. The rejection of claims 1, 3-8, 10-14, and 21-26 under 35 USC § 101 has been withdrawn.
Particularly, the newly amended manufacturing control step that controls substrate processing in a dynamic way based on simulation output successfully integrates the claims into a practical application.
Response to Arguments- 35 USC § 103
Applicant's arguments filed 12/18/2025 have been fully considered but they are not persuasive.
Applicant argues that no prior art teaches the newly amended limitations.
Examiner responds by explaining that these new features are taught by the previously cited references. In particular, Nakamura teaches
obtaining, from the machine-learning model, an output reflecting predictive data comprising the candidate set of substrates and one or more dispatching decisions indicative of a time period to initiate the set of operations on the candidate set of substrates; ([Page 14 Par 8] “As shown in FIG. 2, the substrate processing apparatus 10 according to the present embodiment is a polishing apparatus, which is a substantially rectangular housing 11 and a plurality of substrates…“ [Page 19 Par 2] “The action selection unit 82b takes out one action (that is, a new substrate W from the cassette 12 and conveys it to the first processing unit 20) based on the prediction model 85 by inputting the state information acquired by the state information acquisition unit 82a.” [Page 11 Par 8-10] “Recipe information for surface treatment in a processing unit that surface-treats a substrate, substrate information, usage time of consumable members used in the processing unit, continuous operation time of the processing unit, and actual operation time in the processing unit. It is a machine learning device that machine-learns the relationship with the surface treatment time. … Surface treatment in the treatment unit based on recipe information of surface treatment in the treatment unit, substrate information, usage time of consumable members used in the treatment unit, and continuous operation time of the treatment unit. A prediction unit having a prediction model for predicting time, using input information acquired by the input information acquisition unit as input, and predicting and outputting the surface treatment time in the processing unit based on the prediction model.” [Page 11 Par 14 – Page 12 Par 1] “Therefore, by using the trained prediction model generated by such a machine learning device, not only the recipe information of surface treatment in the processing unit and the substrate information, but also the consumable members used in the processing unit. It is possible to accurately predict the surface treatment time in the treatment unit in consideration of the usage time and the continuous operation time of the treatment unit, so that the predicted surface treatment time can be obtained when the time chart is created. Based on this, it becomes possible to accurately determine the timing of starting the transfer of the substrate.”)
([Page 28 Par 5] “Machine learning (supervised learning) of the prediction model 285 is performed using the correspondence relationship with the actual surface treatment time in the processing unit 30) as teacher data… However, the usage time of the consumable member used in the first processing unit 20 (or the second processing unit 30) and the continuous operation time of the first processing unit 20 (or the second processing unit 30) are also taken into consideration. Therefore, it becomes possible to accurately predict the surface treatment time in the first treatment unit 20 (or the second treatment unit 30), whereby when the time chart is created, the substrate is based on the predicted surface treatment time.”) by applying one or more dispatching rules to the predictive data, ([Page 11 Par 14 – Page 12 Par 1] “Therefore, by using the trained prediction model generated by such a machine learning device, not only the recipe information of surface treatment in the processing unit and the substrate information, but also the consumable members used in the processing unit. It is possible to accurately predict the surface treatment time in the treatment unit in consideration of the usage time and the continuous operation time of the treatment unit, so that the predicted surface treatment time can be obtained when the time chart is created. Based on this, it becomes possible to accurately determine the timing of starting the transfer of the substrate.” [Page 4 Par 1] “Therefore, by using the trained prediction model generated by such a machine learning device, the timing of the transfer start of the substrate and the transfer route thereof can be set according to the state at that time in the substrate processing apparatus (unit). It becomes possible to make an appropriate decision (so that the number of processed sheets per hour is large and the waiting time is short).”)
([Page 14 Par 8] “As shown in FIG. 2, the substrate processing apparatus 10 according to the present embodiment is a polishing apparatus, which is a substantially rectangular housing 11 and a plurality of substrates…“ [Page 19 Par 2] “The action selection unit 82b takes out one action (that is, a new substrate W from the cassette 12 and conveys it to the first processing unit 20) based on the prediction model 85 by inputting the state information acquired by the state information acquisition unit 82a.” [Page 7 Par 11] “The first processing unit, the second processing unit, and the cleaning unit are in accordance with a transfer rule that defines the correspondence between the order of the substrates taken out from the cassette and whether to transfer to the first processing unit or the second processing unit.”) an end of the time period predicted by the machine-learning model; ([Page 28 Par 5] “According to the third embodiment as described above, the machine learning device 280 has the recipe information of the surface treatment in the first processing unit 20 … The usage time of the consumable member used in the 20 (or the second processing unit 30), the continuous operation time of the first processing unit 20 (or the second processing unit 30), and the first processing unit 20 (or the second processing unit 30). Machine learning (supervised learning) of the prediction model 285 is performed using the correspondence relationship with the actual surface treatment time in the processing unit 30) as teacher data… However, the usage time of the consumable member used in the first processing unit 20 (or the second processing unit 30) and the continuous operation time of the first processing unit 20 (or the second processing unit 30) are also taken into consideration. Therefore, it becomes possible to accurately predict the surface treatment time in the first treatment unit 20 (or the second treatment unit 30), whereby when the time chart is created, the substrate is based on the predicted surface treatment time. It becomes possible to accurately determine the timing of the start of transportation.”)
