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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4, 6-8, 10, 11, and 13-16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Suzuki (US-2020/0101579).
Regarding claim 1 (Currently Amended), Suzuki (US-2020/0101579) discloses an information processing apparatus comprising:
an information acquisition section (state acquisition unit 846) configured to acquire polishing conditions including top-ring state information indicating a state of a top ring (motor current of top ring 31A, rotational frequency of top ring 31A) (“A scheme will be described in the present embodiment in which the top ring is held at an end portion of the swing arm and a polishing end point is detected based on the arm torque, and it is likewise possible to detect a polishing end point by detecting a drive load of the drive section that drives to rotate the rotational table or the top ring, by the motor current.”) [Suzuki; paragraph 0097] (“The variation of the polishing frictional force is reflected as the variation of the motor current value of the above-described drive section, and therefore the film thickness of the target can be detected by a motor current sensor.”) [Suzuki; paragraph 0006], polishing-table state information indicating a state of a polishing table (motor current of turntable 30A) (“A scheme will be described in the present embodiment in which the top ring is held at an end portion of the swing arm and a polishing end point is detected based on the arm torque, and it is likewise possible to detect a polishing end point by detecting a drive load of the drive section that drives to rotate the rotational table or the top ring, by the motor current.”) [Suzuki; paragraph 0097] (“The dataset can include a pressure of the top ring 31A on the semiconductor wafer 16, a current of the swing shaft motor 14, a motor current of the rotational table 30A”) [Suzuki; paragraph 0212], and polishing-fluid-supply-nozzle state information (“a flow rate of slurry supplied from the polishing liquid supply nozzle 32A”) [Suzuki; paragraph 0126] indicating a state of a polishing-fluid supply nozzle in chemical mechanical polishing of a substrate performed by a substrate processing apparatus including the polishing table configured to rotatably support a polishing pad (polishing pad 10), the top ring (top ring 31A) configured to press the substrate against the polishing pad, and the polishing-fluid supply nozzle configured to supply a polishing fluid onto the polishing pad (“During polishing, a polishing liquid is supplied to the polishing surface of the polishing pad 10 from the polishing liquid supply nozzle 32A, and the semiconductor wafer 16, which is a target, is pressed against the polishing surface by the top ring 31A, thus being polished.”) [Suzuki; paragraph 0053]; and
a state prediction section (learning unit 848) configured to predict substrate state information (film thickness) for the substrate on which the chemical mechanical polishing is performed under the polishing conditions by inputting the polishing conditions acquired by the information acquisition section to a learning model having been generated by machine learning that causes the learning model to learn a correlation between the polishing conditions and the substrate state information indicating a state of the substrate on which the chemical mechanical polishing is performed under the polishing conditions (“The learning section 848 learns the change in the film thickness of the semiconductor wafer 16 based on the data set created based on a combination of the state variable and the determination data.”) [Suzuki; paragraph 0130] (“The learning section 848 can predict the change in the film thickness of the semiconductor wafer 16 after learning the change. Furthermore, the learning section 848 can learn the change in the film thickness of the semiconductor wafer 16 to determine whether the change is normal or abnormal.”) [Suzuki; paragraph 0131].
Regarding claim 2 (Currently Amended), Suzuki discloses the information processing apparatus according to claim 1, wherein the top ring includes:
a top ring body which is moved by a rotating mechanism,
a vertical movement mechanism, and an oscillation mechanism;
a membrane housed in the top ring body and configured to press the substrate against the polishing pad according to pressurized fluid supplied to a membrane pressure chamber; and
a retainer ring disposed at a periphery of the membrane and configured to press the polishing pad according to pressurized fluid supplied to a retainer-ring pressure chamber, and
the top-ring state information included in the polishing conditions includes at least one of: a rotation speed of the top ring;
a rotation torque of the top ring (“That is, the state acquisition section 846 receives data such as a torque command value (and/or speed command value) for driving one or a plurality of motors”) [Suzuki; paragraph 0158];
an oscillation position of the top ring;
an oscillation torque of the top ring;
a height of the top ring;
an elevating torque of the top ring;
a pressure in the membrane pressure chamber;
a flow rate of the pressurized fluid supplied to the membrane pressure chamber; a condition of the membrane;
a pressure in the retainer-ring pressure chamber;
a flow rate of the pressurized fluid supplied to the retainer-ring pressure chamber; and
a condition of the retainer ring [Suzuki; paragraph 0126].
