DETAILED ACTION
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 .
Response to Amendment
Applicant's arguments filed 10/27/2025 regarding the rejection under 35 USC 103 have been fully considered and they are persuasive.
Applicant argues, see especially pages 7 (a), that claim 1 are patent eligible because “Regarding the amended claim 1, Jeong fails to teach that "the first implantation (a) recipe is a nominal recipe", as disclosed at least in paragraph [0119] of the present application.” Examiner respectfully disagrees. Jeong teaches the first implantation recipe as being a nominal recipe under the broadest reasonable interpretation of “a nominal recipe”. Jeong teaches in paragraphs [0034] – [0035] the obtaining and the use a first process recipe (i.e. implantation recipe) that under the broadest reasonable interpretation is a nominal recipe in that it is the starting point for training of a machine learning algorithm to create better recipes.
Applicant argues, see especially pages 7 (b), that claims 1, 5, and 9 are patent eligible because “Regarding the amended claims 1, 5, and 9, Jeong fails to teach that "the implantation module and the first measurement module are integrated together to form a process tool", as disclosed at least in paragraph [0116] and FIG. 16 of the present application.” Examiner respectfully disagrees. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). The combination of the Jeong reference and the Levy reference are used to teach this limitation. Jeong in paragraphs [0029], [0054], and [0098] teaches the use of measurements in the implantation and machine learning process. Levy in paragraphs [0022] – [0035] teaches a tool cluster that includes both a measurement tool and a deposition tool (Levy in paragraph [0031] teaches that the deposition tool can be an implantation tool).
Applicant argues, see especially pages 7-8 (c), that claims 1, 5, and 9 are patent eligible because “Regarding the amended claims 1, 5, and 9, Jeong fails to teach that "the process tool to change the current wafer from a first state before an implantation process of the current wafer to a second state after the implantation process of the current wafer", as disclosed at least in paragraphs [0116], [0120] and [0122] of the present application” Examiner respectfully disagrees. Jeong in paragraph [0093]-[0104] teaches the wafer implantation process in which a wafer is transformed from an initial state (i.e. first state before the implantation process) to a different state (i.e. second state after the implantation process).
Applicant argues, see especially pages 8 (d), that claims 1, 5, and 9 are patent eligible because “Regarding the amended claims 1, 5, and 9, Jeong fails to teach that "the first set of (d) date of the current wafer is collected by the first measurement module at the second state thereof" as disclosed at least in paragraph [0122] of the present application.” Examiner respectfully disagrees. Jeong in paragraphs [0098] - [0100] teaches the data that is used to be evaluated and processed by the machine learning model to be a set of data that is collected after the processing of the wafer and changing it from its initial state to a second state.
Applicant argues, see especially pages 8 (e), that claims 3, 7, and 10 are patent eligible because “Regarding the amended claims 3, 7, and 10, Jeong fails to teach that "the artificial intelligence module is integrated in the implantation tool to form an integrated module, such that a graphic user interface (GUI) and a database are shared therebetween" as disclosed at least in paragraph [0142] of the present application.” Examiner respectfully disagrees. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Examiner respectfully disagrees. Jeong in paragraph [] teaches the use of user interfaces and a database that is shared between both the implantation tool and the reinforcement model (i.e. artificial intelligence module). Jeong is combined with Levy which in paragraphs [0060] – [0066] which teaches the use of a user interface that is shared between the feed forward algorithm (i.e. artificial intelligence module) and the implantation tool.
Applicant argues, see especially pages 8 (f), that claims 3, 7, and 10 are patent eligible because “Regarding the amended claims 4, 8, and 12, Jeong fails to teach that "the artificial 10 intelligence module further analyze the feedback data to determine whether to update the second stage of the first implantation recipe or not, wherein if the first stage of the first implantation recipe includes a process deviation, the artificial intelligence module is configured to make correction and update the second stage of the implantation process" as disclosed at least in paragraph [0134] of the present application.” Examiner respectfully disagrees. Jeong in paragraphs [0096] – [0100] teaches the use of a test wafer as a first stage of the implantation process and checks a process condition (i.e. process deviation) to decide if the result is good enough to move on to the device wafer (i.e. second stage) and if it is not the process goes back to the reinforcement learning to update the second stage.
