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 .
DETAILED ACTION
2. The action is responsive to the communications filed on 8/10/2026. Claims 1-9 are pending in the case. Claims 1, 7-9 are amended. Claim 1, 7-9 are independent claims. Claims 1-9 are rejected.
Summary of claims
3. Claims 1-9 are pending,
Claims 1, 7-9 are amended,
Claims 1, 7-9 are independent claims,
Claims 1-9 are rejected.
Remarks
4. Applicant’s arguments, see Remarks, filed on 8/10/2026, with respect to the rejection(s) of claim(s) 1-9 under 103 have been fully considered and are not persuasive in view of new rejection ground(s).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
4. Claims 1, 5-9 are rejected under 35 U.S.C. 103 as being unpatentable over David Norman (US Publication 20230107813 A1, hereinafter Norman), and in view of Robert Clark et al (US Publication 20200083080 A1, hereinafter Clark), and further in view of Naoaki Sato et al (US Publication 20080081540 A1, hereinafter Sato).
As for independent claim 1, Norman discloses: An information processing apparatus (Abstract, A method for time constraint management at a manufacturing system), creating a substrate processing schedule for sequentially performing each processing (Abstract, initiate a set of operations to be run at a manufacturing system, wherein the set of operations comprises one or more operations that each have one or more time constraints) on a predetermined number of substrates in a substrate processing apparatus ([0019], The processing device can determine, in view of the time constraints, a number of substrates that can be successfully processed at the manufacturing system within a particular time period. For example, the processing device can identify a set of candidate substrates at the manufacturing system to be processed during the set of operations. In some embodiments, the processing device can determine the set of candidate substrates based on a queue of substrates to be processed at the manufacturing system; [0063], simulation module 107 can execute simulation model 163 to simulate a particular set of operations, simulate one or more operations for a particular time period, simulate a particular number of substrates, simulate substrates having particular identifiers, and so forth) comprising a plurality of polishing units that perform polishing processing on the substrate in parallel ([0075], each operation 410 can correspond to an individual process performed at a front-end manufacturing facility, including, but not limited to, photolithography, deposition, etching, cleaning, ion implantation, chemical and mechanical polishing, etc), a plurality of finishing units that perform finishing processing on the substrate after the polishing processing in order of finishing processes ([0002], Before a substrate becomes a finished product (e.g., a wafer, an electronic device, etc.), the substrate can be processed according to a set of operations each performed at a tool of a manufacturing system), and a plurality of transfer units that perform transfer processing for transferring the substrate ([0088], Transfer chamber 310 can also include a transfer chamber robot 312 configured to transfer substrate 302 between process chambers 314, 316, 318, load lock 320, etc.[0090], A factory interface robot 326 (shown dotted) can be configured to transfer substrates 302 between carriers (also referred to as containers) 322 and load lock 320. Carriers 322 can be a substrate storage carrier or a replacement part storage carrier), wherein the information processing apparatus comprises: a processor, configured to function as: an information acquisition part ([0093], System controller 328 can receive data from sensors included on or within various portions of manufacturing system 300 (e.g., processing chambers 314, 316, 318, transfer chamber 310, load lock 320, etc.). In some embodiments, data received by the system controller 328 can include spectral data and/or non-spectral data for a portion of substrate 302), acquiring recipe information and transfer time information, the recipe information indicating processing content of the polishing processing and the finishing processing ([0027], Manufacturing equipment 122 can perform each process according to a process recipe. A process recipe defines a particular set of operations to be performed for the substrate during the process and can include one or more settings associated with each operation. For example, a deposition process recipe can include a temperature setting for the process chamber, a pressure setting for the process chamber, a flow rate setting for a precursor for a material included in the film deposited on the substrate surface, etc.), the transfer time information indicating a transfer time required for each of following processing as the transfer processing: carry-in processing for carrying the substrate from a substrate carry-in position into a first substrate delivery position, pre-polishing transfer processing for transferring the substrate from the first substrate delivery position to the plurality of polishing units, post-polishing transfer processing for transferring the substrate after the polishing processing from the plurality of polishing units to a second substrate delivery position, pre-finishing transfer processing for transferring the substrate after the polishing