initiating, ([Page 24 Par 4] “As shown in FIG. 10, first, when one cycle of processing (that is, processing of a predetermined number or lots) is started by the substrate processing apparatus 10, the control unit 182 of the machine learning apparatus 180 processes the substrate. A processing start notification is received from the control unit 70 of the device 10 (step S110).” [Fig. 10] shows a flowchart of processing operations) ([Page 19 Par 2] “The action selection unit 82b takes out one action (that is, a new substrate W from the cassette 12 and conveys it to the first processing unit 20) based on the prediction model 85 by inputting the state information acquired by the state information acquisition unit 82a.” [Page 7 Par 11] “The first processing unit, the second processing unit, and the cleaning unit are in accordance with a transfer rule that defines the correspondence between the order of the substrates taken out from the cassette and whether to transfer to the first processing unit or the second processing unit.”) over the time period, ([Page 28 Par 5] “Machine learning (supervised learning) of the prediction model 285 is performed using the correspondence relationship with the actual surface treatment time in the processing unit 30) as teacher data… However, the usage time of the consumable member used in the first processing unit 20 (or the second processing unit 30) and the continuous operation time of the first processing unit 20 (or the second processing unit 30) are also taken into consideration. Therefore, it becomes possible to accurately predict the surface treatment time in the first treatment unit 20 (or the second treatment unit 30), whereby when the time chart is created, the substrate is based on the predicted surface treatment time.”)
wherein initiating ([Page 24 Par 4] “As shown in FIG. 10, first, when one cycle of processing (that is, processing of a predetermined number or lots) is started by the substrate processing apparatus 10, the control unit 182 of the machine learning apparatus 180 processes the substrate. A processing start notification is received from the control unit 70 of the device 10 (step S110).” [Fig. 10] shows a flowchart of processing operations)
Nakamura does not explicitly teach wherein the current data comprises at least one of a number of substrates being processed at manufacturing equipment of the manufacturing system or a number of substrates in a manufacturing equipment queue; wherein the simulation system simulates operation of the manufacturing system and generates a simulation output indicating a subset of substrates that were processed without a time constraint violation during each of the simulated set of operations to reach end of a time period, and performing, in view of the simulation results, operations using the subset of substrates over the time period, wherein the process comprises setting a substrate limit for a particular operation of the set of operations based on a number of substrates in the subset indicated by the simulation output and controlling a substrate counter value to prevent substrates from starting at the first operation when the substrate counter value satisfies a threshold criterion.
Shi makes obvious wherein the current data comprises at least one of a number of substrates being processed at manufacturing equipment of the manufacturing system or a number of substrates in a manufacturing equipment queue; ([Par 11] “. To ensure that each machine of a step runs at its full capacity, a certain number of wafers (or wafer lots) should be available, waiting to be processed by the machine of that step. The number of wafers (or wafer lots) waiting to be processed is referred to as work-in-processes or WIP (P.sub.i, S.sub.j), where P.sub.i, i=1, . . . , N represents the i.sup.th product, and S.sub.j, j=1, . . . , M represents the j.sup.th step in the manufacture process of product P.sub.i, and N and M are integers greater than one.”)
wherein the simulation system simulates operation of the manufacturing system and generates a simulation output indicating a subset of substrates that were processed without a time constraint violation during each of the simulated set of operations to reach end of a time period, ([Par 39] “Referring to FIG. 3, at 302, the processing device may run, without imposing Q-time constraints for Q-zones, a simulation of the operation of the semiconductor manufacture plant for a period of time. The simulation may include simulation of machines (or groups of machines) to process wafers (or wafer lots) through different steps.” [Par 43] “In one implementation, instead of using the Q-time requirement of Q-zone, the sum, over all steps of Z.sub.i of the assigned Q-time for each step obtained as described above using the assignment program” [Par 44-45] “At 306, the processing device may run the simulation again, with the original Q-time requirements restored and the initial kanban capacity K.sub.i computed at 304. The simulation may generate a second simulation result. At 308, the processing device may check the second simulation result, generated at 306, to determine whether the performance indices, such as the number of Q-time violations and the number of wafers processed (also called moves), meet performance targets for each Q-zone.” [Par 12] “If a wafer of P.sub.i spends longer than Q-time T(P.sub.i, S.sub.J, S.sub.K) to complete steps S.sub.J to S.sub.K, the wafer is deemed as defective or unreliable.”)