Regarding claim 3 (Currently Amended), Suzuki discloses the information processing apparatus according to claim 1,
wherein the polishing-table state information included in the polishing conditions includes at least one of:
a rotation speed of the polishing table;
a rotation torque of the polishing table (“The first polishing unit 3A may include a table torque detection section (not illustrated) that detects table torque applied to the rotational table 30A. The table torque detection section can detect table torque from the current of the table drive section, which is a rotation motor.”) [Suzuki; paragraph 0105];
a surface temperature of the polishing pad (“a temperature of the polishing pad 10”) [Suzuki; paragraph 0128]; and
a condition of the polishing pad (temperature) (“a temperature of the polishing pad 10”) [Suzuki; paragraph 0128].
Regarding claim 4 (Currently Amended), Suzuki discloses the information processing apparatus according to claim 1,
wherein the polishing-fluid-supply-nozzle state information included in the polishing conditions includes at least one of:
a flow rate of the polishing fluid (“a flow rate of slurry supplied from the polishing liquid supply nozzle 32A”) [Suzuki; paragraph 0126];
a dropping position of the polishing fluid; and
a temperature of the polishing fluid.
Regarding claim 6 (Currently Amended), Suzuki discloses the information processing apparatus according to claim 1, wherein the polishing conditions further include:
unprocessed substrate information indicating a state of an unprocessed substrate which is the substrate before the chemical mechanical polishing is performed (“A film thickness calculating section 830 (see FIG. 10) of the end point detection section 28 can determine the film thickness from a correspondence relationship between the current command 18a and the current value and the film thickness. For example, the correspondence relationship between the current command 18a and the current value and the film thickness can be obtained before starting the polishing step, and be stored in the film thickness calculating section 830.”) [emphasis added] [Suzuki; paragraph 0110].
Regarding claim 7 (Currently Amended), Suzuki discloses the information processing apparatus according to claim 6, wherein the unprocessed substrate information included in the polishing conditions includes at least one of:
a shape of the unprocessed substrate;
a weight of the unprocessed substrate; and
a condition of a substrate surface of the unprocessed substrate (“A film thickness calculating section 830 (see FIG. 10) of the end point detection section 28 can determine the film thickness from a correspondence relationship between the current command 18a and the current value and the film thickness. For example, the correspondence relationship between the current command 18a and the current value and the film thickness can be obtained before starting the polishing step, and be stored in the film thickness calculating section 830.”) [emphasis added] [Suzuki; paragraph 0110].
Regarding claim 8 (Currently Amended), Suzuki discloses the information processing apparatus according toclaim 1,
wherein the substrate state information comprises stress information indicating stress applied to the substrate (“data detected by the control section 65 (pressure detection section) capable of detecting the pressure applied to the semiconductor wafer 16, and data on the characteristics of the target. As for pressures applied to the semiconductor wafer 16, the control section 65 can detect the pressures using the pressure sensors provided in the respective fluid paths 651, 652, 653, and 654.”) [Suzuki; paragraph 0113], and
the stress information indicates at least one of mechanical stress and thermal stress applied to the substrate (“data detected by the control section 65 (pressure detection section) capable of detecting the pressure applied to the semiconductor wafer 16, and data on the characteristics of the target. As for pressures applied to the semiconductor wafer 16, the control section 65 can detect the pressures using the pressure sensors provided in the respective fluid paths 651, 652, 653, and 654.”) [Suzuki; paragraph 0113].