processing from the second substrate delivery position to the finishing unit [in a most upstream process], in-finishing transfer processing for transferring the substrate in the middle of the finishing processing between the plurality of finishing units in the order of finishing processes, and carry-out processing for carrying out the substrate after the finishing processing from the finishing unit of [a most downstream process] to a substrate carry-out position ([0023], A processing device can apply a trained machine-learning model to the set of operations to determine a set of candidate substrates for processing during a current or future time period. By applying the trained machine-learning model, the processing device can obtain a dispatching decision indicative of number of substrates that are likely to be successfully processed according to the set of operations over the time period. By obtaining the number of substrates that are likely to be successfully processed over the time period, the processing device can schedule an appropriate number of substrates to be initiated at the set of operations within the time period so that few or no substrates violate a time constraint for the set of operations; [0075], each operation 410 can correspond to an individual process performed at a front-end manufacturing facility, including, but not limited to, photolithography, deposition, etching, cleaning, ion implantation, chemical and mechanical polishing, etc. In other or similar embodiments, each operation can correspond to an individual process performed at a back-end manufacturing facility, including, but not limited to, dicing a completed wafer into individual semiconductor die, testing, assembly, packaging, etc.; please note scheduling data may apply on each operation process of substrate manufacturing including polishing, finishing, transporting, etc.); and a schedule creation part, based on the recipe information and the transfer time information acquired by the information acquisition part, creating the substrate processing schedule by determining a start timing of each of the processing so that a final processing end time during which a last one of the substrate after the finishing processing is carried out to the substrate carry-out position is shortest ([0049], the output can include one or more predictions or inferences. For example, an output prediction or inference can include whether or not a certain candidate set of substrates can start a time-sensitive constraint within a predetermined amount of time (e.g., the next 15 minutes); [0060], Time constraint window manager 110 can determine a number of substrates to start at an initiating operation of a time constraint window for a particular time period. In some embodiments, time constraint window manager 110 can determine the number of substrates to be started at the initiating operation in response to a request (e.g., from production dispatcher system 103, from an operator, etc.); [0077], Operation 3 is to be started at manufacturing equipment 112 so that operations 3, 4, 5, and 6 will be completed within the second time constraint window 412B. In accordance with the previous example, operation 3 can be subject to a time constraint with operation 2. As such, operation 2 is to be started for a substrate so that operations 2 and 3 will be completed for the substrate within the first time constraint window 412A and that operations 3, 4, 5, and 6 will be completed within the second time constraint window 412B; [0052], The validation engine 184 can be capable of validating machine-learning model 190 using a corresponding set of features of a validation set from training set generator 172. Once the model parameters have been optimized, model validation can be performed to determine whether the model has improved and to determine a current accuracy of the deep learning model; please note machine learning model is utilized in the system to optimize the substrate manufacturing process with more efficiency).
Norman discloses managing and scheduling substrate manufacturing including varied operation process, but does not clearly disclose a most upstream process, and a most downstream process, in an analogous art of managing and scheduling substrate manufacturing using machine learning technologies, Clark discloses: in a most upstream process… a most downstream process (Clark: [0076], The integrated metrology or measurement components, together with in-situ equipment module diagnostics and virtual metrology, collects data on-wafer, and collects equipment data upstream and downstream within the process sequence flow; [0119], The system then provides for corrective processing of the workpiece, such as in upstream or downstream processing modules in the process sequence to ameliorate/correct the non-conformities or defects and improve the overall process);
Norman and Clark are analogous arts because they are in the same field of endeavor, monitoring, managing and scheduling substrate manufacturing using machine learning technologies. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Norman using the teachings of Clark to include considering upstream and downstream processing data in the substrate manufacturing process. It would provide Norman’s apparatus with enhanced capabilities of improve the overall process with more accuracy and efficiency.