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 (i.e., changing from AIA to pre-AIA ) 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, 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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-2, 4-6, 8-10, and 12 /are rejected under 35 U.S.C. 103 as being unpatentable over Jeong et al. Pub No.: US 20200303266 A1 in view of Cheng Pub No.: US 20220302278 A1, Ko et al. Patent No.: US 8682466 B2 and Levy et al. Pub. No.: US 20180096906 A1.
Regarding claim 1 Jeong teaches A method for controlling an implantation tool through a computing device, comprising: via an implantation module which is controlled by the computing device,1…(Jeong, paragraph 0097 – 0098 and FIG.10B, teaches performing an ion implantation on a test wafer (S172) by applying a process recipe (i.e., first implantation recipe, Jeong, paragraph 0041-0052, teaches the implantation module being part of a computing device that controls it)) wherein the first implantation recipe is a nominal recipe; (Jeong, paragraphs 0034 – 0035, teaches the obtaining and the use a first process recipe (i.e. implantation recipe) that under the broadest reasonable interpretation is a nominal recipe in that it is the starting point for training of a machine learning algorithm to create better recipes.) generating a first set of data of the current wafer by a first measurement module, (Jeong, paragraph 0098 and FIG.10B, teaches the generating of an ion depth profile based on the measurements from the test wafer (S174) (i.e., a first set of data) Jeong, paragraph 0041-0052, teaches the implantation module being part of a computing device that controls it)) 2… to change the current wafer from a first state before an implantation process of the current wafer to a second state after the implantation process of the current wafer, wherein the first set of date of the current wafer is collected by the first measurement module at the second state thereof; (Jeong, paragraph [0093]-[0104], teaches the wafer implantation process in which a wafer is transformed from an initial state (i.e. first state before the implantation process) to a different state (i.e. second state after the implantation process). Jeong, paragraphs [0098] - [0100], teaches the data that is used to be evaluated and processed by the machine learning model to be a set of data that is collected after the processing of the wafer and changing it from its initial state to a second state ) 3… connecting an artificial intelligence module to the first measurement module to analyze the first set of data by using at least one of a table-based control and a formula-based control in order to determine whether the first set of data is within a predetermined range or not; (Jeong, paragraph 0043, teaches the use of a reinforcement learning simulation device unit (i.e., artificial intelligence module). Jeong, paragraph 0107 – 0108, teaches that the artificial intelligence model used in Jeong can be a module.) Jeong, paragraph 0058-0064, teaches the reinforcement learning artificial intelligence module using a formula based control. Jeong, paragraph 0100, FIG 1 and 10B, teaches the reinforcement learning model (i.e., artificial intelligence), determining that a similarity is less than a predetermined value (S178) and returning back to reinforcement learning model (S110) to generate a second implantation recipe that is then applied by the implantation module.)) generating, by the artificial intelligence a second implantation recipe based on the first set of data, and applying the second implantation recipe, by the implantation module, to the implantation tool when the first set of data is not within the predetermined range, (Jeong, paragraph 0100, FIG 1 and 10B, teaches the reinforcement learning model (i.e., artificial intelligence), determining that a similarity is less than a predetermined value (S178) and returning back to reinforcement learning model (S110) to generate a second implantation recipe that is then applied by the implantation module.) wherein the artificial intelligence module updates the first implantation recipe according to a feedback data to provide the second implantation recipe for a next wafer; and (Jeong, paragraph 58-68, teaches the learning process of the reinforcement learning module where as new data comes in from the previous recipe the recipe is updated to create a new recipe that will be utilized for the next wafer.) 4… executing, via the implantation module, the second implantation recipe on the next wafer.(Jeong, paragraph 99, teaches the implantation module executing the second recipe on a semiconductor wafer)
Jeong does not teach 1… placing a current wafer in an implantation chamber for implantation by using a first implantation recipe, wherein the first implantation recipe is executed on the current wafer by the implantation module;… However, Cheng in analogous art teaches this limitation (Cheng, paragraph 0027, teaches an implantation device using an implantation chamber to execute an implantation recipe.)
