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 Objection
Claim 11 is objected to under 37 CFR 1.75(c) as being in improper form because a multiple dependent claim should refer to other claims in the alternative only. See MPEP § 608.01(n). Accordingly, the claim 11 has not been further treated on the merits.
Claim 11 is objected to as being dependent upon Claims 1 and 5 concurrently. It does not refer back in the Alternative Only.
Double Patenting Rejection
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-17 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-14 of U.S. Patent No. 12,176,190 (hereinafter ‘190) in view of Fink et al. (2024/0355598).
Regarding Claim 1, the US Patent 12,176,190 discloses a computer-implemented method of training a machine learning model for detecting and managing arc events during a plasma chamber process (Claim 1 of ‘190), comprising:
collecting impedance values measured at an output of an RF generator or an input of a process chamber during respective ignition phases of a plurality of plasma chamber processes (“receiving impedance measurement data captured during at least one of an ignition phase, a normal operating process phase or an arc event of the plasma chamber process”, Claim 1 of ‘190);
measured impedance values of the ignition phases (“analyzing the impedance measurement data, using a machine learning process, to determine a status of the plasma chamber process, including at least one of the occurrence of an ignition process”, Claim 1);
training the machine learning model (“using a machine learning process”) (Claim 1);
collecting impedance values measured at an output of an RF generator or an input of a process chamber during respective process phases of a plurality of plasma chamber processes (“analyzing the additional impedance measurement data to determine if the additional impedance measurement data includes impedance measurement data that is equal to or within a tolerance of impedance measurement data captured during the normal operating process phase of the plasma chamber process”, Claim 1);
measured impedance values of the process phases (“analyzing the additional impedance measurement data to determine if the additional impedance measurement data includes impedance measurement data that is equal to or within a tolerance of impedance measurement data captured during the normal operating process phase of the plasma chamber process”, Claim 1);
collecting impedance values measured at an output of an RF generator or an input of a process chamber for respective arc events occurring during a plurality of plasma chamber processes (“and if the additional impedance measurement data includes impedance measurement data that is equal to or within a tolerance of impedance measurement data captured during the normal operating process phase of the plasma chamber process, determining that the arc was suppressed”, Claim 1);
measured impedance values of the arc events (“and if the additional impedance measurement data includes impedance measurement data that is equal to or within a tolerance of impedance measurement data captured..”, Claim 1); and
As discussed above, US Patent ‘190 essentially discloses the claimed invention but does not disclose training the machine learning model in the first, second and third stages using the first, second and third training sets from collected data.
However, Fink et al. (2024/0355598) discloses a machine learned detection of the at least one irregularity is used for arc detection and/or arc prevention and/or arc management. The detection may result in an output of a warning message. Further actions like a shut-off allow for an arc management. If the expected occurrence of the arc can be detected, this allows for an arc prevention (paragraph [0053]).
Fink also teaches further training with new sets of training data, i. e. a machine-learning of new situations, can advantageously be performed at a later point of time, and an already existing parametrization can be optimized further (in the following, this is particularly referred to as a “switching the processing from the real-time mode to the training mode” (paragraph [0024]).
It would have been obvious to one of ordinary skill in the art to have collected sets of training data in different stages for arc detection in the US Patent ‘190 in order to optimize the arc prevention and management as taught by Fink.
Regarding Claims 3 and 11, US Patent’s 190 does not disclose training the machine learning model in the fourth, fifth and sixth stages using the fourth, fifth, sixth training sets from collected data. As discussed above, Fink et al. (2024/0355598) discloses a machine learned detection of the at least one irregularity is used for arc detection and/or arc prevention and/or arc management. The detection may result in an output of a warning message. Further actions like a shut-off allow for an arc management. If the expected occurrence of the arc can be detected, this allows for an arc prevention (paragraph [0053]).
Fink also teaches further training with new sets of training data, i. e. a machine-learning of new situations, can advantageously be performed at a later point of time, and an already existing parametrization can be optimized further (in the following, this is particularly referred to as a “switching the processing from the real-time mode to the training mode” (paragraph [0024]).