Further, Norman does not clearly disclose a range of post-polishing finishing start times for the corresponding predetermined number of substrates is within a predetermined value, in another analogous art of managing and scheduling substrate manufacturing, Sato discloses: wherein a range of post-polishing finishing start times for the corresponding predetermined number of substrates is within a predetermined value, wherein the range of the post-polishing finishing start times for the corresponding predetermined number of substrates is a differential value between a minimum value and a maximum value of the post-polishing finishing start times (Sato: [0026], the substrate processing apparatus is further comprises a unit that acquires an expected polishing completion time to polish the objects by the each polishing platen and a unit that calculates a time difference between the expected polishing completion time by the polishing platen for the final stage polishing and an expected polishing completion time by the other each polishing platen. Then, if the time difference is not within an allowable range corresponding to the predetermined time period, the apparatus control unit changes the polishing condition for the polishing platen for the final stage polishing. Alternatively, the apparatus control unit changes the polishing condition for the other each polishing platen. Furthermore, another configuration can be adopted where if the time difference is not within an allowable range corresponding to the predetermined time period, the polishing is stopped; [0110], When the allowed time period T is greater than both the difference T.sub.1 and the difference T.sub.2, the conveyance of the completely polished wafer is started within the allowed time period T);
Norman and Sato are analogous arts because they are in the same field of endeavor, monitoring, managing and scheduling substrate manufacturing. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Norman using the teachings of Sato to include determining if the time difference is not within an allowable range corresponding to the predetermined time period. It would provide Norman’s apparatus with enhanced capabilities of improve the overall process with more accuracy and efficiency.
As for claim 5, Norman-Clark-Sato discloses: wherein the schedule creation part comprises: a schedule inference part, creating the substrate processing schedule with respect to the recipe information and the transfer time information acquired by the information acquisition part by inputting the recipe information and the transfer time information to a learning model that has learned by machine learning a correlation between the recipe information and the transfer time information and the substrate processing schedule for sequentially performing the polishing processing and the finishing processing based on the recipe information as well as the transfer processing requiring the transfer time indicated in the transfer time information on the number of the substrates (Norman: [0019], The processing device can determine, in view of the time constraints, a number of substrates that can be successfully processed at the manufacturing system within a particular time period. For example, the processing device can identify a set of candidate substrates at the manufacturing system to be processed during the set of operations. In some embodiments, the processing device can determine the set of candidate substrates based on a queue of substrates to be processed at the manufacturing system; [0063], simulation module 107 can execute simulation model 163 to simulate a particular set of operations, simulate one or more operations for a particular time period, simulate a particular number of substrates, simulate substrates having particular identifiers, and so forth; Clark: [0209], The pattern recognition engine may correlate an extracted data pattern with a learned attribute on the workpiece. The pattern recognition engine may implement a correlation engine 1126 that accesses one or more learned attributes, 1128, such as in a database 1132 in order to correlate measured data in the form of a data pattern with a learned attribute; [0341], Specific operations that can be performed by an autobot can include a sequence average; a sequence ordering; a scalar product among a first and a second vector; a multiplication of a first matrix and a second matrix; a time sequence derivative with respect to time; a sequence autocorrelation computation; a cross-correlation operation between a first and a second sequence; [0355], AKM 2330 can include functional relationships that can link two or more concepts like an average, a standard deviation, a range, a correlation, a principal component analysis (PCA), a multi-scale principal component analysis (MSPCA), a wavelet or substantially any basis function, etc).
As for claim 6, Norman-Clark-Sato discloses: a schedule evaluation part, evaluating the substrate processing schedule created by the schedule creation part, and calculating an evaluation index of the substrate processing schedule as an evaluation result, wherein the evaluation index comprises at least one of:
number of the substrates processed per unit time; takt time of each of the processing; rate-determining processing among the processings that requires a longest processing time; and degree of variation in a post-polishing finishing start time from an end timing of the polishing processing to the start timing of the finishing processing in the most upstream process (Norman: [0035], Examples of data processes can include, and are not limited to import data, compress data, index data, filter data, perform a mathematical function on data, etc.; Clark: [0376], OKM 2610 comprises domain independent concepts, which can include a measurement (e.g., measurements from measurement modules), a sequence, a comparator, a case, a case-index, a case-parameter, a cause, an influence, a causal-dependency, an evidence, a causal-graph, etc.; [0397], index C.sub.a to account for the fact that output assets generated in an autonomous system comprised of multiple conglomerate tools can be transported from a first conglomerate (N) to a second conglomerate (N′). Thus, the composite symbol for tracking performance associated with a transfer of assets (e.g., as a part of a multi-step fabrication process) can read C.sub.α;N.fwdarw.N′.).