Further, Jeong does not teach 4… via the implantation module which is controlled by the computing device, placing the next wafer in the implantation chamber for implantation by using the second implantation recipe by… However, Cheng in analogous art teaches this limitation (Cheng, paragraph 0027, teaches an implantation device using an implantation chamber to execute an implantation recipe.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Cheng’s teaching of the use of an implantation chamber with Jeong’s teaching of an implantation tool that uses artificial intelligence. The motivation to do so would be to improve the accuracy of the production of semiconductor wafers using implantation tools (Cheng, paragraphs 0020 and 0131)
Further, the combination Jeong and Cheng does not teach 3… based on one of a data of critical dimension of the current wafer, a data of film thickness of the current wafer, and an electrical data collected by the first measurement module;… (Ko, page 11, column 3, paragraph 4, teaches the gathering of measurement data by a measurement module that includes a critical dimension of the wafer, a film thickness of the wafer and electrical data.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Ko teaching of a measurement tool that measures wafer critical dimensions, film thickness and electrical data with the combination of Jeong and Cheng’s teaching of an artificial intelligent wafer implantation tool. The motivation to do so would be to improve the monitoring of the semiconductor process and increase the data collected to improve the effectiveness of the wafer manufacturing process (Ko, page 10, section Background)
The combination of Jeong, Cheng, and Ko does not teach 2…wherein the implantation module and the first measurement module are integrated together to form a process tool… However, Levy in analogous art teaches this limitation (Levy, paragraphs [0022] – [0035], teaches a tool cluster that integrates both a measurement tool and a deposition tool (Levy in paragraph [0031] teaches that the deposition tool can be an implantation tool))
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Levy teaching of an integrated measurement system with Jeongs teaching of an implantation tool system for wafer manufacturing. The motivation to do so would be to lower the time and cost constraints of wafer manufacturing by providing an all in one system for measuring, analyzing, processing, and tooling of wafers in the production line to prevent the need for multiple machines. (Levy, paragraph 0005-0007)
Regarding claim 2 the combination of Jeong, Cheng and Ko teaches the method for controlling the implantation tool of claim 1, wherein the artificial intelligence module comprises algorithms comprising one or more of the following, alone or in combination: machine learning, hidden Markov models; recurrent neural networks; convolutional neural networks; Bayesian symbolic methods; general adversarial networks; or support vector machines to analyze the first set of data and to generate the second implantation recipe. (Jeong, paragraph 0071 – 0079, teaches the use of multiple different neural networks in the reinforcement learning process which are all directed towards machine learning. Further Jeong, paragraph 0058 - 0068, teaches the reinforcement machine learning system being a Markov decision process that takes in a set of first data and generates a new implantation recipe.)
Regarding Claim 3 the combination of Jeong, Cheng, and Ko teaches the method for controlling the implantation tool of claim 1, wherein the artificial intelligence module (Jeong, paragraph 0070 – 0076, teaches the use of reinforcement learning and neural networks in the process of using the implantation tool. Jeong, paragraph 0107 – 0108, teaches that the artificial intelligence model used in Jeong can be various hardware implementations including a controller and a module. Jeong, Fig. 3, paragraph 0041-0042 teaches that the reinforcement learning simulation device unit (i.e., artificial intelligence module) is connected to the implantation tool in the implantation system however it does not teach the artificial intelligence module being integrated in an etching tool.)