It would have been obvious to one of ordinary skill in the art to have collected sets of training data in different stages for arc detection in the US Patent ‘190 in order to optimize the arc prevention and management as taught by Fink.
Regarding Claims 2, 4-10, 12-17, the claims of US Patent ‘190 overlap or teach the limitation as claimed. Please see table below.
Current application (18/949,472)
US Patent 12,176,190
1. A computer-implemented method of training a machine learning model for detecting and managing arc events during a plasma chamber process, comprising:
collecting impedance values measured at an output of an RF generator or an input of a process chamber during respective ignition phases of a plurality of plasma chamber processes;
creating a first training set from the collected, measured impedance values of the ignition phases;
training the machine learning model in a first stage using the first training set;
collecting impedance values measured at an output of an RF generator or an input of a process chamber during respective process phases of a plurality of plasma chamber processes;
creating a second training set from the collected, measured impedance values of the process phases;
training the machine learning model in a second stage using the second training set;
collecting impedance values measured at an output of an RF generator or an input of a process chamber for respective arc events occurring during a plurality of plasma chamber processes;
creating a third training set from the collected, measured impedance values of the arc events; and
training the machine learning model in a third stage using the third training set.
1. A method for detecting and managing arc events during a plasma chamber process, comprising:
receiving impedance measurement data captured during at least one of an ignition phase, a normal operating process phase or an arc event of the plasma chamber process;
analyzing the impedance measurement data, using a machine learning process, to determine a status of the plasma chamber process, including at least one of the occurrence of an ignition process, or a normal operation, or the arc event during of the plasma chamber process;
if it is determined that an arc event is occurring, taking an action to suppress an arc of the arc event;
after taking the action to suppress the arc, receiving additional impedance measurement data;
analyzing the additional impedance measurement data to determine if the additional impedance measurement data includes impedance measurement data that is equal to or within a tolerance of impedance measurement data captured during the normal operating process phase of the plasma chamber process;
and if the additional impedance measurement data includes impedance measurement data that is equal to or within a tolerance of impedance measurement data captured during the normal operating process phase of the plasma chamber process, determining that the arc was suppressed.
2. The method of claim 1, wherein the machine learning model is trained to recognize a status of the plasma chamber process, including at least one of the occurrence of an arc event, the occurrence of an ignition process, a normal operation of the plasma chamber process or destroyed plasma.
2. The method of claim 1, further comprising: receiving at least one of inductance, capacitance, voltage or current measurement data; and further analyzing the received at least one of the inductance, capacitance, voltage or current measurement data to determine the status of the plasma chamber process.
9. “…determining from the measured impedance values, using a machine learning (ML) model, a status of the plasma chamber process, including at least one of the occurrence of an arc event, the occurrence of an ignition process, or a normal operation of the plasma chamber process…”
3. The method of claim 1, further comprising: collecting at least one of inductance, capacitance, voltage or current measurement data at an output of an RF generator or an input of a process chamber during respective ignition phases of a plurality of plasma chamber processes; creating a fourth training set from the collected at least one of the inductance, the capacitance, the voltage or the current measurement data; training the machine learning model in a fourth stage using the fourth training set; collecting at least one of inductance, capacitance, voltage or current measurement data at an output of an RF generator or an input of a process chamber during respective process phases of a plurality of plasma chamber processes; creating a fifth training set from the collected at least one of the inductance, the capacitance, the voltage or the current measurement data of the process phases; training the machine learning model in a fifth stage using the fifth training set; collecting at least one of inductance, capacitance, voltage or current measurement data at an output of an RF generator or an input of a process chamber for respective arc events occurring during a plurality of plasma chamber processes; creating a sixth training set from the collected at least one of the inductance, the capacitance, the voltage or the current measurement data of the arc events; and training the machine learning model in a sixth stage using the sixth training set.