As per independent claim 7, it recites features that are substantially same as those features claimed by claim 1, thus the rationales for rejecting claim 1 are incorporated herein. Further, Clark discloses: an evaluation index inference part (Clark: [0143], the captured measurement data 550 may then be directed to control system 522 and further evaluated and analyzed to determine a particular action for the substrate measured; [0370], Basic knowledge with output (e.g., relationship(output(κ.sub.E),T)) can facilitate autobots in CWM 2540 to construct and evaluate various functional descriptions that involve data for κ.sub.E and T in order to identify the best function that can describe a relationship among κ.sub.E and T; [0404], The autonomous system 3104 can evaluate, as illustrated by 3146, a set of performance metrics {P.sup.(C).sub.N-1.fwdarw.N} leading to a specific degradation vector (not shown), based at least in part on an expected performance, for substantially all operational components (e.g., tool, group or conglomerate tool) in the process station 3110.sub.N), inferring an evaluation index with respect to the recipe information and the transfer time information acquired by the information acquisition part by inputting the recipe information and the transfer time information to a learning model that has learned by machine learning a correlation between the recipe information and the transfer time information and the evaluation index when evaluating the substrate processing schedule for sequentially performing the polishing processing and the finishing processing based on the recipe information as well as the transfer processing requiring the transfer time indicated in the transfer time information on the number of the substrates (Clark: [0376], OKM 2610 comprises domain independent concepts, which can include a measurement (e.g., measurements from measurement modules), a sequence, a comparator, a case, a case-index, a case-parameter, a cause, an influence, a causal-dependency, an evidence, a causal-graph, etc.; [0397], index C.sub.a to account for the fact that output assets generated in an autonomous system comprised of multiple conglomerate tools can be transported from a first conglomerate (N) to a second conglomerate (N′). Thus, the composite symbol for tracking performance associated with a transfer of assets (e.g., as a part of a multi-step fabrication process) can read C.sub.α;N.fwdarw.N′.), based on the correlation between the recipe information and the transfer time information and the evaluation index (Clark: [0211], The correlation engine/component 1126 might also be used for predicting whether or not a non-conformity exists. Specifically, measured data is obtained in two or more areas of a workpiece. The correlation engine 1126 uses the measured data from the multiple locations and based on a correlation of the location measured data, the existence of a non-conformity may be predicted).
Norman and Clark are analogous arts because they are in the same field of endeavor, monitoring, managing and scheduling substrate manufacturing using machine learning technologies. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Norman using the teachings of Clark to include using an index to evaluate the functions in order to determine the best process. It would provide Norman’s apparatus with enhanced capabilities of optimize the overall process with more accuracy and efficiency.
Further, Sato discloses: based on the correlation between the recipe information and the transfer time information and the evaluation index (Sato: [0043], showing the relationship between the elapsed time after the final stage of polishing is completed and the production yield);
Norman and Sato are analogous arts because they are in the same field of endeavor, monitoring, managing and scheduling substrate manufacturing. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Norman using the teachings of Sato to include determining the relationship between the elapsed time after the final stage of polishing is completed and the production yield. It would provide Norman’s apparatus with enhanced capabilities of improve the overall process with more accuracy and efficiency.