The combination of Jeong, Cheng, and Ko does not teach is integrated in the implantation tool to form an integrated module, such that a graphic user interface (GUI) and a database are shared therebetween.. However, Levy in analogous art teaches this limitation (Levy, paragraph 0066 – 0070, FIG. 2A, FIG. 3, 110, 304, 306, teaches the use of measuring systems integrated into a tool cluster (Levy, paragraph 0031, where the tool cluster can be any of an etching tool, or an implantation tool) used in the process of wafer manufacturing. Levy, paragraph 0021, 0071, 0084, and 0087, teaches the use of feed forward and feedback loops being used in an integrated controller that determines correctable that are repeatedly used to learn a better recipe for the next recipe (i.e., artificial intelligence module integrated in the etching tool). Levy, paragraphs [0060] – [0066], teaches the use of a user interface that is shared between the feed forward algorithm (i.e. artificial intelligence module) and the implantation tool)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Levy teaching of an integrated measurement system with Jeongs teaching of an implantation tool system for wafer manufacturing. The motivation to do so would be to lower the time and cost constraints of wafer manufacturing by providing an all in one system for measuring, analyzing, processing, and tooling of wafers in the production line to prevent the need for multiple machines. (Levy, paragraph 0005-0007)
Regarding Claim 4 the combination of Jeong, Cheng, and Ko teaches the method for controlling the implantation tool of claim 2, further comprising feeding forward, by the implantation module, at least one parameter of the implantation tool to the artificial intelligence module before executing the first implantation recipe on the current wafer. (Jeong, paragraph 0078, teaches the use of a feed forward deep neural network in the reinforcement learning model (i.e., artificial intelligence), Jeong, FIG 1, shows the reinforcement learning process that is executed before the implantation recipe.), wherein the artificial intelligence module further analyze the feedback data to determine whether to update the second stage of the first implantation recipe or not, wherein if the first stage of the first implantation recipe includes a process deviation, the artificial intelligence module is configured to make correction and update the second stage of the implantation process. (Jeong, paragraphs [0096] – [0100], teaches the use of a test wafer as a first stage of the implantation process and checks a process condition (i.e. process deviation) to decide if the result is good enough to move on to the device wafer (i.e. second stage) and if it is not the process goes back to the reinforcement learning to update the second stage.)
Regarding claim 5 Jeong teaches A method for controlling an implantation tool through a computing device, comprising via an implantation module which is controlled by the computing device, 5… , wherein the first implantation recipe is executed on the current wafer by the implantation module (Jeong, paragraph 0097 – 0098 and FIG.10B, teaches performing an ion implantation on a test wafer (S172) by applying a process recipe (i.e., first implantation recipe, Jeong, paragraph 0041-0052, teaches the implantation module being part of a computing device that controls it) ) 6…generating a first set of data of the current wafer by a first measurement module, (Jeong, paragraph 0098 and FIG.10B, teaches the generating of an ion depth profile based on the measurements from the test wafer (S174) (i.e., a first set of data) Jeong, paragraph 0041-0052, teaches the implantation module being part of a computing device that controls it)) 7… to change the current wafer from a first state before an implantation process of the current wafer to a second state after the implantation process of the current wafer, wherein the first set of date of the current wafer is collected by the first measurement module at the second state thereof; (Jeong, paragraph [0093]-[0104], teaches the wafer implantation process in which a wafer is transformed from an initial state (i.e. first state before the implantation process) to a different state (i.e. second state after the implantation process). Jeong, paragraphs [0098] - [0100], teaches the data that is used to be evaluated and processed by the machine learning model to be a set of data that is collected after the processing of the wafer and changing it from its initial state to a second state 7…connecting an artificial intelligence module to the first measurement module to analyze the first set of data by using at least one of a table-based control and a formula-based control, in order to determine whether the first set of data is within a predetermined range or not; (Jeong, paragraph 0043, teaches the use of a reinforcement learning simulation device unit (i.e., artificial intelligence module). Jeong, paragraph 0107 – 0108, teaches that the artificial intelligence model used in Jeong can be a module.) Jeong, paragraph 0058-0064, teaches the reinforcement learning artificial intelligence module using a formula based control.) and 8… by executing, via the implantation module, the first implantation recipe on the next wafer when the first set of data is within the predetermined range. (Jeong, paragraph 58-68, teaches the learning process of the reinforcement learning module where as new data comes in from the previous recipe the recipe is updated to create a new recipe that will be utilized for the next wafer. Jeong, paragraph 0099, teaches that when a wafer is determined to be above or equal to a set value (i.e., predetermined range) the recipe is to be performed on a device wafer (i.e., a next wafer)))
Jeong does not teach 5… placing a current wafer in an implantation chamber for implantation by using a first implantation recipe… (Cheng, paragraph 0027, teaches an implantation device using an implantation chamber to execute an implantation recipe.)