Note that marking a set of wafers as defective or unreliable if they violate the time constraints naturally creates a second set of wafers that are not defective and did not violate the time constraints; e.g. if I have a pile of green and red balls and I separate all the red balls into their own pile, I have also created a pile of green balls.
and performing, in view of the simulation results, ([Par 44-45] “…. The simulation may generate a second simulation result. At 308, the processing device may check the second simulation result, generated at 306, to determine whether the performance indices, such as the number of Q-time violations and the number of wafers processed (also called moves), meet performance targets for each Q-zone… If the violation-move indices are satisfied for all Q-zones, or if a good compromise is achieved, at 312, then the kanban capacity value K(Z) for all Q-zones are sent to the semiconductor plant to be used in the control of the plant.”) operations using the subset of substrates over the time period, ([Par 11-12] “…At each step, the machines (or the machine group) assigned to the step may receive wafers or wafer lots that had been processed by machines used by the previous step and may further produce the wafers for the next step… the manufacture process may be associated with constraints to ensure the quality of the products. One type of constraints is a time constraint (e.g., a time limit) to perform the manufacture process or part of the manufacture process. This time constraint may require that certain consecutive steps (e.g., steps S.sub.J to S.sub.K, inclusive) of a manufacturing process for producing a certain product P.sub.i be completed within a certain time period” [Par 12] “If a wafer of P.sub.i spends longer than Q-time T(P.sub.i, S.sub.J, S.sub.K) to complete steps S.sub.J to S.sub.K, the wafer is deemed as defective or unreliable.”)
wherein the process comprises setting a substrate limit for a particular operation of the set of operations based on a number of substrates in the subset indicated by the simulation output
and controlling a substrate counter value to prevent substrates from starting at the first operation when the substrate counter value satisfies a threshold criterion. ([Par 19] “As the wafers having been processed and exiting from the Q-zone (Z), the WIP level associated with Q-zone(Z) may decrease. When the WIP level is lowered to a pre-determined threshold value (e.g., 10 lots or 20% of the original WIP level), the event may trigger a workflow requesting a supply of wafers from steps before Q-zone(Z). In addition to the WIP level in real time, the kanban system may also include a maximum WIP value. The maximum WIP value may indicate the maximum number of wafers that the Q-zone(Z) may hold. In this disclosure, the maximum WIP value associated with Q-zone(Z) is referred to as the kanban capacity value K(Z). K(Z) may be used to control the manufacture process. During the manufacturing process, if WIP(Z)≥K(Z) for Q-zone Z(P.sub.i, S.sub.J, S.sub.K), then no wafer of product P.sub.i is allowed to be supplied to Q-zone Z(P.sub.i, S.sub.K) and start step S.sub.J. Those wafers of product P.sub.i that have already been supplied to Q-zone Z(P.sub.i, S.sub.J, S.sub.K) may continue to be processed in Q-zone Z(P.sub.i, S.sub.J, S.sub.K). If WIP(Z)<K(Z), then wafers of P.sub.i may be supplied to Q-zone Z(P.sub.i, S.sub.J, S.sub.K) and start step S.sub.J.” [Par 44-45] “…. The simulation may generate a second simulation result. At 308, the processing device may check the second simulation result, generated at 306, to determine whether the performance indices, such as the number of Q-time violations and the number of wafers processed (also called moves), meet performance targets for each Q-zone… If the violation-move indices are satisfied for all Q-zones, or if a good compromise is achieved, at 312, then the kanban capacity value K(Z) for all Q-zones are sent to the semiconductor plant to be used in the control of the plant.”)
Claim Objections
Claims 1, 8, 21 objected to because of the following informalities:
Claims 1, 8, and 21 recite “…the subset of candidate substrates...” This should instead read “…the subset of the candidate substrates...”
Appropriate correction is required.
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.
(1) Claims 1, 3-8, 10-14 and 21-26 are rejected under 35 U.S.C. 103 as being unpatentable over Nakamura (WO 2021054236 A1) in view of Shi (US 20210181726 A1)
Claim 1. Nakamura makes obvious receiving a request to initiate a set of operations to be run at a manufacturing system ([Page 24 Par 4] “As shown in FIG. 10, first, when one cycle of processing (that is, processing of a predetermined number or lots) is started by the substrate processing apparatus 10, the control unit 182 of the machine learning apparatus 180 processes the substrate. A processing start notification is received from the control unit 70 of the device 10 (step S110).” [Fig. 10] shows a flowchart of processing operations ),([Page 22 Par 3] “The state information acquisition unit 182a provides state information including the position of the substrate W in the substrate processing apparatus 10 and the elapsed time of the substrate W located in each of the units 20, 30 and 40 in the unit of the substrate processing apparatus 10”) ([Page 3 Par 16 – Page 4 Par 1] “According to such an aspect, the machine learning device is a prediction model according to the state information including the position of the board at that time in the board processing device and the elapsed time of the board located in each unit in the unit. Based on the above, a trial and error is performed to select whether or not to take out a new substrate from the cassette, and when taking out, which action is to be carried to the first processing unit or the second processing unit, and a predetermined number of sheets are obtained… Therefore, by using the trained prediction model generated by such a machine learning device, the timing of the transfer start of the substrate and the transfer route thereof can be set according to the state at that time in the substrate processing apparatus (unit).”) to determine a candidate set of substrates to be processed ([Page 14 Par 8] “As shown in FIG. 2, the substrate processing apparatus 10 according to the present embodiment is a polishing apparatus, which is a substantially rectangular housing 11 and a plurality of substrates…“ [Page 19 Par 2] “The action selection unit 82b takes out one action (that is, a new substrate W from the cassette 12 and conveys it to the first processing unit 20) based on the prediction model 85 by