As per independent claim 8, it recites features that are substantially same as those features claimed by claim 1, thus the rationales for rejecting claim 1 are incorporated herein. Further, Norman-Clark-Sato discloses: A machine learning apparatus, generating a learning model for creating a substrate processing schedule for sequentially performing each processing on a predetermined number of substrates in a substrate processing apparatus (Norman: Abstract, The method further includes applying a machine-learning model to the current data to determine a candidate set of substrates to be processed during the set of operations. The method further includes initiating the set of operations on the candidate set of substrates based on an output of the machine-learning model) comprising a plurality of polishing units that perform polishing processing on the substrate in parallel, a plurality of finishing units that perform finishing processing on the substrate after the polishing processing in order of finishing processes, and a plurality of transfer units that perform transfer processing for transferring the substrate, wherein the machine learning apparatus comprises: a learning data storage part, storing a plurality of sets of learning data configured in which recipe information and transfer time information are input data (Norman: [0030], Data stores 140, 150, and 160 can be a memory (e.g., random access memory), a drive (e.g., a hard drive, a flash drive), a database system, or another type of component or device capable of storing data. Data stores 140, 150, and 160 can include multiple storage components (e.g., multiple drives or multiple databases) that can span multiple computing devices; [0092], System controller 328 can include a data storage device (e.g., one or more disk drives and/or solid state drives), a main memory, a static memory, a network interface, and/or other components), the recipe information indicating processing content of the polishing processing and the finishing processing, the transfer time information indicating a transfer time required for each of following processing as the transfer processing: carry-in processing for carrying the substrate from a substrate carry-in position into a first substrate delivery position, pre-polishing transfer processing for transferring the substrate from the first substrate delivery position to the plurality of polishing units, post-polishing transfer processing for transferring the substrate after the polishing processing from the plurality of polishing units to a second substrate delivery position, pre-finishing transfer processing for transferring the substrate after the polishing processing from the second substrate delivery position to the finishing unit in a most upstream process, in-finishing transfer processing for transferring the substrate in the middle of the finishing processing between the plurality of finishing units in the order of finishing processes, and carry-out processing for carrying out the substrate after the finishing processing from the finishing unit of a most downstream process to a substrate carry-out position, and the substrate processing schedule for sequentially performing the polishing processing and the finishing processing based on the recipe information as well as the transfer processing requiring the transfer time indicated in the transfer time information on the number of the substrates is output data; a machine learning part, causing the learning model to learn a correlation between the input data and the output data by inputting the plurality of sets of learning data to the learning model (Norman: Abstract, The method further includes applying a machine-learning model to the current data to determine a candidate set of substrates to be processed during the set of operations. The method further includes initiating the set of operations on the candidate set of substrates based on an output of the machine-learning model); and
a learned model storage part, storing the learning model that has learned the correlation by the machine learning part (Norman: [0030], Data stores 140, 150, and 160 can be a memory (e.g., random access memory), a drive (e.g., a hard drive, a flash drive), a database system, or another type of component or device capable of storing data. Data stores 140, 150, and 160 can include multiple storage components (e.g., multiple drives or multiple databases) that can span multiple computing devices; [0092], System controller 328 can include a data storage device (e.g., one or more disk drives and/or solid state drives), a main memory, a static memory, a network interface, and/or other components), wherein the learning model that has learned the correlation creates the substrate processing schedule based on the correlation between the input data and the output data so that a difference between post-polishing finishing start times of any two of the substrates is within a predetermined value, wherein the post-polishing finishing start time is a time period from an end timing of the polishing processing to a start timing of the finishing processing in the most upstream process (Sato: [0026], the substrate processing apparatus is further comprises a unit that acquires an expected polishing completion time to polish the objects by the each polishing platen and a unit that calculates a time difference between the expected polishing completion time by the polishing platen for the final stage polishing and an expected polishing completion time by the other each polishing platen. Then, if the time difference is not within an allowable range corresponding to the predetermined time period, the apparatus control unit changes the polishing condition for the polishing platen for the final stage polishing. Alternatively, the apparatus control unit changes the polishing condition for the other each polishing platen. Furthermore, another configuration can be adopted where if the time difference is not within an allowable range corresponding to the predetermined time period, the polishing is stopped; [0110], When the allowed time period T is greater than both the difference T.sub.1 and the difference T.sub.2, the conveyance of the completely polished wafer is started within the allowed time period T);
Norman and Sato are analogous arts because they are in the same field of endeavor, monitoring, managing and scheduling substrate manufacturing. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Norman using the teachings of Sato to include determining if the time difference is not within an allowable range corresponding to the predetermined time period. It would provide Norman’s apparatus with enhanced capabilities of improve the overall process with more accuracy and efficiency.