Further, Jeong does not teach 8…via the implantation module which is controlled by the computing device, placing a next wafer in the implantation chamber for implantation by using the first implantation recipe… (Cheng, paragraph 0027, teaches an implantation device using an implantation chamber to execute an implantation recipe.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Cheng’s teaching of the use of an implantation chamber with Jeong’s teaching of an implantation tool that uses artificial intelligence. The motivation to do so would be to improve the accuracy of the production of semiconductor wafers using implantation tools (Cheng, paragraphs 0020 and 0131)
Further, Jeong does not teach 7… based on one of a data of critical dimension of the current wafer, a data of film thickness of the current wafer, and an electrical data collected by the first measurement module;… (Ko, page 11, column 3, paragraph 4, teaches the gathering of measurement data by a measurement module that includes a critical dimension of the wafer, a film thickness of the wafer and electrical data.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Ko teaching of a measurement tool that measures wafer critical dimensions, film thickness and electrical data with the combination of Jeong and Cheng’s teaching of an artificial intelligent wafer implantation tool. The motivation to do so would be to improve the monitoring of the semiconductor process and increase the data collected to improve the effectiveness of the wafer manufacturing process (Ko, page 10, section Background)
The combination of Jeong, Cheng, and Ko does not teach 6…wherein the implantation module and the first measurement module are integrated together to form a process tool… However, Levy in analogous art teaches this limitation (Levy, paragraphs [0022] – [0035], teaches a tool cluster that integrates both a measurement tool and a deposition tool (Levy in paragraph [0031] teaches that the deposition tool can be an implantation tool))
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Levy teaching of an integrated measurement system with Jeongs teaching of an implantation tool system for wafer manufacturing. The motivation to do so would be to lower the time and cost constraints of wafer manufacturing by providing an all in one system for measuring, analyzing, processing, and tooling of wafers in the production line to prevent the need for multiple machines. (Levy, paragraph 0005-0007)
Regarding Claim 6 the combination of Jeong, Cheng and Ko teaches The method for controlling the implantation tool of claim 5, wherein the artificial intelligence module comprises algorithms comprising one or more of the following, alone or in combination: machine learning, hidden Markov models; recurrent neural networks; convolutional neural networks; Bayesian symbolic methods; general adversarial networks; or support vector machines to analyze the first set of data. (Jeong, paragraph 0071 – 0079, teaches the use of multiple different neural networks in the reinforcement learning process which are all directed towards machine learning. Further Jeong, paragraph 0058 - 0068, teaches the reinforcement machine learning system being a Markov decision process that takes in a set of first data and generates a new implantation recipe.)