inputting the state information acquired by the state information acquisition unit 82a.” [Page 7 Par 11] “The first processing unit, the second processing unit, and the cleaning unit are in accordance with a transfer rule that defines the correspondence between the order of the substrates taken out from the cassette and whether to transfer to the first processing unit or the second processing unit.”) during the set of operations; ([Page 24 Par 4] “As shown in FIG. 10, first, when one cycle of processing (that is, processing of a predetermined number or lots) is started by the substrate processing apparatus 10, the control unit 182 of the machine learning apparatus 180 processes the substrate. A processing start notification is received from the control unit 70 of the device 10 (step S110).” [Fig. 10] shows a flowchart of processing operations ) obtaining, from the machine-learning model, an output reflecting predictive data comprising the candidate set of substrates and one or more dispatching decisions indicative of a time period to initiate the set of operations on the candidate set of substrates; ([Page 14 Par 8] “As shown in FIG. 2, the substrate processing apparatus 10 according to the present embodiment is a polishing apparatus, which is a substantially rectangular housing 11 and a plurality of substrates…“ [Page 19 Par 2] “The action selection unit 82b takes out one action (that is, a new substrate W from the cassette 12 and conveys it to the first processing unit 20) based on the prediction model 85 by inputting the state information acquired by the state information acquisition unit 82a.” [Page 11 Par 8-10] “Recipe information for surface treatment in a processing unit that surface-treats a substrate, substrate information, usage time of consumable members used in the processing unit, continuous operation time of the processing unit, and actual operation time in the processing unit. It is a machine learning device that machine-learns the relationship with the surface treatment time. … Surface treatment in the treatment unit based on recipe information of surface treatment in the treatment unit, substrate information, usage time of consumable members used in the treatment unit, and continuous operation time of the treatment unit. A prediction unit having a prediction model for predicting time, using input information acquired by the input information acquisition unit as input, and predicting and outputting the surface treatment time in the processing unit based on the prediction model.” [Page 11 Par 14 – Page 12 Par 1] “Therefore, by using the trained prediction model generated by such a machine learning device, not only the recipe information of surface treatment in the processing unit and the substrate information, but also the consumable members used in the processing unit. It is possible to accurately predict the surface treatment time in the treatment unit in consideration of the usage time and the continuous operation time of the treatment unit, so that the predicted surface treatment time can be obtained when the time chart is created. Based on this, it becomes possible to accurately determine the timing of starting the transfer of the substrate.”) running,([Page 6 Par 15] “It is a machine learning program for making a computer function so as to perform machine learning for a substrate processing apparatus having the above or a simulator of the substrate processing apparatus.” [Page 9 Par 12] “Status information including the position of the substrate in the substrate processing apparatus and the elapsed time of the substrate located in each unit in the unit is acquired, and the acquired status information is input to the input layer, thereby from the output layer. One action is selected based on the output value for performing the action of whether or not to take out a new substrate from the cassette, and the operation of the transport unit is controlled and predetermined so as to perform the selected action.” [Page 19 Par 2] “The action selection unit 82b takes out one action (that is, a new substrate W from the cassette 12 and conveys it to the first processing unit 20) based on the prediction model 85 by inputting the state information acquired by the state information acquisition unit 82a.” [Page 3 Par 16 – Page 4 Par 1] “According to such an aspect, the machine learning device is a prediction model according to the state information including the position of the board at that time in the board processing device and the elapsed time of the board located in each unit in the unit. Based on the above, a trial and error is performed to select whether or not to take out a new substrate from the cassette, and when taking out, which action is to be carried to the first processing unit or the second processing unit, and a predetermined number of sheets are obtained. After the substrate processing is completed, … Therefore, by using the trained prediction model generated by such a machine learning device, the timing of the transfer start of the substrate and the transfer route thereof can be set according to the state at that time in the substrate processing apparatus (unit).” [Page 7 Par 11] “The first processing unit, the second processing unit, and the cleaning unit are in accordance with a transfer rule that defines the correspondence between the order of the substrates taken out from the cassette and whether to transfer to the first processing unit or the second processing unit.” [Page 24 Par 4] “As shown in FIG. 10, first, when one cycle of processing (that is, processing of a predetermined number or lots) is started by the substrate processing apparatus 10, the control unit 182 of the machine learning apparatus 180 processes the substrate. A processing start notification is received from the control unit 70 of the device 10 (step S110).” [Examiner’s note: the machine learning system decides which substrates to process as well as when and how to process them]) ([Page 28 Par 5] “Machine learning (supervised learning) of the prediction model 285 is performed using the correspondence relationship with the actual surface treatment time in the processing unit 30) as teacher data… However, the usage time of the consumable member used in the first processing unit 20 (or the second processing unit 30) and the continuous operation time of the first processing unit 20 (or the second processing unit 30) are also taken into consideration. Therefore, it becomes possible to accurately predict the surface treatment time in the first treatment unit 20 (or the second treatment unit 30), whereby when the time chart is created, the substrate is based on the predicted surface