DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Remarks
2. This Office action is responsive to the Request for Continued Examination (RCE) filed under 37 CFR §1.53(d) for the instant application on June 10, 2026. Applicants have properly set forth the RCE, which has been entered into the application, and an examination on the merits follows herewith.
Claims 1-20 have been examined and rejected. This Office action is responsive to the amendment filed on April 27, 2026, which has been entered in the above identified application.
Claim Rejections - 35 USC § 103
3. 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.
4. Claims 1-2 and 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Tetiker (Pub. No. US 2019/0049937) in view of Mehr et al (Pub. No. US 2018/0341248).
Claims 1-2, 4-5
4-1. Regarding claim 1, Tetiker teaches the claim comprising: causing a manufacturing equipment to release at least one gaseous substance through a plurality of regions of a pixelated showerhead at a specified flow rate for a manufacturing process while a wafer is being processed in a processing chamber, by disclosing an inductively coupled plasma reactor used in etch operations having a chuck 1017 that is configured to receive and hold a semiconductor wafer 1019 upon which etching process is performed [paragraph 146] and where process gases are flowed through one or more showerheads having internal channels and holes that allow delivery of process gases into a chamber [paragraphs 148-149].
Tetiker teaches obtaining, from a trained digital simulation associated with the manufacturing equipment, a gas profile for each of the plurality of regions of the pixelated showerhead, wherein the trained digital simulation is executed concurrently with the manufacturing process by disclosing a system controller that implements an optimized etch profile model (EPM) and adjusts operation of an etcher apparatus in response to computed etch profiles generated using the optimized EPM [paragraph 156]. The EPM computes a theoretically determined etch profile from a set of input etch reaction parameters (independent variables) characterizing the underlying physical and chemical etch processes and reaction mechanisms [paragraph 40]. The independent input variables – such as plasma parameters – are determined by using an etch chamber plasma model, wherein such models may calculate the applicable input EPM parameters from various process parameters over which the process engineer does have control (e.g., by turning a knob) – e.g., chamber environment parameters such as flow rate [paragraph 44]. Various experiments are performed to train and optimize the EPM [paragraph 48]. Examiner notes that the term “profile” has been interpreted as a set of data portraying the significant features of something. In this case, the gas profile may be interpreted at least as the computed effect the gas flow has on etch profile of the EPM.
Tetiker teaches simulating a wafer feature based at least in part on the obtained gas profile, the simulation predicting at least one feature of a processed wafer to be generated by the manufacturing process, by disclosing that the EPM is used to simulate the etch profile evolution of a substrate feature over time – i.e., the time-dependent changes in the shape of a feature at various spatial locations on the feature’s surface – by calculating reaction rates associated with the etch process at each of these spatial locations which result from an incident flux of etchant and deposition species characteristic of the plasma conditions set up in the reaction chamber, and do so over the course of the simulated etch process [paragraph 24]. The model may be used for monitoring and processing in situ optical signals, in real time, to generate geometric etch parameter from the in situ optical information (e.g., real time end point or critical dimension monitoring). [paragraph 107].
Although Tetiker discloses that the optimized EPM may be integrated with an etcher apparatus to determine appropriate adjustments to process parameters, such as to vary one or more values of the set of independent input parameters [Tetiker, paragraph 106], Tetiker does not expressly teach causing, based on the prediction, performance of one or more corrective actions associated with the manufacturing equipment during the manufacturing process and prior to completion of processing of the wafer, wherein, prior to causing performance of the one or more corrective actions, the method further comprises simulating, using the trained digital simulation, a predicted effect of the one or more corrective actions on the wafer feature. Mehr discloses a method for classification of object defects during a fabrication manufacturing process that comprises: a) providing a training data set, wherein the training data set comprises fabrication process simulation data, fabrication process characterization data, in-process inspection data, post-build inspection data, or any combination thereof, for a plurality of object design geometries that are the same as or different from the object; b) providing one or more sensors, wherein the one or more sensors provide real-time data for one or more object properties; c) providing a processor programmed to provide a classification of detected object defects using a machine learning algorithm that has been trained using the training data set of step (a), wherein the real-time data from the one or more sensors is provided as input to the machine learning algorithm and allows the classification of detected object defects to be adjusted in real-time [paragraph 26]. Real-time process characterization data is obtained using any of a variety of sensors, measurement tools, or inspection tools that monitor various process parameters in real-time during a manufacturing process [paragraph 136]. The real-time process characterization data is provided as input to the machine learning algorithm to adjust one or more process control parameters in real-time to compensate or correct for part defects as they arise during the build process [paragraphs 27, 137]. The machine learning algorithm used to run the automated process control may be configured to adjust the process control parameters in real-time as necessary to maximize a reward function (or to minimize a loss function) in order to optimize the deposition process [paragraph 139]. Once a current build state of a part has been determined, a reinforcement learning algorithm uses the current state information and the model developed using past training data to predict a proposed action that will maximize a reward function by determining a corresponding reward for each set of “next N states” [paragraph 140; figure 8]. This is effectively a simulation/evaluation of alternative corrective actions before selecting one. In one example, an artificial neural network (ANN) architecture is used for real-time, adaptive process control to predict a future build state based on current build state and a set of actions [paragraph 178; figure 10]. This would help improve the quality of the manufactured product. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use real-time process characterization data process parameters monitored in real-time during the manufacturing process as input to a machine learning algorithm to compensate or correct for defects as they arise during the manufacturing process, as taught by Mehr. This would help improve the quality of the manufactured product.
4-2. Regarding claim 2, Tetiker-Mehr teach all the limitations of claim 1, further comprising: comparing the simulated wafer feature to a target reference wafer feature, by disclosing that when optimizing model parameters, calculating an error metric which is indicative of (related to, quantifies, etc.) the difference between the experimental and computed etch profiles over all the different sets of values for the input parameters [Tetiker, paragraph 59, lines 9-15].
4-3. Regarding claim 4, Tetiker-Mehr teach all the limitations of claim 1, wherein the one or more corrective actions associated with the manufacturing equipment comprises a change in a flow rate of the at least one gaseous substance through at least one of the plurality of regions of the pixelated showerhead, by disclosing that parameters adjusted by the system controller relate to process conditions, including process gas compositions and flow rates [Tetiker, paragraph 170].