Regarding Claim 7 the combination of Jeong, Cheng, and Ko teaches the method for controlling the implantation tool of claim 1, wherein the artificial intelligence module (Jeong, paragraph 0070 – 0076, teaches the use of reinforcement learning and neural networks in the process of using the implantation tool. Jeong, paragraph 0107 – 0108, teaches that the artificial intelligence model used in Jeong can be various hardware implementations including a controller and a module. Jeong, Fig. 3, paragraph 0041-0042 teaches that the reinforcement learning simulation device unit (i.e., artificial intelligence module) is connected to the implantation tool in the implantation system however it does not teach the artificial intelligence module being integrated in an etching tool.)
The combination of Jeong, Cheng, and Ko does not teach is integrated in the implantation tool to form an integrated module, such that a graphic user interface (GUI) and a database are shared therebetween.. However, Levy in analogous art teaches this limitation (Levy, paragraph 0066 – 0070, FIG. 2A, FIG. 3, 110, 304, 306, teaches the use of measuring systems integrated into a tool cluster (Levy, paragraph 0031, where the tool cluster can be any of an etching tool, or an implantation tool) used in the process of wafer manufacturing. Levy, paragraph 0021, 0071, 0084, and 0087, teaches the use of feed forward and feedback loops being used in an integrated controller that determines correctable that are repeatedly used to learn a better recipe for the next recipe (i.e., artificial intelligence module integrated in the etching tool). Levy, paragraphs [0060] – [0066], teaches the use of a user interface that is shared between the feed forward algorithm (i.e. artificial intelligence module) and the implantation tool)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Levy teaching of an integrated measurement system with Jeongs teaching of an implantation tool system for wafer manufacturing. The motivation to do so would be to lower the time and cost constraints of wafer manufacturing by providing an all in one system for measuring, analyzing, processing, and tooling of wafers in the production line to prevent the need for multiple machines. (Levy, paragraph 0005-0007)
Regarding Claim 8 the combination of Jeong, Cheng and Ko teaches The method for controlling the implantation tool of claim 6, further comprising: feeding forward, by the implantation module, at least one parameter of the implantation tool to the artificial intelligence module before executing the first implantation recipe on the current wafer. (Jeong, paragraph 0078, teaches the use of a feed forward deep neural network in the reinforcement learning model (i.e., artificial intelligence), Jeong, FIG 1, shows the reinforcement learning process that is executed before the implantation recipe.), wherein the artificial intelligence module further analyze the feedback data to determine whether to update the second stage of the first implantation recipe or not, wherein if the first stage of the first implantation recipe includes a process deviation, the artificial intelligence module is configured to make correction and update the second stage of the implantation process. (Jeong, paragraphs [0096] – [0100], teaches the use of a test wafer as a first stage of the implantation process and checks a process condition (i.e. process deviation) to decide if the result is good enough to move on to the device wafer (i.e. second stage) and if it is not the process goes back to the reinforcement learning to update the second stage.)
Regarding Claim 9 Jeong teaches A method for controlling an implanting implantation tool through a computing device, comprising: via an implantation module which is controlled by the computing device, 9… wherein the first implantation recipe is executed on the current wafer by the implantation module (Jeong, paragraph 0097 – 0098 and FIG.10B, teaches performing an ion implantation on a test wafer (S172) by applying a process recipe (i.e., first implantation recipe, Jeong, paragraph 0041-0052, teaches the implantation module being part of a computing device that controls it)) generating a first set of data of the current wafer by a first measurement module (Jeong, paragraph 0098 and FIG.10B, teaches the generating of an ion depth profile based on the measurements from the test wafer (S174) (i.e., a first set of data) Jeong, paragraph 0041-0052, teaches the implantation module being part of a computing device that controls it)) 10…… to change the current wafer from a first state before an implantation process of the current wafer to a second state after the implantation process of the current wafer, wherein the first set of date of the current wafer is collected by the first measurement module at the second state thereof; (Jeong, paragraph [0093]-[0104], teaches the wafer implantation process in which a wafer is transformed from an initial state (i.e. first state before the implantation process) to a different state (i.e. second state after the implantation process). Jeong, paragraphs [0098] - [0100], teaches the data that is used to be evaluated and processed by the machine learning model to be a set of data that is collected after the processing of the wafer and changing it from its initial state to a second state ) 11… connecting an artificial intelligence module to