4. The method of claim 3, wherein the machine learning model is further trained to recognize a status of the plasma chamber process, including at least one of the occurrence of an arc event, the occurrence of an ignition process, a normal operation of the plasma chamber process or destroyed plasma using the collected at least one of the inductance, the capacitance, the voltage or the current measurement data.
2. The method of claim 1, further comprising: receiving at least one of inductance, capacitance, voltage or current measurement data; and further analyzing the received at least one of the inductance, capacitance, voltage or current measurement data to determine the status of the plasma chamber process.
9. “…determining from the measured impedance values, using a machine learning (ML) model, a status of the plasma chamber process, including at least one of the occurrence of an arc event, the occurrence of an ignition process, or a normal operation of the plasma chamber process…”
5. A method for detecting and managing arc events during a plasma chamber process, comprising:
receiving impedance measurement data captured during at least one of an ignition phase, a normal operating process phase or an arc event of the plasma chamber process;
analyzing the impedance measurement data, using a machine learning model trained according to claim 1, to determine a status of the plasma chamber process, including at least one of the occurrence of an ignition process, a normal operation, or the arc event of the plasma chamber process;
if it is determined that an arc event is occurring, taking an action to suppress an arc of the arc event;
after taking the action to suppress the arc, receiving additional impedance measurement data;
analyzing the additional impedance measurement data to determine if the additional impedance measurement data includes impedance measurement data that is equal to or within a tolerance of impedance measurement data captured during the normal operating process phase of the plasma chamber process; and if the additional impedance measurement data includes impedance measurement data that is equal to or within a tolerance of impedance measurement data captured during the normal operating process phase of the plasma chamber process, determining that the arc was suppressed.
1. A method for detecting and managing arc events during a plasma chamber process, comprising:
receiving impedance measurement data captured during at least one of an ignition phase, a normal operating process phase or an arc event of the plasma chamber process;
analyzing the impedance measurement data, using a machine learning process, to determine a status of the plasma chamber process, including at least one of the occurrence of an ignition process, or a normal operation, or the arc event during of the plasma chamber process;
if it is determined that an arc event is occurring, taking an action to suppress an arc of the arc event;
after taking the action to suppress the arc, receiving additional impedance measurement data;
analyzing the additional impedance measurement data to determine if the additional impedance measurement data includes impedance measurement data that is equal to or within a tolerance of impedance measurement data captured during the normal operating process phase of the plasma chamber process;
and if the additional impedance measurement data includes impedance measurement data that is equal to or within a tolerance of impedance measurement data captured during the normal operating process phase of the plasma chamber process, determining that the arc was suppressed.
6. The method of claim 5, wherein the received measurement data originates from at least one of measurements taken at an output of an RF generator or an input of a process chamber.
3. The method of claim 1, wherein the received measurement data originates from at least one of measurements taken at an output of an RF generator or an input of a process chamber.
7. The method of claim 5, wherein the machine learning model is further trained to recognize the status of the plasma chamber process using historical impedance data.
5. The method of claim 1, wherein the machine learning process includes a machine learning model that has been trained to recognize the status of the plasma chamber process using historical impedance data.
8. The method of claim 5, wherein the action taken to suppress the arc includes turning off power to an RF generator of the plasma chamber process.
6. The method of claim 1, wherein the action taken to suppress the arc includes turning off power to an RF generator of the plasma chamber process.
9. The method of claim 5, further comprising; analyzing the additional impedance data measurements to determine if the additional impedance data measurements include impedance data measurements that are equal to or within a tolerance of impedance data measurements captured during the ignition phase of the plasma chamber process; if the additional impedance data measurements include impedance data measurements that are equal to or within a tolerance of impedance data measurements captured during the ignition phase of the plasma chamber process, determining that a plasma of the plasma chamber process was destroyed; and if the plasma was destroyed, reigniting the plasma.