4-4. Regarding claim 5, Tetiker-Mehr teach all the limitations of claim 1, further comprising: receiving metrology information from at least one in situ, ex situ, or onboard metrology tool, prior to obtaining the gas profile information from the digital simulation, by disclosing one or more in-situ or offline metrology tools may be used to measure the experimental etch profiles which result from experimental etch process operations [Tetiker, paragraph 51]. The result of the etch experiments and metrology procedures is a set of measured etch profiles that are used as inputs to train, optimize, and improve the computerized etch profile models [Tetiker, paragraph 52].
5. Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Tetiker (Pub. No. US 2019/0049937), in view of Mehr et al (Pub. No. US 2018/0341248), in view of Yamamoto et al (U.S. Patent No. 12,400,191).
5-1. Regarding claim 3, Tetiker-Mehr teach all the limitations of claim 1. Tetiker-Mehr do not expressly teach the claim further comprising: determining that the simulated wafer feature deviates from a target reference wafer feature by at least a predetermined threshold amount, prior to causing performance of the one or more corrective actions. Yamamoto discloses performing a process by a semiconductor manufacturing apparatus according to process parameters output from an apparatus control controller [column 10, lines 34-39] and acquiring physical sensor data measured by sensors from the semiconductor manufacturing apparatus that is performing the process [column 10, lines 39-43]. A simulation execution unit executes a simulation by a simulation model according to the same process parameters as those for the semiconductor manufacturing apparatus that is performing the process, and calculates a virtual sensor data and virtual process result data [column 10, lines 44-49]. When the process being performed by the semiconductor manufacturing apparatus is ended, a simulation result determination unit compares the physical sensor data and the virtual sensor data for the same position and time, to determine whether the physical sensor data and the virtual sensor data for the same position and time match each other [column 10, line 63 to column 11, line 3]. When it is determined that the physical sensor data and the virtual sensor data do not match each other, the simulation result determination unit 110 performs a process parameter adjusting process for optimizing the process parameters, so as to obtain customer's desired results after the process is performed [column 11, lines 4-9]. When the difference between the physical sensor data and the virtual sensor data for the same position and time exceeds a predetermined threshold value, optimization of the process parameters is stopped, and the difference is handled through the editing of the simulation model or the maintenance of the semiconductor manufacturing apparatus [column 11, lines 10-17]. This would save resources by undertaking certain corrective action when differences in data are significant enough. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to perform the corrective actions when the simulated wafer feature deviates from the target reference wafer by a predetermined amount, as taught by Yamamoto. This would save resources by undertaking certain corrective action when differences in data are significant enough.
6. Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Tetiker (Pub. No. US 2019/0049937), in view of Mehr et al (Pub. No. US 2018/0341248), in view of Dehghanimohammadabadi (Pub. No. US 2023/0004149).
6-1. Regarding claim 6, Tetiker-Mehr teach all the limitations of claim 1. Tetiker-Mehr do not expressly teach the claim further comprising: obtaining from the digital simulation, an updated gas profile for each of the plurality of regions of the pixelated showerhead based on the predicted effect of the one or more corrective actions, prior to causing performance of the one or more corrective actions. Dehghanimohammadabadi discloses a machine learning system for optimizing a production process [paragraph 5]. A simulation system 120 at block 122 includes the creation of a digital twin model of a physical production process managed by a production planning system 110 [paragraph 117, lines 4-7; Figure 1A]. The simulation system 120, at block 124, receives as input, real-time data of the production process (block 114) as well as input parameters from the analytics system 130 (block 134) [paragraph 117, lines 7-13]. Next, the simulation system 120 at block 126 runs the digital twin model using the real-time data and input simulation parameters to produce output parameters [paragraph 118, lines 6-12]. These output parameters represent a predictive effect that the input parameters from the analytics system 130 (i.e. a corrective action) will have on the product being produced. The analytics system 130 at block 138 receives the output parameter of the simulation from the simulation system 120 [paragraph 18, lines 12-14] and at block 136 analyzes the output parameters using a machine learning algorithm to determine whether or not the production process has been sufficiently optimized [paragraph 19, lines 1-5]. If not, the analytics system 130 at block 134 iteratively learns by programming new input parameters so that the simulation runs again at blocks 124-126 [paragraph 19, lines 5-7]. Running the digital twin model again at block 126 using the real-time data of the production process and the new input parameters would cause an update in the simulation of all aspects of the production process affected by the new input parameters. If the analytics system 130 at block 140 determines that optimal values have been achieved, those optimal values may be sent to the production planning system for execution in the real physical process byte analytics system 130 at block 142 [paragraph 20]. This would allow the production process to be continually tuned based on real-time production data, so that the process can be modified and optimized with newly determined parameters [paragraph 21]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to receive updated profiles from running a simulation based on the predicted effects of input parameters, as taught by Dehghanimohammadabadi. This would allow for a more optimal result when corrections are made.
7. Claims 7-8, 10-11, 13-14, 16-17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Tetiker (Pub. No. US 2019/0049937), in view of Babayan et al (U.S. Patent No. 12,406,865), and further in view of Mehr et al (Pub. No. US 2018/0341248),
7-1. Regarding claim 7, Tetiker teaches the claim comprising: causing an individual tunable heater to heat a plurality of regions of an electrostatic chuck to a specified temperature for a manufacturing process, wherein the individual tunable heater is associated with at least one region of the electrostatic chuck, while a wafer is being processed in a processing chamber, by disclosing an inductively coupled plasma reactor used in etch operations having a chuck that is configured to receive and hold a semiconductor wafer 1019 upon which etching process is performed and that is positioned within the lower level chamber near the bottom inner surface that can be electrically charged using an RF power supply [paragraph 146]. The chuck may operate at certain temperatures depending on the etching process operation and specific recipe [paragraph 153].