Regarding claim 10 (Currently Amended), Suzuki discloses the information processing apparatus according to claim 8, wherein the stress information indicates an in-plane distribution state of the stress applied to a substrate surface of the substrate (calculates the air bag pressures P1, P2, P3, P4) (Fig. 4) (“air bag pressure”0 [Suzuki; paragraph 0132] (“data detected by the control section 65 (pressure detection section) capable of detecting the pressure applied to the semiconductor wafer 16, and data on the characteristics of the target. As for pressures applied to the semiconductor wafer 16, the control section 65 can detect the pressures using the pressure sensors provided in the respective fluid paths 651, 652, 653, and 654.”) [Suzuki; paragraph 0113].
Regarding claim 11 (Currently Amended), Suzuki discloses the information processing apparatus according to claim 1, wherein the substrate state information comprises polishing quality information indicating a polishing quality of the substrate (film thickness is considered a measurement of quality regarding desired thickness and desired uniformity) (“The learning section 848 learns the change in the film thickness of the semiconductor wafer 16 based on the data set created based on a combination of the state variable and the determination data.”) [Suzuki; paragraph 0130] (“The learning section 848 can predict the change in the film thickness of the semiconductor wafer 16 after learning the change. Furthermore, the learning section 848 can learn the change in the film thickness of the semiconductor wafer 16 to determine whether the change is normal or abnormal.”) [Suzuki; paragraph 0131].
Regarding claim 13 (Currently Amended), Suzuki discloses an inference apparatus comprising:
a memory (“Next, the control of the entire substrate processing apparatus by the control section 65 will be described with reference to FIG. 17. The control section 65 which is a min controller includes a CPU, a memory, a recording medium and software recorded in the recording medium or the like.”) [Suzuki; paragraph 0178]; and
a processor (“Next, the control of the entire substrate processing apparatus by the control section 65 will be described with reference to FIG. 17. The control section 65 which is a min controller includes a CPU, a memory, a recording medium and software recorded in the recording medium or the like.”) [Suzuki; paragraph 0178] configured to perform:
an information acquisition process of acquiring polishing conditions including top-ring state information indicating a state of a top ring (motor current of top ring 31A, rotational frequency of top ring 31A) (“A scheme will be described in the present embodiment in which the top ring is held at an end portion of the swing arm and a polishing end point is detected based on the arm torque, and it is likewise possible to detect a polishing end point by detecting a drive load of the drive section that drives to rotate the rotational table or the top ring, by the motor current.”) [Suzuki; paragraph 0097] (“The variation of the polishing frictional force is reflected as the variation of the motor current value of the above-described drive section, and therefore the film thickness of the target can be detected by a motor current sensor.”) [Suzuki; paragraph 0006], polishing-table state information indicating a state of a polishing table (motor current of turntable 30A) (“A scheme will be described in the present embodiment in which the top ring is held at an end portion of the swing arm and a polishing end point is detected based on the arm torque, and it is likewise possible to detect a polishing end point by detecting a drive load of the drive section that drives to rotate the rotational table or the top ring, by the motor current.”) [Suzuki; paragraph 0097] (“The dataset can include a pressure of the top ring 31A on the semiconductor wafer 16, a current of the swing shaft motor 14, a motor current of the rotational table 30A”) [Suzuki; paragraph 0212], and polishing-fluid-supply-nozzle state information (“a flow rate of slurry supplied from the polishing liquid supply nozzle 32A”) [Suzuki; paragraph 0126] indicating a state of a polishing-fluid supply nozzle in chemical mechanical polishing of a substrate performed by a substrate processing apparatus including the polishing table configured to rotatably support a polishing pad (polishing pad 10), the top ring (top ring 31A) configured to press the substrate against the polishing pad, and the polishing-fluid supply nozzle configured to supply a polishing fluid onto the polishing pad (“During polishing, a polishing liquid is supplied to the polishing surface of the polishing pad 10 from the polishing liquid supply nozzle 32A, and the semiconductor wafer 16, which is a target, is pressed against the polishing surface by the top ring 31A, thus being polished.”) [Suzuki; paragraph 0053]; and
an inference process of inferring substrate state information indicating a state of the substrate on which the chemical mechanical polishing is performed under the polishing conditions when the polishing conditions are acquired in the information acquisition process (“The learning section 848 learns the change in the film thickness of the semiconductor wafer 16 based on the data set created based on a combination of the state variable and the determination data.”) [Suzuki; paragraph 0130] (“The learning section 848 can predict the change in the film thickness of the semiconductor wafer 16 after learning the change. Furthermore, the learning section 848 can learn the change in the film thickness of the semiconductor wafer 16 to determine whether the change is normal or abnormal.”) [Suzuki; paragraph 0131].