As per independent claim 9, it recites features that are substantially same as those features claimed by claim 1, thus the rationales for rejecting claim 1 are incorporated herein. Further, Norman-Clark discloses: A machine learning apparatus, generating a learning model for evaluating a substrate processing schedule for sequentially performing each processing on a predetermined number of substrates in a substrate processing apparatus (Norman: Abstract, The method further includes applying a machine-learning model to the current data to determine a candidate set of substrates to be processed during the set of operations. The method further includes initiating the set of operations on the candidate set of substrates based on an output of the machine-learning model) comprising a plurality of polishing units that perform polishing processing on the substrate in parallel, a plurality of finishing units that perform finishing processing on the substrate after the polishing processing in order of finishing processes, and a plurality of transfer units that perform transfer processing for transferring the substrate, wherein the machine learning apparatus comprises: a learning data storage part, storing a plurality of sets of learning data configured in which recipe information and transfer time information are input data, the recipe information indicating processing content of the polishing processing and the finishing processing, the transfer time information indicating a transfer time required for each of following processing as the transfer processing: carry-in processing for carrying the substrate from a substrate carry-in position into a first substrate delivery position, pre-polishing transfer processing for transferring the substrate from the first substrate delivery position to the plurality of polishing units, post-polishing transfer processing for transferring the substrate after the polishing processing from the plurality of polishing units to a second substrate delivery position, pre-finishing transfer processing for transferring the substrate after the polishing processing from the second substrate delivery position to the finishing unit in a most upstream process, in-finishing transfer processing for transferring the substrate in the middle of the finishing processing between the plurality of finishing units in the order of finishing processes, and carry-out processing for carrying out the substrate after the finishing processing from the finishing unit of a most downstream process to a substrate carry-out position, and an evaluation index when evaluating the substrate processing schedule for sequentially performing the polishing processing and the finishing processing based on the recipe information as well as the transfer processing requiring the transfer time indicated in the transfer time information on the number of the substrates is output data (Clark: [0376], OKM 2610 comprises domain independent concepts, which can include a measurement (e.g., measurements from measurement modules), a sequence, a comparator, a case, a case-index, a case-parameter, a cause, an influence, a causal-dependency, an evidence, a causal-graph, etc.; [0397], index C.sub.a to account for the fact that output assets generated in an autonomous system comprised of multiple conglomerate tools can be transported from a first conglomerate (N) to a second conglomerate (N′). Thus, the composite symbol for tracking performance associated with a transfer of assets (e.g., as a part of a multi-step fabrication process) can read C.sub.α;N.fwdarw.N′.); a machine learning part, causing the learning model to learn a correlation between the input data and the output data by inputting the plurality of sets of learning data to the learning model (Norman: Abstract, The method further includes applying a machine-learning model to the current data to determine a candidate set of substrates to be processed during the set of operations. The method further includes initiating the set of operations on the candidate set of substrates based on an output of the machine-learning model); and a learned model storage part, storing the learning model that has learned the correlation by the machine learning part (Norman: [0030], Data stores 140, 150, and 160 can be a memory (e.g., random access memory), a drive (e.g., a hard drive, a flash drive), a database system, or another type of component or device capable of storing data. Data stores 140, 150, and 160 can include multiple storage components (e.g., multiple drives or multiple databases) that can span multiple computing devices; [0092], System controller 328 can include a data storage device (e.g., one or more disk drives and/or solid state drives), a main memory, a static memory, a network interface, and/or other components).
Norman and Clark are analogous arts because they are in the same field of endeavor, monitoring, managing and scheduling substrate manufacturing using machine learning technologies. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Norman using the teachings of Clark to include using an index to evaluate the functions in order to determine the best process. It would provide Norman’s apparatus with enhanced capabilities of optimize the overall process with more accuracy and efficiency.
Furthermore, Sato discloses: wherein in response to determining that the recipe information and the transfer time information are input to the learning model that has learned the correlation, the learning model that has learned the correlation infers the evaluation index based on the correlation between the input data and the output data (Sato: [0043], showing the relationship between the elapsed time after the final stage of polishing is completed and the production yield);
Norman and Sato are analogous arts because they are in the same field of endeavor, monitoring, managing and scheduling substrate manufacturing. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Norman using the teachings of Sato to include determining the relationship between the elapsed time after the final stage of polishing is completed and the production yield. It would provide Norman’s apparatus with enhanced capabilities of improve the overall process with more accuracy and efficiency.
6. Claims 2-4 are rejected under 35 U.S.C. 103 as being unpatentable over Norman and Clark and Sato as applied on claim 1, and further in view of Mitsunori Sugiyama et al (US Publication 20150290766 A1, hereinafter Sugiyama).