Tetiker teaches obtaining, from a trained digital simulation associated with the electrostatic chuck, a heat profile for each of the plurality of regions of the electrostatic chuck, wherein the trained digital simulation is executed concurrently with the manufacturing process, by disclosing a system controller that implements an optimized etch profile model (EPM) and adjusts operation of an etcher apparatus in response to computed etch profiles [paragraph 156]. The EPM computes a theoretically determined etch profile from a set of input etch reaction parameters (independent variables) characterizing the underlying physical and chemical etch processes and reaction mechanisms [paragraph 40]. The independent input variables – such as plasma parameters – are determined by using an etch chamber plasma model, wherein such models may calculate the applicable input EPM parameters from various process parameters over which the process engineer does have control (e.g., by turning a knob) – e.g., chamber environment parameters such as wafer temperature, ICP coil currents, bias voltages/power, and the like [paragraph 44]. Various experiments are performed to train and optimize the EPM [paragraph 48]. Examiner notes that the term “profile” has been interpreted as a set of data portraying the significant features of something. In this case, the heat profile may be interpreted at least as the computed effect the temperature of the electrostatic chuck has on the etch profile of the EPM.
Tetiker teaches simulating a wafer feature based at least in part on the obtained heat profile, the simulation predicting at least one feature of a processed wafer to be generated by the manufacturing process, by disclosing that the EPM is used to simulate the etch profile evolution of a substrate feature over time – i.e., the time-dependent changes in the shape of a feature at various spatial locations on the feature’s surface – by calculating reaction rates associated with the etch process at each of these spatial locations which result from an incident flux of etchant and deposition species characteristic of the plasma conditions set up in the reaction chamber, and do so over the course of the simulated etch process [paragraph 24]. The model may be used for monitoring and processing in situ optical signals, in real time, to generate geometric etch parameter from the in situ optical information (e.g., real time end point or critical dimension monitoring). [paragraph 107].
Tetiker does not expressly teach a plurality of tunable heaters to heat a plurality of regions of an electrostatic chuck to a specified temperature for a manufacturing process, wherein at least one of the plurality of individually tunable heaters is associated with at least one region of the electrostatic chuck. Babayan discloses fitting an electrostatic chuck with an array of heater elements to allow for the temperature of the ESC to be adjusted differently at different positions across the surface of the ESC [column 2, lines 53-56]. The array of heater elements provides independent temperature measurements at different positions on the electrostatic chuck, allowing the heaters to be operated to even out the temperature, or for the chuck to be modified to correct for the inconsistent temperatures [column 2, lines 46-52]. The thermal sensor data from the heater elements can be used to feed open loop models or a time based closed loop control PID (Proportional-Integral-Derivative) scheme [column 3, lines 10-12]. This would provide more accurate information on temperature variations across the chuck, thus allowing the user to more closely monitor the etching process. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use, in the inductively coupled plasma reactor that implements an optimized etch profile model (EPM) to adjust operation of an etcher apparatus in response to computed etch profiles of Tetiker, an array of heater elements on the electrostatic chuck, as taught by Babayan. This would provide more accurate information on temperature variations across the chuck, thus allowing the user to more closely monitor the etching process.
Although Tetiker-Babyan disclose that the optimized EPM may be integrated with an etcher apparatus to determine appropriate adjustments to process parameters, such as to vary one or more values of the set of independent input parameters [Tetiker, paragraph 106], Tetiker-Babyan do not expressly teach causing, based on the prediction, performance of one or more corrective actions associated with the manufacturing process during the manufacturing process and prior to completion of processing of the wafer, wherein, prior to causing performance of the one or more corrective actions, the method further comprises simulating, using the trained digital simulation, a predicted effect of the one or more corrective actions on the wafer feature. Mehr discloses a method for classification of object defects during a fabrication manufacturing process that comprises: a) providing a training data set, wherein the training data set comprises fabrication process simulation data, fabrication process characterization data, in-process inspection data, post-build inspection data, or any combination thereof, for a plurality of object design geometries that are the same as or different from the object; b) providing one or more sensors, wherein the one or more sensors provide real-time data for one or more object properties; c) providing a processor programmed to provide a classification of detected object defects using a machine learning algorithm that has been trained using the training data set of step (a), wherein the real-time data from the one or more sensors is provided as input to the machine learning algorithm and allows the classification of detected object defects to be adjusted in real-time [paragraph 26]. Real-time process characterization data is obtained using any of a variety of sensors, measurement tools, or inspection tools that monitor various process parameters in real-time during a manufacturing process [paragraph 136]. The real-time process characterization data is provided as input to the machine learning algorithm to adjust one or more process control parameters in real-time to compensate or correct for part defects as they arise during the build process [paragraphs 27, 137]. The machine learning algorithm used to run the automated process control may be configured to adjust the process control parameters in real-time as necessary to maximize a reward function (or to minimize a loss function) in order to optimize the deposition process [paragraph 139]. Once a current build state of a part has been determined, a reinforcement learning algorithm uses the current state information and the model developed using past training data to predict a proposed action that will maximize a reward function by determining a corresponding reward for each set of “next N states” [paragraph 140; figure 8]. This is effectively a simulation/evaluation of alternative corrective actions before selecting one. In one example, an artificial neural network (ANN) architecture is used for real-time, adaptive process control to predict a future build state based on current build state and a set of actions [paragraph 178; figure 10]. This would help improve the quality of the manufactured product. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use real-time process characterization data process parameters monitored in real-time during the manufacturing process as input to a machine learning algorithm to compensate or correct for defects as they arise during the manufacturing process, as taught by Mehr. This would help improve the quality of the manufactured product.
7-2. Regarding claim 8, Tetiker-Babayan-Mehr teach all the limitations of claim 7, further comprising: comparing the simulated wafer feature to a target reference wafer feature, by disclosing that when optimizing model parameters, calculating an error metric which is indicative of (related to, quantifies, etc.) the difference between the experimental and computed etch profiles over all the different sets of values for the input parameters [Tetiker, paragraph 59, lines 9-15].