Regarding claim 14 (Currently Amended), Suzuki discloses an inference apparatus comprising:
a memory (“Next, the control of the entire substrate processing apparatus by the control section 65 will be described with reference to FIG. 17. The control section 65 which is a min controller includes a CPU, a memory, a recording medium and software recorded in the recording medium or the like.”) [Suzuki; paragraph 0178]; and
a processor (“Next, the control of the entire substrate processing apparatus by the control section 65 will be described with reference to FIG. 17. The control section 65 which is a min controller includes a CPU, a memory, a recording medium and software recorded in the recording medium or the like.”) [Suzuki; paragraph 0178] configured to perform:
an information acquisition process of acquiring stress information indicating stress applied to a substrate (“data detected by the control section 65 (pressure detection section) capable of detecting the pressure applied to the semiconductor wafer 16, and data on the characteristics of the target. As for pressures applied to the semiconductor wafer 16, the control section 65 can detect the pressures using the pressure sensors provided in the respective fluid paths 651, 652, 653, and 654.”) [Suzuki; paragraph 0113], on which chemical mechanical polishing is performed by a substrate processing apparatus[, the processing apparatus] including a polishing table (rotational table 30A) configured to rotatably support a polishing pad (polishing pad 10), a top ring (top ring 31A) configured to press the substrate against the polishing pad (“The top ring (holding section) 31A holds a semiconductor wafer and polishes the semiconductor wafer while pressing the semiconductor wafer against the polishing pad 10 on the rotational table 30A.”) [Suzuki; paragraph 0050], and a polishing-fluid supply nozzle (polishing liquid supply nozzle 32A) configured to supply a polishing fluid onto the polishing pad (“The polishing liquid supply nozzle 32A supplies a polishing liquid or a dressing liquid (for example, deionized water) to the polishing pad 10.”) [Suzuki; paragraph 0050]; and
an inference process of inferring polishing quality information indicating a polishing quality of the substrate to which the stress indicated by the stress information is applied when the stress information is acquired in the information acquisition process (film thickness is considered a measurement of quality regarding desired thickness and desired uniformity) (“The learning section 848 learns the change in the film thickness of the semiconductor wafer 16 based on the data set created based on a combination of the state variable and the determination data.”) [Suzuki; paragraph 0130] (“The learning section 848 can predict the change in the film thickness of the semiconductor wafer 16 after learning the change. Furthermore, the learning section 848 can learn the change in the film thickness of the semiconductor wafer 16 to determine whether the change is normal or abnormal.”) [Suzuki; paragraph 0131] (“The pressure detection section includes the pressure sensors, and the control section 65 that outputs the pressures applied to the semiconductor wafer 16, as pressure commands, to the pressure adjusting section 675. When the control section 65 is a pressure detection section, the data detected by the control section 65 may be a pressure command. For learning, the pressure command is output, as a signal 65b, from the control section 65 to the state acquisition section 846 in addition to the pressure adjusting section 675.”) [Suzuki; paragraph 0118].