the first measurement module to analyze the first set of data by using at least one of a table-based control and a formula-based control in order to determine whether the first set of data is within a predetermined range or not; (Jeong, paragraph 0043, teaches the use of a reinforcement learning simulation device unit (i.e., artificial intelligence module). Jeong, paragraph 0107 – 0108, teaches that the artificial intelligence model used in Jeong can be a module.) Jeong, paragraph 0058-0064, teaches the reinforcement learning artificial intelligence module using a formula based control. Jeong, paragraph 0100, FIG 1 and 10B, teaches the reinforcement learning model (i.e., artificial intelligence), determining that a similarity is less than a predetermined value (S178) and returning back to reinforcement learning model (S110) to generate a second implantation recipe that is then applied by the implantation module.)) generating, by the artificial intelligence module, a second implanting recipe based on an update of the first set of data, and applying the second implanting recipe, by the implantation module, to the implanting tool when the first set of data is not within the predetermined range, (Jeong, paragraph 0100, FIG 1 and 10B, teaches the reinforcement learning model (i.e., artificial intelligence), determining that a similarity is less than a predetermined value (S178) and returning back to reinforcement learning model (S110) to generate a second implantation recipe that is then applied by the implantation module.) taking into consideration at least one of the first implanting recipe, an etching recipe, or a deposition recipe; 12… executing, via the implantation module, the second implanting recipe on the next wafer (Jeong, paragraph 99, teaches the implantation module executing the second recipe on a semiconductor wafer)
Jeong does not teach 9… placing a current wafer in an implantation chamber for implantation by using a first implantation recipe… (Cheng, paragraph 0027, teaches an implantation device using an implantation chamber to execute an implantation recipe.)
Further, Jeong does not teach 11…via the implantation module which is controlled by the computing device, placing a next wafer in the implantation chamber for implantation by using the second implantation recipe by... (Cheng, paragraph 0027, teaches an implantation device using an implantation chamber to execute an implantation recipe.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Cheng’s teaching of the use of an implantation chamber with Jeong’s teaching of an implantation tool that uses artificial intelligence. The motivation to do so would be to improve the accuracy of the production of semiconductor wafers using implantation tools (Cheng, paragraphs 0020 and 0131)
Further, the combination Jeong and Cheng does not teach 12… based on one of a data of critical dimension of the current wafer, a data of film thickness of the current wafer, and an electrical data collected by the first measurement module:… (Ko, page 11, column 3, paragraph 4, teaches the gathering of measurement data by a measurement module that includes a critical dimension of the wafer, a film thickness of the wafer and electrical data.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Ko teaching of a measurement tool that measures wafer critical dimensions, film thickness and electrical data with the combination of Jeong and Cheng’s teaching of an artificial intelligent wafer implantation tool. The motivation to do so would be to improve the monitoring of the semiconductor process and increase the data collected to improve the effectiveness of the wafer manufacturing process (Ko, page 10, section Background)
The combination of Jeong, Cheng, and Ko does not teach 10…wherein the implantation module and the first measurement module are integrated together to form a process tool… However, Levy in analogous art teaches this limitation (Levy, paragraphs [0022] – [0035], teaches a tool cluster that integrates both a measurement tool and a deposition tool (Levy in paragraph [0031] teaches that the deposition tool can be an implantation tool))
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Levy teaching of an integrated measurement system with Jeongs teaching of an implantation tool system for wafer manufacturing. The motivation to do so would be to lower the time and cost constraints of wafer manufacturing by providing an all in one system for measuring, analyzing, processing, and tooling of wafers in the production line to prevent the need for multiple machines. (Levy, paragraph 0005-0007)
Regarding Claim 10 the combination of Jeong, Cheng, and Ko teaches the method for controlling the implantation tool of claim 1, wherein the artificial intelligence module (Jeong, paragraph 0070 – 0076, teaches the use of reinforcement learning and neural networks in the process of using the implantation tool. Jeong, paragraph 0107 – 0108, teaches that the artificial intelligence model used in Jeong can be various hardware implementations including a controller and a module. Jeong, Fig. 3, paragraph 0041-0042 teaches that the reinforcement learning simulation device unit (i.e., artificial intelligence module) is connected to the implantation tool in the implantation system however it does not teach the artificial intelligence module being integrated in an etching tool.)