7-3. Regarding claim 10, Tetiker-Babayan-Mehr teach all the limitations of claim 7, wherein the one or more corrective actions associated with the manufacturing process comprises a change in a rate of heating applied by at least one of the individually tunable heaters to the at least one region of the electrostatic chuck, by disclosing that parameters adjusted by the system controller relate to process conditions, including temperatures (e.g., substrate holder and showerhead temperatures) [Tetiker, paragraph 170].
7-4. Regarding claim 11, Tetiker-Babayan-Mehr teach all the limitations of claim 7, further comprising: receiving metrology information from at least one in situ, ex situ, or onboard metrology tool, prior to obtaining the gas profile information from the digital simulation, by disclosing one or more in-situ or offline metrology tools may be used to measure the experimental etch profiles which result from experimental etch process operations [Tetiker, paragraph 51]. The result of the etch experiments and metrology procedures is a set of measured etch profiles that are used as inputs to train, optimize, and improve the computerized etch profile models [Tetiker, paragraph 52].
Claims 13-14, 16-17
7-5. Regarding claim 13, Tetiker teaches the claim comprising: causing an individual tunable RF field generator to generate a plasma across a plurality of regions in a processing chamber conducting a manufacturing process, wherein the individual tunable radiofrequency (RF) field generator is associated with at least one region of the processing chamber, while a wafer is being processed in a processing chamber by disclosing an inductively coupled plasma reactor used in etch operations having a chuck 1017 that is configured to receive and hold a semiconductor wafer 1019 upon which etching process is performed [paragraph 146] and having an RF power supply configured to supply RF power to coil 1033 [paragraphs 147, 150].
Tetiker teaches obtaining, from a trained digital simulation associated with the processing chamber, a plasma profile for each of the plurality of regions of the processing chamber, wherein the trained digital simulation is executed concurrently with the manufacturing process, by disclosing a system controller that implements an optimized etch profile model (EPM) and adjusts operation of an etcher apparatus in response to computed etch profiles [paragraph 156]. The EPM computes a theoretically determined etch profile from a set of input etch reaction parameters (independent variables) characterizing the underlying physical and chemical etch processes and reaction mechanisms [paragraph 40]. The independent input variables – such as plasma parameters – are determined by using an etch chamber plasma model, wherein such models may calculate the applicable input EPM parameters from various process parameters over which the process engineer does have control (e.g., by turning a knob) – e.g., chamber environment parameters such as plasma power [paragraph 44]. Various experiments are performed to train and optimize the EPM [paragraph 48]. Examiner notes that the term “profile” has been interpreted as a set of data portraying the significant features of something. In this case, the plasma profile may be interpreted at least as the computed effect the plasma power has on etch profile of the EPM.
Tetiker teaches simulating a wafer feature based at least in part on the obtained plasma profile, the simulation predicting at least one feature of a processed wafer to be generated by the manufacturing process, by disclosing that the EPM is used to simulate the etch profile evolution of a substrate feature over time – i.e., the time-dependent changes in the shape of a feature at various spatial locations on the feature’s surface – by calculating reaction rates associated with the etch process at each of these spatial locations which result from an incident flux of etchant and deposition species characteristic of the plasma conditions set up in the reaction chamber, and do so over the course of the simulated etch process [paragraph 24]. The model may be used for monitoring and processing in situ optical signals, in real time, to generate geometric etch parameter from the in situ optical information (e.g., real time end point or critical dimension monitoring). [paragraph 107].
Tetiker does not expressly teach a plurality of individually tunable RF field generators to generate a plasma across a plurality of regions in a processing chamber conducting a manufacturing process, wherein at least one of the plurality of individually tunable RF field generators is associated with at least one region of the processing chamber. Babayan discloses a plasma system having a radio frequency (RF) source coupled to a showerhead assembly for powering the showerhead assembly to facilitate generation of plasma between a faceplate of the showerhead assembly and a heated pedestal [column 11, lines 1-5]. The RF source may include a HFRF power source and a low frequency radio frequency power source, and may be coupled to other portions of the processing chamber body, such as the pedestal, to facilitate plasma generation [column 11, lines 8-13]. This would allow the system to separately manage the ion flux and the ion energy, thus providing greater control of the etching process. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use, in the inductively coupled plasma reactor that implements an optimized etch profile model (EPM) to adjust operation of an etcher apparatus in response to computed etch profiles of Tetiker, a plurality of tunable RF field generators, as taught by Babayan. This would allow the system to separately manage the ion flux and the ion energy, thus providing greater control of the etching process.
Although Tetiker-Babyan disclose that the optimized EPM may be integrated with an etcher apparatus to determine appropriate adjustments to process parameters, such as to vary one or more values of the set of independent input parameters [Tetiker, paragraph 106], Tetiker-Babyan do not expressly teach causing, based on the prediction, performance of one or more corrective actions associated with the manufacturing process during the manufacturing process and prior to completion of processing of the wafer, wherein, prior to causing performance of the one or more corrective actions, the method further comprises simulating, using the trained digital simulation, a predicted effect of the one or more corrective actions on the wafer feature. Mehr discloses a method for classification of object defects during a fabrication manufacturing process that comprises: a) providing a training data set, wherein the training data set comprises fabrication process simulation data, fabrication process characterization data, in-process inspection data, post-build inspection data, or any combination thereof, for a plurality of object design geometries that are the same as or different from the object; b) providing one or more sensors, wherein the one or more sensors provide real-time data for one or more object properties; c) providing a processor programmed to provide a classification of detected object defects using a machine learning algorithm that has been trained using the training data set of step (a), wherein the real-time data from the one or more sensors is provided as input to the machine learning algorithm and allows the classification of detected object defects to be adjusted in real-time [paragraph 26]. Real-time process characterization data is obtained using any of a variety of sensors, measurement tools, or inspection tools that monitor various process parameters in real-time during a manufacturing process [paragraph 136]. The real-time process characterization data is provided as input to the machine learning algorithm to adjust one or more process control parameters in real-time to compensate or correct for part defects as they arise during the build process [paragraphs 27, 137]. The machine learning algorithm used to run the automated process control may be configured to adjust the process control parameters in real-time as necessary to maximize a reward function (or to minimize a loss function) in order to optimize the deposition process [paragraph 139]. Once a current build state of a part has been determined, a reinforcement learning algorithm uses the current state information and the model developed using past training data to predict a proposed action that will maximize a reward function by determining a corresponding reward for each set of “next N states” [paragraph 140; figure 8]. This is effectively a simulation/evaluation of alternative corrective actions before selecting one. In one example, an artificial neural network (ANN) architecture is used for real-time, adaptive process control to predict a future build state based on current build state and a set of actions [paragraph 178; figure 10]. This would help improve the quality of the manufactured product. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use real-time process characterization data process parameters monitored in real-time during the manufacturing process as input to a machine learning algorithm to compensate or correct for defects as they arise during the manufacturing process, as taught by Mehr. This would help improve the quality of the manufactured product.