treatment time.”) by applying one or more dispatching rules to the predictive data, and ([Page 11 Par 14 – Page 12 Par 1] “Therefore, by using the trained prediction model generated by such a machine learning device, not only the recipe information of surface treatment in the processing unit and the substrate information, but also the consumable members used in the processing unit. It is possible to accurately predict the surface treatment time in the treatment unit in consideration of the usage time and the continuous operation time of the treatment unit, so that the predicted surface treatment time can be obtained when the time chart is created. Based on this, it becomes possible to accurately determine the timing of starting the transfer of the substrate.” [Page 4 Par 1] “Therefore, by using the trained prediction model generated by such a machine learning device, the timing of the transfer start of the substrate and the transfer route thereof can be set according to the state at that time in the substrate processing apparatus (unit). It becomes possible to make an appropriate decision (so that the number of processed sheets per hour is large and the waiting time is short).”) ([Page 14 Par 8] “As shown in FIG. 2, the substrate processing apparatus 10 according to the present embodiment is a polishing apparatus, which is a substantially rectangular housing 11 and a plurality of substrates…“ [Page 19 Par 2] “The action selection unit 82b takes out one action (that is, a new substrate W from the cassette 12 and conveys it to the first processing unit 20) based on the prediction model 85 by inputting the state information acquired by the state information acquisition unit 82a.” [Page 7 Par 11] “The first processing unit, the second processing unit, and the cleaning unit are in accordance with a transfer rule that defines the correspondence between the order of the substrates taken out from the cassette and whether to transfer to the first processing unit or the second processing unit.”) ([Page 28 Par 5] “According to the third embodiment as described above, the machine learning device 280 has the recipe information of the surface treatment in the first processing unit 20 … The usage time of the consumable member used in the 20 (or the second processing unit 30), the continuous operation time of the first processing unit 20 (or the second processing unit 30), and the first processing unit 20 (or the second processing unit 30). Machine learning (supervised learning) of the prediction model 285 is performed using the correspondence relationship with the actual surface treatment time in the processing unit 30) as teacher data… However, the usage time of the consumable member used in the first processing unit 20 (or the second processing unit 30) and the continuous operation time of the first processing unit 20 (or the second processing unit 30) are also taken into consideration. Therefore, it becomes possible to accurately predict the surface treatment time in the first treatment unit 20 (or the second treatment unit 30), whereby when the time chart is created, the substrate is based on the predicted surface treatment time. It becomes possible to accurately determine the timing of the start of transportation.”) and initiating, ([Page 24 Par 4] “As shown in FIG. 10, first, when one cycle of processing (that is, processing of a predetermined number or lots) is started by the substrate processing apparatus 10, the control unit 182 of the machine learning apparatus 180 processes the substrate. A processing start notification is received from the control unit 70 of the device 10 (step S110).” [Fig. 10] shows a flowchart of processing operations) the candidate substrates ([Page 19 Par 2] “The action selection unit 82b takes out one action (that is, a new substrate W from the cassette 12 and conveys it to the first processing unit 20) based on the prediction model 85 by inputting the state information acquired by the state information acquisition unit 82a.” [Page 7 Par 11] “The first processing unit, the second processing unit, and the cleaning unit are in accordance with a transfer rule that defines the correspondence between the order of the substrates taken out from the cassette and whether to transfer to the first processing unit or the second processing unit.”) over the time period ([Page 28 Par 5] “Machine learning (supervised learning) of the prediction model 285 is performed using the correspondence relationship with the actual surface treatment time in the processing unit 30) as teacher data… However, the usage time of the consumable member used in the first processing unit 20 (or the second processing unit 30) and the continuous operation time of the first processing unit 20 (or the second processing unit 30) are also taken into consideration. Therefore, it becomes possible to accurately predict the surface treatment time in the first treatment unit 20 (or the second treatment unit 30), whereby when the time chart is created, the substrate is based on the predicted surface treatment time.”) wherein initiating ([Page 24 Par 4] “As shown in FIG. 10, first, when one cycle of processing (that is, processing of a predetermined number or lots) is started by the substrate processing apparatus 10, the control unit 182 of the machine learning apparatus 180 processes the substrate. A processing start notification is received from the control unit 70 of the device 10 (step S110).” [Fig. 10] shows a flowchart of processing operations)
Nakamura does not explicitly teach wherein the set of operations comprises one or more operations that each have one or more time constraints; wherein the current data comprises at least one of a number of substrates being processed at manufacturing equipment of the manufacturing system or a number of substrates in a manufacturing equipment queue; running, via a simulation system, a simulation of a set of operations wherein the simulation system simulates operation of the manufacturing system and generates a simulation output indicating a subset of substrates that were processed without a time constraint violation during each of the simulated set of operations to reach end of the time period, and performing, in view of the simulation results, operations using the subset of substrates over the time period, wherein the process comprises setting a substrate limit for a particular operation of the set of operations based on a number of substrates in the subset indicated by the simulation output and controlling a substrate counter value to prevent substrates from starting at the first operation when the substrate counter value satisfies a threshold criterion.