7. The method of claim 1, further comprising: analyzing the additional impedance data measurements to determine if the additional impedance data measurements include impedance data measurements that are equal to or within a tolerance of impedance data measurements captured during the ignition phase of the plasma chamber process; if the additional impedance data measurements include impedance data measurements that are equal to or within a tolerance of impedance data measurements captured during the ignition phase of the plasma chamber process, determining that a plasma of the plasma chamber process was destroyed; and if the plasma was destroyed, reigniting the plasma.
10. The method of claim 5, further comprising: analyzing received reflected voltage and current measurements of the plasma chamber process to determine if a microarc arc event is occurring during the plasma chamber process.
8. The method of claim 1, further comprising: analyzing received reflected voltage and current measurements of the plasma chamber process to determine if a microarc arc event is occurring during the plasma chamber process.
11. The method of claim 5, wherein the training of the machine learning model according to claim 1 further comprises:11. The method of claim 5, wherein the training of the machine learning model according to claim 1 further comprises: collecting at least one of inductance, capacitance, voltage or current measurement data at an output of an RF generator or an input of a process chamber during respective ignition phases of a plurality of plasma chamber processes; creating a fourth training set from the collected at least one of the inductance, the capacitance, the voltage or the current measurement data; training the machine learning model in a fourth stage using the fourth training set; collecting at least one of inductance, capacitance, voltage or current measurement data at an output of an RF generator or an input of a process chamber during respective process phases of a plurality of plasma chamber processes; creating a fifth training set from the collected at least one of the inductance, the capacitance, the voltage or the current measurement data of the process phases; training the machine learning model in a fifth stage using the fifth training set; collecting at least one of inductance, capacitance, voltage or current measurement data at an output of an RF generator or an input of a process chamber for respective arc events occurring during a plurality of plasma chamber processes; creating a sixth training set from the collected at least one of the inductance, the capacitance, the voltage or the current measurement data of the arc events; and training the machine learning model in a sixth stage using the sixth training set.
7. The method of claim 1, further comprising: analyzing the additional impedance data measurements to determine if the additional impedance data measurements include impedance data measurements that are equal to or within a tolerance of impedance data measurements captured during the ignition phase of the plasma chamber process; if the additional impedance data measurements include impedance data measurements that are equal to or within a tolerance of impedance data measurements captured during the ignition phase of the plasma chamber process, determining that a plasma of the plasma chamber process was destroyed; and if the plasma was destroyed, reigniting the plasma.
12. An apparatus for detecting and managing arc events during a plasma chamber process, comprising: a processor; and a memory coupled to the processor, the memory having stored therein at least one of programs or instructions executable by the processor to configure the apparatus to:
receive impedance measurement data captured during at least one of an ignition phase, a normal operating process phase or an arc event of the plasma chamber process;
analyze the impedance measurement data, using a machine learning model trained according to claim 1,
to determine a status of the plasma chamber process, including at least one of the occurrence of an ignition process, a normal operation, or the arc event of the plasma chamber process;
if it is determined that an arc event is occurring, take an action to suppress an arc of the arc event;
after taking the action to suppress the arc, receive additional impedance measurement data;
analyze the additional impedance measurement data to determine if the additional impedance measurement data includes impedance measurement data that is equal to or within a tolerance of impedance measurement data captured during the normal operating process phase of the plasma chamber process; and
if the additional impedance measurement data includes impedance measurement data that is equal to or within a tolerance of impedance measurement data captured during the normal operating process phase of the plasma chamber process, determine that the arc was suppressed.
1. A method for detecting and managing arc events during a plasma chamber process, comprising:
receiving impedance measurement data captured during at least one of an ignition phase, a normal operating process phase or an arc event of the plasma chamber process;
analyzing the impedance measurement data, using a machine learning process,
to determine a status of the plasma chamber process, including at least one of the occurrence of an ignition process, or a normal operation, or the arc event during of the plasma chamber process;
if it is determined that an arc event is occurring, taking an action to suppress an arc of the arc event;
after taking the action to suppress the arc, receiving additional impedance measurement data;
analyzing the additional impedance measurement data to determine if the additional impedance measurement data includes impedance measurement data that is equal to or within a tolerance of impedance measurement data captured during the normal operating process phase of the plasma chamber process;
and if the additional impedance measurement data includes impedance measurement data that is equal to or within a tolerance of impedance measurement data captured during the normal operating process phase of the plasma chamber process, determining that the arc was suppressed.