7-6. Regarding claim 14, Tetiker-Babayan-Mehr teach all the limitations of claim 13, further comprising: comparing the simulated wafer feature to a target reference wafer feature, by disclosing that when optimizing model parameters, calculating an error metric which is indicative of (related to, quantifies, etc.) the difference between the experimental and computed etch profiles over all the different sets of values for the input parameters [Tetiker, paragraph 59, lines 9-15].
7-7. Regarding claim 16, Tetiker-Babayan-Mehr teach all the limitations of claim 13, wherein the one or more corrective actions associated with the manufacturing process comprises adjusting RF power applied by at least one of the individually tunable RF field generators to a corresponding region of the processing chamber, by disclosing that parameters adjusted by the system controller relate to process conditions, including temperatures (e.g., substrate holder and showerhead temperatures), plasma conditions (such as RF bias power levels and exposure times) [Tetiker, paragraph 170].
7-8. Regarding claim 17, Tetiker-Babayan-Mehr teach all the limitations of claim 13, further comprising: receiving metrology information from at least one in situ, ex situ, or onboard metrology tool, prior to obtaining the gas profile information from the digital simulation, by disclosing one or more in-situ or offline metrology tools may be used to measure the experimental etch profiles which result from experimental etch process operations [Tetiker, paragraph 51]. The result of the etch experiments and metrology procedures is a set of measured etch profiles that are used as inputs to train, optimize, and improve the computerized etch profile models [Tetiker, paragraph 52].
Claim 19
7-9. Regarding claim 19, Tetiker teaches the claim comprising: causing an individual tunable heater to heat a plurality of regions of an electrostatic chuck to a specified temperature for a manufacturing process, wherein the individual tunable heater is associated with at least one region of the electrostatic chuck, while a wafer is being processed in a processing chamber, by disclosing an inductively coupled plasma reactor used in etch operations having a chuck 1017 that is configured to receive and hold a semiconductor wafer 1019 upon which etching process is performed and positioned within the lower level chamber near the bottom inner surface that can be electrically charged using an RF power supply [paragraph 146]. The chuck may operate at certain temperatures depending on the etching process operation and specific recipe [paragraph 153].
Tetiker teaches causing an individual tunable RF field generator to generate a plasma across a plurality of regions in a processing chamber conducting the manufacturing process, wherein the individual tunable RF field generator is associated with at least one region of the processing chamber, while the wafer is being processed in the processing chamber, by disclosing that the inductively coupled plasma reactor used in etch operations [paragraph 146] has an RF power supply configured to supply RF power to coil 1033 [paragraphs 147, 150].
Tetiker teaches obtaining, from a trained digital simulation associated with the electrostatic chuck, a heat profile information for each of the plurality of regions of the electrostatic chuck, wherein the trained digital simulation is executed concurrently with the manufacturing process, by disclosing a system controller that implements an optimized etch profile model (EPM) and adjusts operation of an etcher apparatus in response to computed etch profiles [paragraph 156]. The EPM computes a theoretically determined etch profile from a set of input etch reaction parameters (independent variables) characterizing the underlying physical and chemical etch processes and reaction mechanisms [paragraph 40]. The independent input variables – such as plasma parameters – are determined by using an etch chamber plasma model, wherein such models may calculate the applicable input EPM parameters from various process parameters over which the process engineer does have control (e.g., by turning a knob) – e.g., chamber environment parameters such as wafer temperature, ICP coil currents, bias voltages/power, and the like [paragraph 44]. Various experiments are performed to train and optimize the EPM [paragraph 48]. Examiner notes that the term “profile” has been interpreted as a set of data portraying the significant features of something. In this case, the heat profile information may be interpreted at least as the computed effect the temperature of the electrostatic chuck has on the etch profile of the EPM.
Tetiker teaches obtaining, from a trained digital simulation associated with the processing chamber, plasma profile information for each of the plurality of regions of the processing chamber, wherein the trained digital simulation is executed concurrently with the manufacturing process, by disclosing that the chamber environment parameters include plasma power [paragraph 44]. The plasma profile information may be interpreted at least as the computed effect the plasma power has on etch profile of the EPM.
Tetiker teaches simulating a wafer feature based at least in part on the heat profile information or the plasma profile information, the simulation predicting at least one feature of a processed wafer to be generated by the manufacturing process, by disclosing that the EPM is used to simulate the etch profile evolution of a substrate feature over time – i.e., the time-dependent changes in the shape of a feature at various spatial locations on the feature’s surface – by calculating reaction rates associated with the etch process at each of these spatial locations which result from an incident flux of etchant and deposition species characteristic of the plasma conditions set up in the reaction chamber, and do so over the course of the simulated etch process [paragraph 24]. The model may be used for monitoring and processing in situ optical signals, in real time, to generate geometric etch parameter from the in situ optical information (e.g., real time end point or critical dimension monitoring). [paragraph 107].