Shi makes obvious wherein the set of operations comprises one or more operations that each have one or more time constraints; ([Par 11] “ Each machine may have a certain capacity to process a certain number of wafers (or a certain number of wafer lots) associated with one or more manufacture steps for a duration of time (e.g., a day or a week).” [Par 12] “The manufacture process may be associated with constraints to ensure the quality of the products. One type of constraints is a time constraint (e.g., a time limit) to perform the manufacture process or part of the manufacture process” [Par 24] “The processing device may execute an assignment program to assign Q-time allowance to each step,”) wherein the current data comprises at least one of a number of substrates being processed at manufacturing equipment of the manufacturing system or a number of substrates in a manufacturing equipment queue; ([Par 11] “. To ensure that each machine of a step runs at its full capacity, a certain number of wafers (or wafer lots) should be available, waiting to be processed by the machine of that step. The number of wafers (or wafer lots) waiting to be processed is referred to as work-in-processes or WIP (P.sub.i, S.sub.j), where P.sub.i, i=1, . . . , N represents the i.sup.th product, and S.sub.j, j=1, . . . , M represents the j.sup.th step in the manufacture process of product P.sub.i, and N and M are integers greater than one.”) running, via a simulation system, a simulation of a set of operations wherein the simulation system simulates operation of the manufacturing system and generates a simulation output indicating a subset of substrates that were processed without a time constraint violation during each of the simulated set of operations to reach end of the time period, ([Par 39] “Referring to FIG. 3, at 302, the processing device may run, without imposing Q-time constraints for Q-zones, a simulation of the operation of the semiconductor manufacture plant for a period of time. The simulation may include simulation of machines (or groups of machines) to process wafers (or wafer lots) through different steps.” [Par 43] “In one implementation, instead of using the Q-time requirement of Q-zone, the sum, over all steps of Z.sub.i of the assigned Q-time for each step obtained as described above using the assignment program” [Par 44-45] “At 306, the processing device may run the simulation again, with the original Q-time requirements restored and the initial kanban capacity K.sub.i computed at 304. The simulation may generate a second simulation result. At 308, the processing device may check the second simulation result, generated at 306, to determine whether the performance indices, such as the number of Q-time violations and the number of wafers processed (also called moves), meet performance targets for each Q-zone.” [Par 12] “If a wafer of P.sub.i spends longer than Q-time T(P.sub.i, S.sub.J, S.sub.K) to complete steps S.sub.J to S.sub.K, the wafer is deemed as defective or unreliable.”) and performing, in view of the simulation results, ([Par 44-45] “…. The simulation may generate a second simulation result. At 308, the processing device may check the second simulation result, generated at 306, to determine whether the performance indices, such as the number of Q-time violations and the number of wafers processed (also called moves), meet performance targets for each Q-zone… If the violation-move indices are satisfied for all Q-zones, or if a good compromise is achieved, at 312, then the kanban capacity value K(Z) for all Q-zones are sent to the semiconductor plant to be used in the control of the plant.”) operations using the subset of substrates over the time period, ([Par 11-12] “…At each step, the machines (or the machine group) assigned to the step may receive wafers or wafer lots that had been processed by machines used by the previous step and may further produce the wafers for the next step… the manufacture process may be associated with constraints to ensure the quality of the products. One type of constraints is a time constraint (e.g., a time limit) to perform the manufacture process or part of the manufacture process. This time constraint may require that certain consecutive steps (e.g., steps S.sub.J to S.sub.K, inclusive) of a manufacturing process for producing a certain product P.sub.i be completed within a certain time period” [Par 12] “If a wafer of P.sub.i spends longer than Q-time T(P.sub.i, S.sub.J, S.sub.K) to complete steps S.sub.J to S.sub.K, the wafer is deemed as defective or unreliable.”) wherein the process comprises setting a substrate limit for a particular operation of the set of operations based on a number of substrates in the subset indicated by the simulation output and controlling a substrate counter value to prevent substrates from starting at the first operation when the substrate counter value satisfies a threshold criterion. ([Par 19] “As the wafers having been processed and exiting from the Q-zone (Z), the WIP level associated with Q-zone(Z) may decrease. When the WIP level is lowered to a pre-determined threshold value (e.g., 10 lots or 20% of the original WIP level), the event may trigger a workflow requesting a supply of wafers from steps before Q-zone(Z). In addition to the WIP level in real time, the kanban system may also include a maximum WIP value. The maximum WIP value may indicate the maximum number of wafers that the Q-zone(Z) may hold. In this disclosure, the maximum WIP value associated with Q-zone(Z) is referred to as the kanban capacity value K(Z). K(Z) may be used to control the manufacture process. During the manufacturing process, if WIP(Z)≥K(Z) for Q-zone Z(P.sub.i, S.sub.J, S.sub.K), then no wafer of product P.sub.i is allowed to be supplied to Q-zone Z(P.sub.i, S.sub.K) and start step S.sub.J. Those wafers of product P.sub.i that have already been supplied to Q-zone Z(P.sub.i, S.sub.J, S.sub.K) may continue to be processed in Q-zone Z(P.sub.i, S.sub.J, S.sub.K). If WIP(Z)<K(Z), then wafers of P.sub.i may be supplied to Q-zone Z(P.sub.i, S.sub.J, S.sub.K) and start step S.sub.J.” [Par 44-45] “…. The simulation may generate a second simulation result. At 308, the processing device may check the second simulation result, generated at 306, to determine whether the performance indices, such as the number of Q-time violations and the number of wafers processed (also called moves), meet performance targets for each Q-zone… If the violation-move indices are satisfied for all Q-zones, or if a good compromise is achieved, at 312, then the kanban capacity value K(Z) for all Q-zones are sent to the semiconductor plant to be used in the control of the plant.”)