As for claim 2, Norman-Clark-Sato discloses: and a mathematical optimization part, creating the substrate processing schedule by performing mathematical optimization in which the start timing of each of the processing is determined with a processing order condition that defines an order of performing each of the processing and a simultaneous processing condition that defines which of the processings are able or unable to be simultaneously performed as a constraint for the mathematical optimization, and with minimizing the final processing end time as an objective function for the mathematical optimization that comprises the polishing time and the finishing time calculated by the processing time calculator and the transfer time indicated in the transfer time information as a variable (Norman: [0035], perform a mathematical function on data; [0048], Each node contains parameters (e.g., weights) to apply to the input values. Each node therefore essentially inputs the input values into a multivariate function (e.g., a non-linear mathematical transformation) to produce an output value), Norman-Clark does not expressly disclose calculating a polishing time required, a finishing time required, in another managing substrate manufacturing process, Sugiyama discloses: a processing time calculator, based on the recipe information, calculating a polishing time required for the polishing processing and a finishing time required for the finishing processing (Sugiyama: [0013], the control section calculates the provisional times of arrival of a substrate to be newly loaded at the polishing section, the cleaning section and the transporting section when creating the time table for the substrate to be newly loaded into the substrate processing apparatus, compares the provisional arrival times and the treatment ending times or the scheduled treatment ending times of a substrate precedently loaded into the substrate processing apparatus in the polishing section, the cleaning section and the transporting section; [0051], the control section 5 calculates the times of arrival of each wafer at respective treatment sections (polishing sections, transporting sections, and cleaning sections) on the basis of the operating times of each wafer predicted in step S102 (step S103). Then, the control section 5 calculates the standby time of each wafer in each treatment section (step S104));
Norman and Clark and Sugiyama are analogous arts because they are in the same field of endeavor, monitoring, managing and scheduling substrate manufacturing using machine learning technologies. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Norman using the teachings of Sugiyama to include calculating polishing time and finishing time. It would provide Norman’s apparatus with enhanced capabilities of improve the overall process with more accuracy and efficiency.
As for claim 3, Norman-Clark-Sato-Sugiyama discloses: wherein the mathematical optimization part performs the mathematical optimization further with a post-polishing finishing start range condition that defines a range of a post-polishing finishing start time from an end timing of the polishing processing to the start timing of the finishing processing in the most upstream process as the constraint (Sugiyama: [0013], the control section calculates the provisional times of arrival of a substrate to be newly loaded at the polishing section, the cleaning section and the transporting section when creating the time table for the substrate to be newly loaded into the substrate processing apparatus, compares the provisional arrival times and the treatment ending times or the scheduled treatment ending times of a substrate precedently loaded into the substrate processing apparatus in the polishing section, the cleaning section and the transporting section; [0051], the control section 5 calculates the times of arrival of each wafer at respective treatment sections (polishing sections, transporting sections, and cleaning sections) on the basis of the operating times of each wafer predicted in step S102 (step S103). Then, the control section 5 calculates the standby time of each wafer in each treatment section (step S104)).
As for claim 4, Norman-Clark-Sato-Sugiyama discloses: wherein the mathematical optimization part performs the mathematical optimization further with minimizing a total value, an average value or a maximum value of the post-polishing finishing start time as the objective function (Clark: [0338], A knowledge base may contain knowledge elements, or concepts. In an aspect, each knowledge element can be associated with two numeric attributes: a suitability (ξ) and an inertia (.Math.) of a knowledge element, or concept; collectively such attributes determine a priority of a concept. A well-defined function, e.g., a weighted sum, a geometric average, of these two numeric attributes can be a concept's situation score (σ); [0341], Specific operations that can be performed by an autobot can include a sequence average; a sequence ordering; a scalar product among a first and a second vector; a multiplication of a first matrix and a second matrix; a time sequence derivative with respect to time; a sequence autocorrelation computation; a cross-correlation operation between a first and a second sequence; a decomposition of a function in a complete set of basic functions; [0355], AKM 2330 can include functional relationships that can link two or more concepts like an average, a standard deviation, a range, a correlation, a principal component analysis (PCA), a multi-scale principal component analysis (MSPCA), a wavelet or substantially any basis function, etc.).
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 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hua Lu whose telephone number is 571-270-1410 and fax number is 571-270-2410. The examiner can normally be reached on Mon-Fri 9:00 am to 6:00 pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman can be reached on 571-272-3644. The fax phone number for the organization where this application or proceeding is assigned is 703-273-8300.
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/Hua Lu/
Primary Examiner, Art Unit 2118
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