Tetiker does not expressly teach a plurality of tunable heaters to heat a plurality of regions of an electrostatic chuck to a specified temperature for the manufacturing process, wherein at least one of the plurality of individually tunable heaters is associated with at least one region of the electrostatic chuck. Babayan discloses fitting an electrostatic chuck with an array of heater elements to allow for the temperature of the ESC to be adjusted differently at different positions across the surface of the ESC [column 2, lines 53-56]. The array of heater elements provides independent temperature measurements at different positions on the electrostatic chuck, allowing the heaters to be operated to even out the temperature, or for the chuck to be modified to correct for the inconsistent temperatures [column 2, lines 46-52]. The thermal sensor data from the heater elements can be used to feed open loop models or a time based closed loop control PID (Proportional-Integral-Derivative) scheme [column 3, lines 10-12]. This would provide more accurate information on temperature variations across the chuck, thus allowing the user to more closely monitor the etching process. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use, in the inductively coupled plasma reactor that implements an optimized etch profile model (EPM) to adjust operation of an etcher apparatus in response to computed etch profiles of Tetiker, an array of heater elements on the electrostatic chuck, as taught by Babayan. This would provide more accurate information on temperature variations across the chuck, thus allowing the user to more closely monitor the etching process.
Tetiker does not expressly teach a plurality of individually tunable RF field generators to generate a plasma across a plurality of regions in a processing chamber conducting the manufacturing process, wherein at least one of the plurality of individually tunable RF field generators is associated with at least one region of the processing chamber. Babayan discloses a plasma system having a radio frequency (RF) source coupled to a showerhead assembly for powering the showerhead assembly to facilitate generation of plasma between a faceplate of the showerhead assembly and a heated pedestal [column 11, lines 1-5]. The RF source may include a HFRF power source and a low frequency radio frequency power source, and may be coupled to other portions of the processing chamber body, such as the pedestal, to facilitate plasma generation [column 11, lines 8-13]. This would allow the system to separately manage the ion flux and the ion energy, thus providing greater control of the etching process. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use, in the inductively coupled plasma reactor that implements an optimized etch profile model (EPM) to adjust operation of an etcher apparatus in response to computed etch profiles of Tetiker, a plurality of tunable RF field generators, as taught by Babayan. This would allow the system to separately manage the ion flux and the ion energy, thus providing greater control of the etching process.
Although Tetiker-Babyan disclose that the optimized EPM may be integrated with an etcher apparatus to determine appropriate adjustments to process parameters, such as to vary one or more values of the set of independent input parameters [Tetiker, paragraph 106], Tetiker-Babyan do not expressly teach causing, based on the prediction, performance of one or more corrective actions associated with the manufacturing process during the manufacturing process and prior to completion of processing of the wafer, wherein, prior to causing performance of the one or more corrective actions, the method further comprises simulating, using the trained digital simulation, a predicted effect of the one or more corrective actions on the wafer feature. Mehr discloses a method for classification of object defects during a fabrication manufacturing process that comprises: a) providing a training data set, wherein the training data set comprises fabrication process simulation data, fabrication process characterization data, in-process inspection data, post-build inspection data, or any combination thereof, for a plurality of object design geometries that are the same as or different from the object; b) providing one or more sensors, wherein the one or more sensors provide real-time data for one or more object properties; c) providing a processor programmed to provide a classification of detected object defects using a machine learning algorithm that has been trained using the training data set of step (a), wherein the real-time data from the one or more sensors is provided as input to the machine learning algorithm and allows the classification of detected object defects to be adjusted in real-time [paragraph 26]. Real-time process characterization data is obtained using any of a variety of sensors, measurement tools, or inspection tools that monitor various process parameters in real-time during a manufacturing process [paragraph 136]. The real-time process characterization data is provided as input to the machine learning algorithm to adjust one or more process control parameters in real-time to compensate or correct for part defects as they arise during the build process [paragraphs 27, 137]. The machine learning algorithm used to run the automated process control may be configured to adjust the process control parameters in real-time as necessary to maximize a reward function (or to minimize a loss function) in order to optimize the deposition process [paragraph 139]. Once a current build state of a part has been determined, a reinforcement learning algorithm uses the current state information and the model developed using past training data to predict a proposed action that will maximize a reward function by determining a corresponding reward for each set of “next N states” [paragraph 140; figure 8]. This is effectively a simulation/evaluation of alternative corrective actions before selecting one. In one example, an artificial neural network (ANN) architecture is used for real-time, adaptive process control to predict a future build state based on current build state and a set of actions [paragraph 178; figure 10]. This would help improve the quality of the manufactured product. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use real-time process characterization data process parameters monitored in real-time during the manufacturing process as input to a machine learning algorithm to compensate or correct for defects as they arise during the manufacturing process, as taught by Mehr. This would help improve the quality of the manufactured product.
8. Claims 9 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Tetiker (Pub. No. US 2019/0049937), in view of Babayan et al (U.S. Patent No. 12,406,865), in view of Mehr et al (Pub. No. US 2018/0341248), and further in view of Yamamoto et al (U.S. Patent No. 12,400,191).
8-1. Regarding claim 9, Tetiker-Babayan-Mehr teach all the limitations of claim 7. Tetiker-Babayan-Mehr do not expressly teach the claim further comprising: determining that the simulated wafer feature deviates from a target reference wafer feature by at least a predetermined threshold amount, prior to causing performance of the one or more corrective actions. Yamamoto discloses performing a process by a semiconductor manufacturing apparatus according to process parameters output from an apparatus control controller [column 10, lines 34-39] and acquiring physical sensor data measured by sensors from the semiconductor manufacturing apparatus that is performing the process [column 10, lines 39-43]. A simulation execution unit executes a simulation by a simulation model according to the same process parameters as those for the semiconductor manufacturing apparatus that is performing the process, and calculates a virtual sensor data and virtual process result data [column 10, lines 44-49]. When the process being performed by the semiconductor manufacturing apparatus is ended, a simulation result determination unit compares the physical sensor data and the virtual sensor data for the same position and time, to determine whether the physical sensor data and the virtual sensor data for the same position and time match each other [column 10, line 63 to column 11, line 3]. When it is determined that the physical sensor data and the virtual sensor data do not match each other, the simulation result determination unit 110 performs a process parameter adjusting process for optimizing the process parameters, so as to obtain customer's desired results after the process is performed [column 11, lines 4-9]. When the difference between the physical sensor data and the virtual sensor data for the same position and time exceeds a predetermined threshold value, optimization of the process parameters is stopped, and the difference is handled through the editing of the simulation model or the maintenance of the semiconductor manufacturing apparatus [column 11, lines 10-17]. This would save resources by undertaking certain corrective action when differences in data are significant enough. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to perform the corrective actions when the simulated wafer feature deviates from the target reference wafer by a predetermined amount, as taught by Yamamoto. This would save resources by undertaking certain corrective action when differences in data are significant enough.