Shi is analogous art because it is within the field of semiconductor manufacturing execution systems. It would have been obvious to one of ordinary skill in the art to combine Shi with Nakamura before the effective filing date.
One of ordinary skill in the art would have been motivated to make this combination in order to place constraints to better manage processing and increase throughput of the substrate processing system of Nakamura. As suggested by Shi, when managing manufacturing systems with potentially hundreds of machines, the precise timing of processes is of extreme importance; too few or too many manufacturing products waiting for a machine to finish to move on to the next step can create significant delays ([Par 20] “When there are fewer than sufficient numbers of wafers waiting to be processed at a step, the machines (or machine group) for this step do not have enough parts to process at their full utilization rates. A sufficient number of waiting wafers not only guarantees that the machines run full time, but also reduces the frequency of product changes which will incur additional time to adjust the machines.”). Further, certain semiconductor manufacturing processes need to be done within a particular timespan of each other, and risk damaging the component if it is left waiting too long ([Par 12] “If a wafer of P.sub.i spends longer than Q-time T(P.sub.i, S.sub.J, S.sub.K) to complete steps S.sub.J to S.sub.K, the wafer is deemed as defective or unreliable. The unreliable wafer may require additional tests (or processing) to determine whether the wafer is defective or not. As such, violations of the Q-time requirement T(P.sub.i, S.sub.J, S.sub.K) can cause a decrease of production rate due to defective wafers or an increase of production time due to the additional tests.”) To remedy these issues, Shi introduces a system that places dynamic limits based on time constraints and a Kanban system to optimize efficiency and improve throughput by ensuring all processes and machines in the overall manufacturing process are always running at their maximum capacity ([Par 11] “To maximize the capability of the semiconductor manufacture plant, it is desirable to have all machines of the semiconductor manufacture plant to run at or substantially close to their full capacities. To ensure that each machine of a step runs at its full capacity, a certain number of wafers (or wafer lots) should be available, waiting to be processed by the machine of that step.” [Par 22] “Implementations of the disclosure provide a technical solution that computes, using a processing device (e.g., a hardware processor), a kanban capacity value for a Q-zone that allows maximizing the throughput and at the same time, minimizing the Q-time violation.”) Overall, one of ordinary skill in the art would have recognized that combining the substrate-processing system of Nakamura with the process scheduling and control techniques of Shi would produce a manufacturing system with significantly higher throughput, producing more products per unit time.
Claim 8. The elements of claim 8 are substantially the same as those of claim 1. Therefore, the elements of claim 8 are rejected due to the same reasons as outlined above for claim 1. Further, Shi makes obvious the additional elements of “a memory; and a processing device operatively coupled with the memory, to perform operations” ([Par 25] “MES 102 may include a dispatcher system or a scheduling system that dispatches wafers or wafer lots to machines in a FAB. MES 102 can include a computer system (as shown in FIG. 4) including a processing device (e.g., a central processing unit (CPU)). MES 102 may also include a storage device 112 to store information associated with machines.”)
Claim 21. The elements of claim 21 are substantially the same as those of claim 1. Therefore, the elements of claim 21 are rejected due to the same reasons as outlined above for claim 1. Further, Shi makes obvious the additional elements of “A non-transitory computer-readable medium comprising instructions that, responsive to execution by a processing device, cause the processing device to perform operations comprising” ([Par 25] “MES 102 may include a dispatcher system or a scheduling system that dispatches wafers or wafer lots to machines in a FAB. MES 102 can include a computer system (as shown in FIG. 4) including a processing device (e.g., a central processing unit (CPU)). MES 102 may also include a storage device 112 to store information associated with machines.”)
Claim 3. Nakamura makes obvious wherein the machine-learning model is trained using reinforcement learning. ([Page 17 Par 7] “Reinforcement learning is performed on the timing of the start of transfer and the transfer route thereof by repeating trial and error in the substrate processing apparatus 10 according to the state at that time.”)