Regarding claim 15 (Currently Amended), Suzuki discloses a machine-learning apparatus comprising:
a learning-data storage section storing multiple sets of learning data including polishing conditions [Suzuki; paragraph 0126] and substrate state information (“A signal of the sensor 676 is transmitted to the control section 65, and the control section 65 generates a monitoring signal representing a film thickness. A value of the monitoring signal (and a sensor signal) does not represent the film thickness itself. However, a value of the monitoring signal changes depending on the film thickness. Therefore, the monitoring signal can be a signal representing the film thickness of the semiconductor wafer 16.”) [Suzuki; paragraph 0065] (“Therefore, after multiple sets of data during the actual polishing are stored in a memory in the learning section 848,”) [Suzuki; paragraph 0152], the polishing conditions including top-ring state information indicating a state of a top ring (“a motor current of the rotational table 30A, the top ring 31A, and the swing shaft motor 14”) [Suzuki; paragraph 0126], polishing-table state information indicating a state of a polishing table (“a motor current of the rotational table 30A, the top ring 31A, and the swing shaft motor 14;”) [Suzuki; paragraph 0126], and polishing-fluid-supply-nozzle state information indicating a state of a polishing-fluid supply nozzle (“a flow rate of slurry supplied from the polishing liquid supply nozzle 32A”) [Suzuki; paragraph 0126] in chemical mechanical polishing of a substrate performed by a substrate processing apparatus[, the substrate processing apparatus] including the polishing table (30A) configured to rotatably support a polishing pad (10) (Fig. 2), the top ring (31A) configured to press the substrate against the polishing pad (10) (Fig. 2), and the polishing-fluid supply nozzle (32A) configured to supply a polishing fluid onto the polishing pad (Fig. 2), the substrate state information indicating a state of the substrate on which the chemical mechanical polishing is performed under the polishing conditions (“The learning section 848 learns the change in the film thickness of the semiconductor wafer 16 based on the data set created based on a combination of the state variable and the determination data.”) [Suzuki; paragraph 0130];
a machine-learning section configured to cause a learning model to learn a correlation between the polishing conditions and the substrate state information by inputting the multiple sets of learning data to the learning model (“For example, the accuracy of the end point detection can be improved by learning the film thickness detection section and the other detection sections (temperature detection section, pressure detection section for detecting an air bag pressure, or the like) and the use time of the consumable part. Furthermore, it is possible to provide a machine learning apparatus capable of reducing the influence of time delay for the film thickness recognized by the processing system and the communication system, and the polishing apparatus.”) [Suzuki; paragraph 0132]; and
a learned-model storage section configured to store the learning model that has learned the correlation by the machine-learning section (“After the program 856 performs the learning, the learned model 858 is used in the actual polishing step.”) [Suzuki; paragraph 0143] (“The learned model 858 is created by learning described below. When the learned model 858 performs the following learning after the learned model 858 is created, the learned model 858 can be automatically updated. The learned model 858 can perform learning for automatic updating while predicting the change in film thickness in the polishing step.”) [Suzuki; paragraph 0147].