8-2. Regarding claim 15, Tetiker-Babayan-Mehr teach all the limitations of claim 13. Tetiker-Babayan-Mehr do not expressly teach the claim further comprising: determining that the simulated wafer feature deviates from a target reference wafer feature by at least a predetermined threshold amount, prior to causing performance of the one or more corrective actions. Yamamoto discloses performing a process by a semiconductor manufacturing apparatus according to process parameters output from an apparatus control controller [column 10, lines 34-39] and acquiring physical sensor data measured by sensors from the semiconductor manufacturing apparatus that is performing the process [column 10, lines 39-43]. A simulation execution unit executes a simulation by a simulation model according to the same process parameters as those for the semiconductor manufacturing apparatus that is performing the process, and calculates a virtual sensor data and virtual process result data [column 10, lines 44-49]. When the process being performed by the semiconductor manufacturing apparatus is ended, a simulation result determination unit compares the physical sensor data and the virtual sensor data for the same position and time, to determine whether the physical sensor data and the virtual sensor data for the same position and time match each other [column 10, line 63 to column 11, line 3]. When it is determined that the physical sensor data and the virtual sensor data do not match each other, the simulation result determination unit 110 performs a process parameter adjusting process for optimizing the process parameters, so as to obtain customer's desired results after the process is performed [column 11, lines 4-9]. When the difference between the physical sensor data and the virtual sensor data for the same position and time exceeds a predetermined threshold value, optimization of the process parameters is stopped, and the difference is handled through the editing of the simulation model or the maintenance of the semiconductor manufacturing apparatus [column 11, lines 10-17]. This would save resources by undertaking certain corrective action when differences in data are significant enough. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to perform the corrective actions when the simulated wafer feature deviates from the target reference wafer by a predetermined amount, as taught by Yamamoto. This would save resources by undertaking certain corrective action when differences in data are significant enough.
9. Claims 12, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Tetiker (Pub. No. US 2019/0049937), in view of Babayan et al (U.S. Patent No. 12,406,865), in view of Mehr et al (Pub. No. US 2018/0341248), and further in view of Dehghanimohammadabadi (Pub. No. US 2023/0004149).
9-1. Regarding claim 12, Tetiker-Babayan-Mehr teach all the limitations of claim 7. Tetiker-Babayan-Mehr do not expressly teach the claim further comprising: obtaining from the digital simulation, an updated heat profile for each of the plurality of regions of the electrostatic chuck based on the predicted effect of the one or more corrective actions, prior to causing performance of the one or more corrective actions. Dehghanimohammadabadi discloses a machine learning system for optimizing a production process [paragraph 5]. A simulation system 120 at block 122 includes the creation of a digital twin model of a physical production process managed by a production planning system 110 [paragraph 117, lines 4-7; Figure 1A]. The simulation system 120, at block 124, receives as input, real-time data of the production process (block 114) as well as input parameters from the analytics system 130 (block 134) [paragraph 117, lines 7-13]. Next, the simulation system 120 at block 126 runs the digital twin model using the real-time data and input simulation parameters to produce output parameters [paragraph 118, lines 6-12]. These output parameters represent a predictive effect that the input parameters from the analytics system 130 (i.e. a corrective action) will have on the product being produced. The analytics system 130 at block 138 receives the output parameter of the simulation from the simulation system 120 [paragraph 18, lines 12-14] and at block 136 analyzes the output parameters using a machine learning algorithm to determine whether or not the production process has been sufficiently optimized [paragraph 19, lines 1-5]. If not, the analytics system 130 at block 134 iteratively learns by programming new input parameters so that the simulation runs again at blocks 124-126 [paragraph 19, lines 5-7]. Running the digital twin model again at block 126 using the real-time data of the production process and the new input parameters would cause an update in the simulation of all aspects of the production process affected by the new input parameters. If the analytics system 130 at block 140 determines that optimal values have been achieved, those optimal values may be sent to the production planning system for execution in the real physical process byte analytics system 130 at block 142 [paragraph 20]. This would allow the production process to be continually tuned based on real-time production data, so that the process can be modified and optimized with newly determined parameters [paragraph 21]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to receive updated profiles from running a simulation based on the predicted effects of input parameters, as taught by Dehghanimohammadabadi. This would allow for a more optimal result when corrections are made.
9-2. Regarding claim 18, Tetiker-Babayan-Mehr teach all the limitations of claim 13. Tetiker-Babayan-Mehr do not expressly teach the claim further comprising: obtaining from the digital simulation, an updated plasma profile for each of the plurality of regions of the processing chamber based on the predicted effect of the one or more corrective actions, prior to causing performance of the one or more corrective actions. Dehghanimohammadabadi discloses a machine learning system for optimizing a production process [paragraph 5]. A simulation system 120 at block 122 includes the creation of a digital twin model of a physical production process managed by a production planning system 110 [paragraph 117, lines 4-7; Figure 1A]. The simulation system 120, at block 124, receives as input, real-time data of the production process (block 114) as well as input parameters from the analytics system 130 (block 134) [paragraph 117, lines 7-13]. Next, the simulation system 120 at block 126 runs the digital twin model using the real-time data and input simulation parameters to produce output parameters [paragraph 118, lines 6-12]. These output parameters represent a predictive effect that the input parameters from the analytics system 130 (i.e. a corrective action) will have on the product being produced. The analytics system 130 at block 138 receives the output parameter of the simulation from the simulation system 120 [paragraph 18, lines 12-14] and at block 136 analyzes the output parameters using a machine learning algorithm to determine whether or not the production process has been sufficiently optimized [paragraph 19, lines 1-5]. If not, the analytics system 130 at block 134 iteratively learns by programming new input parameters so that the simulation runs again at blocks 124-126 [paragraph 19, lines 5-7]. Running the digital twin model again at block 126 using the real-time data of the production process and the new input parameters would cause an update in the simulation of all aspects of the production process affected by the new input parameters. If the analytics system 130 at block 140 determines that optimal values have been achieved, those optimal values may be sent to the production planning system for execution in the real physical process byte analytics system 130 at block 142 [paragraph 20]. This would allow the production process to be continually tuned based on real-time production data, so that the process can be modified and optimized with newly determined parameters [paragraph 21]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to receive updated profiles from running a simulation based on the predicted effects of input parameters, as taught by Dehghanimohammadabadi. This would allow for a more optimal result when corrections are made.