13. The apparatus of claim 12, wherein the received measurement data originates from at least one of measurements taken at an output of an RF generator or an input of a process chamber.
3. The method of claim 1, wherein the received measurement data originates from at least one of measurements taken at an output of an RF generator or an input of a process chamber.
14. The apparatus of claim 12, wherein the machine learning model is further trained to recognize the status of the plasma chamber process using historical impedance data.
5. The method of claim 1, wherein the machine learning process includes a machine learning model that has been trained to recognize the status of the plasma chamber process using historical impedance data.
15. The apparatus of claim 12, wherein the action taken to suppress the arc includes turning off power to an RF generator of the plasma chamber process.
6. The method of claim 1, wherein the action taken to suppress the arc includes turning off power to an RF generator of the plasma chamber process.
16. The apparatus of claim 12, further comprising; analyzing the additional impedance data measurements to determine if the additional impedance data measurements include impedance data measurements that are equal to or within a tolerance of impedance data measurements captured during the ignition phase of the plasma chamber process; if the additional impedance data measurements include impedance data measurements that are equal to or within a tolerance of impedance data measurements captured during the ignition phase of the plasma chamber process, determining that a plasma of the plasma chamber process was destroyed; and if the plasma was destroyed, reigniting the plasma.
7. The method of claim 1, further comprising: analyzing the additional impedance data measurements to determine if the additional impedance data measurements include impedance data measurements that are equal to or within a tolerance of impedance data measurements captured during the ignition phase of the plasma chamber process; if the additional impedance data measurements include impedance data measurements that are equal to or within a tolerance of impedance data measurements captured during the ignition phase of the plasma chamber process, determining that a plasma of the plasma chamber process was destroyed; and if the plasma was destroyed, reigniting the plasma.
17. The apparatus of claim 12, further comprising: analyzing received reflected voltage and current measurements of the plasma chamber process to determine if a microarc arc event is occurring during the plasma chamber process.
8. The method of claim 1, further comprising: analyzing received reflected voltage and current measurements of the plasma chamber process to determine if a microarc arc event is occurring during the plasma chamber process.
Claim Rejections – 35 U.S.C. 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bhutta (2022/0254610) in view of Carter et al. (10,269,540).
Regarding Claim 1, Regarding Claim 1, Bhutta (2022/0254610) discloses a computer-implemented method of training a machine learning model for detecting and managing arc events during a plasma chamber process, comprising:
collecting impedance values measured (plasma impedance…may be monitored, [0055]) at an output of an RF generator (at RF output sensor 49, [0055], fig. 2) of a plurality of plasma chamber processes (“semiconductor processing”, [0055]; “During operation, in one embodiment the matching network determines the input impedance based on the information provided by an input RF sensor, such as a VI (voltage-current) sensor”, paragraph [0142]);
collecting impedance values measured at an output of an RF generator or an input of a process chamber during respective process phases of a plurality of plasma chamber processes (“During operation, in one embodiment the matching network determines the input impedance based on the information provided by an input RF sensor, such as a VI (voltage-current) sensor”, paragraph [0142]);
collecting impedance values measured at an output of an RF generator or an input of a process chamber for respective arc events occurring during a plurality of plasma chamber processes (“These sensors can detect transient changes occurring in the load. For the case where the RF generator and the matching network are used in a system to match the variable load of a plasma chamber to the RF generator, the matching network sensors can detect plasma transients, such as micro-arcing, and then provide that information to the RF generator. RF generator can then alter its power to extinguish the micro-arc”, paragraph [0378]);
As discussed above, Bhutta essentially discloses the claimed invention but does not explicitly disclose collecting impedance during ignition phase.