The combination of Jeong, Cheng, and Ko does not teach is integrated in the implantation tool to form an integrated module, such that a graphic user interface (GUI) and a database are shared therebetween.. However, Levy in analogous art teaches this limitation (Levy, paragraph 0066 – 0070, FIG. 2A, FIG. 3, 110, 304, 306, teaches the use of measuring systems integrated into a tool cluster (Levy, paragraph 0031, where the tool cluster can be any of an etching tool, or an implantation tool) used in the process of wafer manufacturing. Levy, paragraph 0021, 0071, 0084, and 0087, teaches the use of feed forward and feedback loops being used in an integrated controller that determines correctable that are repeatedly used to learn a better recipe for the next recipe (i.e., artificial intelligence module integrated in the etching tool). Levy, paragraphs [0060] – [0066], teaches the use of a user interface that is shared between the feed forward algorithm (i.e. artificial intelligence module) and the implantation tool)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Levy teaching of an integrated measurement system with Jeongs teaching of an implantation tool system for wafer manufacturing. The motivation to do so would be to lower the time and cost constraints of wafer manufacturing by providing an all in one system for measuring, analyzing, processing, and tooling of wafers in the production line to prevent the need for multiple machines. (Levy, paragraph 0005-0007)
Regarding Claim 11 the combination of Jeong, Cheng and Ko teaches The method for controlling the deposition implantation tool of claim 10, wherein the artificial intelligence module comprises algorithms comprising one or 4 5 Appl. No. 17/735,293 Reply to Office Action of 06/13/2025 more of the following, alone or in combination: machine learning, hidden Markov models; recurrent neural networks; convolutional neural networks; Bayesian symbolic methods; general adversarial networks; or support vector machines to analyze the first set of data and to generate the second implantation recipe. (Jeong, paragraph 0071 – 0079, teaches the use of multiple different neural networks in the reinforcement learning process which are all directed towards machine learning. Further Jeong, paragraph 0058 - 0068, teaches the reinforcement machine learning system being a Markov decision process that takes in a set of first data and generates a new implantation recipe.)
Regarding Claim 12 the combination of Jeong, Cheng and Ko teaches The method for controlling the deposition implantation tool of claim 11, further comprising feeding forward, by the implantation module, at least one parameter of the deposition implantation tool to the artificial intelligence module before executing the first implanting recipe on the current wafer. (Jeong, paragraph 0078, teaches the use of a feed forward deep neural network in the reinforcement learning model (i.e., artificial intelligence), Jeong, FIG 1, shows the reinforcement learning process that is executed before the implantation recipe.) , wherein the artificial intelligence module further analyze the feedback data to determine whether to update the second stage of the first implantation recipe or not, wherein if the first stage of the first implantation recipe includes a process deviation, the artificial intelligence module is configured to make correction and update the second stage of the implantation process. (Jeong, paragraphs [0096] – [0100], teaches the use of a test wafer as a first stage of the implantation process and checks a process condition (i.e. process deviation) to decide if the result is good enough to move on to the device wafer (i.e. second stage) and if it is not the process goes back to the reinforcement learning to update the second stage.)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/THOMAS BERNARD LANE/Examiner, Art Unit 2142
/HAIMEI JIANG/Primary Examiner, Art Unit 2142