9-3. Regarding claim 20, Tetiker-Babayan-Mehr teach all the limitations of claim 19. Tetiker-Babayan-Mehr do not expressly teach the claim further comprising: obtaining from the digital simulation, at least one of updated heat profile information or at least one of updated plasma profile information for each of the plurality of regions based on the predicted effect of the one or more corrective actions, prior to causing performance of the one or more corrective actions. Dehghanimohammadabadi discloses a machine learning system for optimizing a production process [paragraph 5]. A simulation system 120 at block 122 includes the creation of a digital twin model of a physical production process managed by a production planning system 110 [paragraph 117, lines 4-7; Figure 1A]. The simulation system 120, at block 124, receives as input, real-time data of the production process (block 114) as well as input parameters from the analytics system 130 (block 134) [paragraph 117, lines 7-13]. Next, the simulation system 120 at block 126 runs the digital twin model using the real-time data and input simulation parameters to produce output parameters [paragraph 118, lines 6-12]. These output parameters represent a predictive effect that the input parameters from the analytics system 130 (i.e. a corrective action) will have on the product being produced. The analytics system 130 at block 138 receives the output parameter of the simulation from the simulation system 120 [paragraph 18, lines 12-14] and at block 136 analyzes the output parameters using a machine learning algorithm to determine whether or not the production process has been sufficiently optimized [paragraph 19, lines 1-5]. If not, the analytics system 130 at block 134 iteratively learns by programming new input parameters so that the simulation runs again at blocks 124-126 [paragraph 19, lines 5-7]. Running the digital twin model again at block 126 using the real-time data of the production process and the new input parameters would cause an update in the simulation of all aspects of the production process affected by the new input parameters. If the analytics system 130 at block 140 determines that optimal values have been achieved, those optimal values may be sent to the production planning system for execution in the real physical process byte analytics system 130 at block 142 [paragraph 20]. This would allow the production process to be continually tuned based on real-time production data, so that the process can be modified and optimized with newly determined parameters [paragraph 21]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to receive updated profiles from running a simulation based on the predicted effects of input parameters, as taught by Dehghanimohammadabadi. This would allow for a more optimal result when corrections are made.
Response to Arguments
10. The Examiner acknowledges the Applicant’s amendments to claims 1, 6-7, 12-13, and 18-20.
Regarding independent claim 1, Applicant alleges that Tetiker (Pub. No. US 2019/0049937) in view of Mehr et al (Pub. No. US 2018/0341248) do not disclose performing a simulation of proposed corrective actions to determine their predicted effect prior to applying those actions because Mehr applies adjustments directly based on model outputs, without any intermediate step of simulating the corrective actions themselves and evaluating their predicted impact on wafer features before implementation.
Contrary to Applicant’s arguments, Mehr discloses that real-time process characterization data is provided as input to a machine learning algorithm to adjust one or more process control parameters in real-time to compensate or correct for part defects as they arise during the build process [paragraphs 27, 137]. The machine learning algorithm used to run the automated process control may be configured to adjust the process control parameters in real-time as necessary to maximize a reward function (or to minimize a loss function) in order to optimize the deposition process [paragraph 139]. Once a current build state of a part has been determined, a reinforcement learning algorithm uses the current state information and the model developed using past training data to predict a proposed action that will maximize a reward function by determining a corresponding reward for each set of “next N states” [paragraph 140; figure 8]. This is effectively a simulation/evaluation of alternative corrective actions before selecting one. In one example, an artificial neural network (ANN) architecture is used for real-time, adaptive process control to predict a future build state based on current build state and a set of actions [paragraph 178; figure 10]. This would help improve the quality of the manufactured product. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use, in the wafer manufacturing process of Tetiker, real-time process characterization data process parameters monitored in real-time during the manufacturing process as input to a machine learning algorithm to compensate or correct for defects as they arise during the manufacturing process, as taught by Mehr. This would help improve the quality of the manufactured product. Thus, the combination of Tetiker in view of Mehr teach wherein, prior to causing performance of the one or more corrective actions, the method further comprises simulating, using the trained digital simulation, a predicted effect of the one or more corrective actions on the wafer feature.
Similar arguments have been presented for independent claims 7, 13, and 19. Examiner has rejected claims 7, 13, and 19 under 35 U.S.C. 103 as being unpatentable over Tetiker (Pub. No. US 2019/0049937), in view of Babayan et al (U.S. Patent No. 12,406,865), and further in view of Mehr et al (Pub. No. US 2018/0341248). Applicant’s arguments have been considered but are moot in view of the new grounds of rejection.
Examiner also notes that dependent claim 6 has been rejected under 35 U.S.C. 103 as being unpatentable over Tetiker, in view of Mehr, and further in view of Dehghanimohammadabadi (Pub. No. US 2023/0004149). Dependent claims 12, 18, and 20 have been rejected under 35 U.S.C. 103 as being unpatentable over Tetiker, in view of Babayan, in view of Mehr, and further in view of Dehghanimohammadabadi
Applicant states that dependent claims 2-6, 8-12, 14-18, and 20 recite all the limitations of the independent claims, and thus, are allowable in view of the remarks set forth regarding independent claims 1, 7, 13, and 19. However, as discussed above, Tetiker in view of Mehr are considered to teach claim 1, Tetiker in view of Babayan, and further in view of Mehr are considered to teach claims 7, 13, and 19, and consequently, claims 2-6, 8-12, 14-18, and 20 are rejected.
Conclusion
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/ALVIN H TAN/Primary Examiner, Art Unit 2118
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