However, Carter et al. (10,269,540) discloses the impedance may vary based on plasma ignition (Col 1, lines 25-32).
It would have been obvious to one of ordinary skill in the art to have detected impedance during plasma ignition in Brutta in order to match the load impedance to the output impedance as taught by Carter.
As discussed above, Bhutta essentially discloses the claimed invention but does not particularly disclose creating first, second and third training sets from the collected, measured impedance value and train the machine learning model in a first, second and third stage.
However, Bhutta also discloses that the training of the learning model may occur in a variety of ways. The training of the learning model comprises the learning model determining a valid range of values for the parameter based on the received first values for the monitored system (paragraph [0344]).
A user can set the matching network in learning mode for a certain period and during that period the matching network will learn the valid ranges and not trigger an alarm or other indication of a characteristic of the matching network. After the learning period is passed, the matching network can then use the learned valid ranges to monitor the process (paragraph [0326]).
Artificial Intelligence (AI) uses machines to mimic human cognitive functions. AI can enable a machine to, in a sense, learn, reason, and predict. AI may be used with semiconductor manufacturing and processing. The system may collect data from one or more sensors, analyze the data to draw conclusions, and use the knowledge to predict future actions, paragraph [0332]).
It would have been obvious one of ordinary skill in the art to have provided a AI system to collect data from sensors and train the learning model in Bhutta in order to sense, learn, reason and predict future actions as taught by Bhutta.
Regarding Claim 2, Bhutta in view of Carter discloses the method of claim 1, wherein a status of the plasma chamber process, including at least one of the occurrence of an arc event (“detect plasma transients, such as micro-arcing, paragraph [0378] of Bhutta), the occurrence of an ignition process (detect ignition based on impedance, Col 1, lines 25-32 of Carter), a normal operation of the plasma chamber process or destroyed plasma.
As discussed above, Bhutta essentially discloses the claimed invention but does not explicitly disclose that the machine learning model is trained to recognize the status.
However, Bhutta also discloses that the training of the learning model may occur in a variety of ways. The training of the learning model comprises the learning model determining a valid range of values for the parameter based on the received first values for the monitored system (paragraph [0344]).
A user can set the matching network in learning mode for a certain period and during that period the matching network will learn the valid ranges and not trigger an alarm or other indication of a characteristic of the matching network. After the learning period is passed, the matching network can then use the learned valid ranges to monitor the process (paragraph [0326]).
Artificial Intelligence (AI) uses machines to mimic human cognitive functions. AI can enable a machine to, in a sense, learn, reason, and predict. AI may be used with semiconductor manufacturing and processing. The system may collect data from one or more sensors, analyze the data to draw conclusions, and use the knowledge to predict future actions, paragraph [0332]).
It would have been obvious to one of ordinary skill in the art to have provided AI system and training of learning model to recognize the status of each stage in Bhutta in order to analyze the data to draw conclusions, and use the knowledge to predict future actions as taught by Bhutta.
Regarding Claim 3, Bhutta discloses the method of claim 1, further comprising:
collecting at least one of inductance, capacitance, voltage or current measurement data (“plasma impedance determination is based on input impedance, the capacitance..”, paragraph [0091])at an output of an RF generator or an input of a process chamber;
collecting at least one of inductance, capacitance, voltage or current measurement data at an output of an RF generator or an input of a process chamber during respective process phases of a plurality of plasma chamber processes (“During operation, in one embodiment the matching network determines the input impedance based on the information provided by an input RF sensor, such as a VI (voltage-current) sensor”, paragraph [0142]);
collecting at least one of inductance, capacitance, voltage or current measurement data (“The matching network can have an output sensor that can detect either voltage, current, or phase”, paragraph [0378]) at an output of an RF generator or an input of a process chamber for respective arc events occurring during a plurality of plasma chamber processes (“These sensors can detect transient changes occurring in the load. For the case where the RF generator and the matching network are used in a system to match the variable load of a plasma chamber to the RF generator, the matching network sensors can detect plasma transients, such as micro-arcing, and then provide that information to the RF generator. RF generator can then alter its power to extinguish the micro-arc”, paragraph [0378]);
As discussed above, Bhutta essentially discloses the claimed invention but does not explicitly disclose collecting impedance during ignition phase.