Regarding claim 16 (Currently Amended), Suzuki discloses a machine-learning apparatus comprising:
a learning-data storage section storing multiple sets of learning data including polishing conditions [Suzuki; paragraph 0126] and substrate state information (“A signal of the sensor 676 is transmitted to the control section 65, and the control section 65 generates a monitoring signal representing a film thickness. A value of the monitoring signal (and a sensor signal) does not represent the film thickness itself. However, a value of the monitoring signal changes depending on the film thickness. Therefore, the monitoring signal can be a signal representing the film thickness of the semiconductor wafer 16.”) [Suzuki; paragraph 0065] (“Therefore, after multiple sets of data during the actual polishing are stored in a memory in the learning section 848,”) [Suzuki; paragraph 0152],
the stress information indicating stress applied to a substrate on which chemical mechanical polishing is performed by a substrate processing apparatus (“data detected by the control section 65 (pressure detection section) capable of detecting the pressure applied to the semiconductor wafer 16, and data on the characteristics of the target. As for pressures applied to the semiconductor wafer 16, the control section 65 can detect the pressures using the pressure sensors provided in the respective fluid paths 651, 652, 653, and 654.”) [Suzuki; paragraph 0113][, the substrate processing apparatus] including a polishing table (rotational table 30A) configured to rotatably support a polishing pad (pad 10) (Fig. 2), a top ring (31A) configured to press the substrate against the polishing pad (10) (Fig. 2), and a polishing-fluid supply nozzle (32A) configured to supply a polishing fluid onto the polishing pad (10) (Fig. 2), the polishing quality information indicating a polishing quality of the substrate to which the stress indicated by the stress information is applied (film thickness is considered a measurement of quality regarding desired thickness and desired uniformity) (“The learning section 848 learns the change in the film thickness of the semiconductor wafer 16 based on the data set created based on a combination of the state variable and the determination data.”) [Suzuki; paragraph 0130] (“The learning section 848 can predict the change in the film thickness of the semiconductor wafer 16 after learning the change. Furthermore, the learning section 848 can learn the change in the film thickness of the semiconductor wafer 16 to determine whether the change is normal or abnormal.”) [Suzuki; paragraph 0131] (“The pressure detection section includes the pressure sensors, and the control section 65 that outputs the pressures applied to the semiconductor wafer 16, as pressure commands, to the pressure adjusting section 675. When the control section 65 is a pressure detection section, the data detected by the control section 65 may be a pressure command. For learning, the pressure command is output, as a signal 65b, from the control section 65 to the state acquisition section 846 in addition to the pressure adjusting section 675.”) [Suzuki; paragraph 0118];
a machine-learning section configured to cause a learning model to learn a correlation between the polishing conditions and the substrate state information by inputting the multiple sets of learning data to the learning model (“For example, the accuracy of the end point detection can be improved by learning the film thickness detection section and the other detection sections (temperature detection section, pressure detection section for detecting an air bag pressure, or the like) and the use time of the consumable part. Furthermore, it is possible to provide a machine learning apparatus capable of reducing the influence of time delay for the film thickness recognized by the processing system and the communication system, and the polishing apparatus.”) [Suzuki; paragraph 0132]; and
a learned-model storage section configured to store the learning model that has learned the correlation by the machine-learning section (“After the program 856 performs the learning, the learned model 858 is used in the actual polishing step.”) [Suzuki; paragraph 0143] (“The learned model 858 is created by learning described below. When the learned model 858 performs the following learning after the learned model 858 is created, the learned model 858 can be automatically updated. The learned model 858 can perform learning for automatic updating while predicting the change in film thickness in the polishing step.”) [Suzuki; paragraph 0147].
Allowable Subject Matter
Claim 5 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
The prior art fails to anticipate or render obvious, in combination with all other claim limitations, “wherein the polishing conditions further includes: device internal-environment information indicating environment of a space in which the chemical mechanical polishing is performed, and the device internal-environment information included in the polishing conditions includes at least one of: temperature of the space; humidity of the space; and atmospheric pressure of the space.”
Claim 9 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
The prior art fails to anticipate or render obvious, in combination with all other claim limitations, “an instantaneous value of the stress at a target point-in-time included in a polishing- process period from start to end of the chemical mechanical polishing; or an accumulated value of the stress in a target period from the start of the chemical mechanical polishing to the target point-in-time.”
Claim 12 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
The prior art fails to anticipate or render obvious, in combination with all other claim limitations, the combination of “a learning model for stress analysis having been generated by machine learning that causes the learning model for stress analysis to learn a correlation between the polishing conditions and stress information indicating stress applied to the substrate on which the chemical mechanical polishing is performed under the polishing conditions” and “a learning model for polishing-quality analysis having been generated by machine learning that causes the learning model for polishing-quality analysis to learn a correlation between the stress information and polishing quality information indicating a polishing quality of the substrate to which the stress indicated by the stress information is applied.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. CN110039440A and US-2022/0193858A1 is pertinent to claim 1.
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/JOEL D CRANDALL/Examiner, Art Unit 3723
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