Claim 4. Shi makes obvious wherein the one or more time constraints for an operation of the set of operations each comprise an amount of time after completion of the operation during which one or more subsequent operations of the plurality of operations are to be completed. ([Par 12] “This time constraint may require that certain consecutive steps (e.g., steps S.sub.J to S.sub.K, inclusive) of a manufacturing process for producing a certain product P.sub.i be completed within a certain time period. These consecutive steps of a product as a whole are referred to as a Q-zone Z(P.sub.i, S.sub.J, S.sub.K) and the maximum time allowed to perform these steps in the Q-zone is referred to as a Q-time requirement T(P.sub.i, S.sub.J, S.sub.K) associated with the Q-zone.” [Par 13] “Under such a definition, the Q-zone may start after the completion of the first step…” [Par 24] “The processing device may execute an assignment program to assign Q-time allowance to each step”)
Claim 5. Nakamura makes obvious wherein the machine-learning model is trained based on at least one of historical state data, current state data, ([Page 13 Par 8] “A learning model update step that updates the predicted model according to an error between the actual surface treatment time acquired in the actual surface treatment time acquisition step and the surface treatment time predicted in the prediction step.”) or perturbed state data. [Examiner’s note: This claim is written in the alternate format. Therefore, unmapped elements are not given patentable weight]
Claim 6. Shi makes obvious wherein the perturbed state data comprises at least one of current state data or historical state data that has one or more parameters modified or distorted. ([Par 29] “ As discussed above, each Q-zone Z may be associated with a WIP level value WIP(Z) and a kanban capacity value K(Z), where the WIP level may indicate the current number of WIPs in the Q-zone. The WIP level can be determined based on the number of WIPs supplied to the Q-zone and the number of WIPs that have been processed in the Q-zone and exited the Q-zone. [Par 26] “ At each step, there is a corresponding number (WIP #) of wafers (or wafer lots) waiting to be processed. [Par 19] “…the WIP level associated with Q-zone(Z) may decrease. When the WIP level is lowered to a pre-determined threshold value (e.g., 10 lots or 20% of the original WIP level), the event may trigger a workflow requesting a supply of wafers from steps before Q-zone(Z). In addition to the WIP level in real time, the kanban system may also include a maximum WIP value.” [Par 43] “The processing device may make certain adjustments based on these guidelines.” [Par 47] “When adjusting the kanban capacity, the processing device may increase or decrease all Q-zones going through the same machines or machine group in a coordinated way.”)
Claim 7. Nakamura makes obvious wherein output is further indicative of a time period to initiate ([Page 4 Par 1] “Therefore, by using the trained prediction model generated by such
a machine learning device, the timing of the transfer start of the substrate and the transfer route
thereof can be set according to the state at that time in the substrate processing apparatus (unit).”) the set of operations ([Page 24 Par 4] “As shown in FIG. 10, first, when one cycle of processing (that is, processing of a predetermined number or lots) is started by the substrate processing apparatus 10, the control unit 182 of the machine learning apparatus 180 processes the substrate. A processing start notification is received from the control unit 70 of the device 10 (step S110).” [Fig. 10] shows a flowchart of processing operations ) on the candidate set of substrates. ([Page 19 Par 2] “The action selection unit 82b takes out one action (that is, a new substrate W from the cassette 12 and conveys it to the first processing unit 20) based on the prediction model 85 by inputting the state information acquired by the state information acquisition unit 82a.” [Page 7 Par 11] “The first processing unit, the second processing unit, and the cleaning unit are in accordance with a transfer rule that defines the correspondence between the order of the substrates taken out from the cassette and whether to transfer to the first processing unit or the second processing unit.”)
Claims 10-14. The elements of claims 10-14 are substantially the same as those of claims 3-7. Therefore, the elements of claim 10-14 are rejected due to the same reasons as outlined above for claims 3-7. Further, Shi makes obvious the additional elements of claim 8, from which claims 10-14 descend “a memory; and a processing device operatively coupled with the memory, to perform operations” ([Par 25] “MES 102 may include a dispatcher system or a scheduling system that dispatches wafers or wafer lots to machines in a FAB. MES 102 can include a computer system (as shown in FIG. 4) including a processing device (e.g., a central processing unit (CPU)). MES 102 may also include a storage device 112 to store information associated with machines.”)
Claims 22-26. The elements of claims 22-26 are substantially the same as those of claims 3-7. Therefore, the elements of claim 22-26-14 are rejected due to the same reasons as outlined above for claims 3-7. Further, Shi makes obvious the additional elements of claim 21, from which claims 22-26 descend “A non-transitory computer-readable medium comprising instructions that, responsive to execution by a processing device, cause the processing device to perform operations comprising” ([Par 25] “MES 102 may include a dispatcher system or a scheduling system that dispatches wafers or wafer lots to machines in a FAB. MES 102 can include a computer system (as shown in FIG. 4) including a processing device (e.g., a central processing unit (CPU)). MES 102 may also include a storage device 112 to store information associated with machines.”)
Conclusion
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/M.P.M./ Examiner, Art Unit 2187
/JOHN E JOHANSEN/ Examiner, Art Unit 2187