However, Carter et al. (10,269,540) discloses the impedance may vary based on plasma ignition (Col 1, lines 25-32).
It would have been obvious to one of ordinary skill in the art to have detected impedance during plasma ignition in Brutta in order to match the load impedance to the output impedance as taught by Carter.
As discussed above, Bhutta essentially discloses the claimed invention but does not particularly disclose creating first, second and third training sets from the collected, measured impedance value and train the machine learning model in a first, second and third stage.
However, Bhutta also discloses that the training of the learning model may occur in a variety of ways. The training of the learning model comprises the learning model determining a valid range of values for the parameter based on the received first values for the monitored system (paragraph [0344])
A user can set the matching network in learning mode for a certain period and during that period the matching network will learn the valid ranges and not trigger an alarm or other indication of a characteristic of the matching network. After the learning period is passed, the matching network can then use the learned valid ranges to monitor the process (paragraph [0326]).
Artificial Intelligence (AI) uses machines to mimic human cognitive functions. AI can enable a machine to, in a sense, learn, reason, and predict. AI may be used with semiconductor manufacturing and processing. The system may collect data from one or more sensors, analyze the data to draw conclusions, and use the knowledge to predict future actions, paragraph [0332]).
It would have been obvious one of ordinary skill in the art to have provided a AI system to collect data from sensors and train the learning model in Bhutta in order to sense, learn, reason and predict future actions as taught by Bhutta.
Regarding Claim 4, Bhutta in view of Carter discloses the method of claim 1, wherein a status of the plasma chamber process, including at least one of the occurrence of an arc event (“detect plasma transients, such as micro-arcing, paragraph [0378] of Bhutta), the occurrence of an ignition process (detect ignition based on impedance, Col 1, lines 25-32 of Carter), a normal operation of the plasma chamber process or destroyed plasma.
As discussed above, Bhutta essentially discloses the claimed invention but does not explicitly disclose that the machine learning model is trained to recognize the status.
However, Bhutta also discloses that the training of the learning model may occur in a variety of ways. The training of the learning model comprises the learning model determining a valid range of values for the parameter based on the received first values for the monitored system (paragraph [0344]).
A user can set the matching network in learning mode for a certain period and during that period the matching network will learn the valid ranges and not trigger an alarm or other indication of a characteristic of the matching network. After the learning period is passed, the matching network can then use the learned valid ranges to monitor the process (paragraph [0326]).
Artificial Intelligence (AI) uses machines to mimic human cognitive functions. AI can enable a machine to, in a sense, learn, reason, and predict. AI may be used with semiconductor manufacturing and processing. The system may collect data from one or more sensors, analyze the data to draw conclusions, and use the knowledge to predict future actions, paragraph [0332]).
It would have been obvious to one of ordinary skill in the art to have provided AI system and training of learning model to recognize the status of each stage in Bhutta in order to analyze the data to draw conclusions, and use the knowledge to predict future actions as taught by Bhutta.
Correspondence
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Examiner Wilson Lee whose telephone number is (571) 272-1824. Proposed amendment and interview agenda can be submitted to Examiner’s direct fax at (571) 273-1824.
If attempts to reach the examiner by telephone are unsuccessful, examiner’s supervisor, Alexander Taningco can be reached at (571) 272-8048. Papers related to the application may be submitted by facsimile transmission. Any transmission not to be considered an official response must be clearly marked "DRAFT". The official fax number is (571) 273-8300.
Information regarding the status of an application may be obtained from the Patent Center. Status information for published applications may be obtained from Patent Center. For more information about the Patent Center, see https://patentcenter.uspto.gov. Should you have questions on access to the Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/WILSON LEE/ Primary Examiner, Art Unit 2844