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 Objections
Claims 5-10, 17 and 19 are objected to because of the following informalities:
In claim 5 line 2, “a substrate” ---, should be corrected to ---, “the substrate” ---.
In claim 6 line 2, “a substrate” ---, should be corrected to ---, “the substrate” ---.
In claim 7 line 3, “a substrate” ---, should be corrected to ---, “the substrate” ---.
In claim 8 line 3, “a degradation” ---, should be corrected to ---, “the degradation” ---.
In claim 9 line 2, “a substrate” ---, should be corrected to ---, “the substrate” ---.
In claim 10 line 2, “a substrate” ---, should be corrected to ---, “the substrate” ---.
In claim 17 line 3, “a degradation” ---, should be corrected to ---, “the degradation” ---.
In claim 19 line 2, “a substrate” ---, should be corrected to ---, “the substrate” ---.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claims 1-6, 9, 13-16 and 19 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Herman (US Publication No. 20220367225).
Regarding claim 1, Herman discloses a system (i.e., a system 100; see for example fig. 1, para. [0022]- [0025]) for monitoring health (i.e., such as system 100 monitors various operational and health parameters of ESC 108; see for example fig. 1, para. [0022]) of a pedestal (i.e., such as ESC 108; see for example fig. 1, para. [0022]) of a processing chamber (i.e., such as a plasma processing chamber 106; see for example fig. 1, para. [0022]), the system (i.e., a system 100; see for example fig. 1, para. [0022]- [0025]) comprising: a memory (i.e., such as a nonvolatile memory 520 within DAQ 104; see for example fig. 14, para. [0054]) storing instructions (i.e., such as the trained parameter values/instructions may be stored in nonvolatile memory in the electrostatic chuck supply 101 when the ESC supply 101 is shipped to an end operator/user of the ESC supply 101, or the trained parameter values may be communicated via communication link to the ESC supply 101; see for example fig. 13, para. [0050]); and a processor (i.e., such as ESC supply 101 is the main processor equipped with controller 103, sensors within DAQ 104, and neural network 150 within advisor 102; a processor 526 may include a video processor, digital signal processor (DSP), graphics processing unit (GPU), and other processing components. In operation, input component 532 may operate to receive user data input 118 of DAQ 104 and may also receive user input to enable the user to control various components of system 100. Output component 534 generally operates to provide one or more analog or digital signals to effectuate one or more operational aspects of system 100. see for example fig. 14, para. [0058]) configured to execute the instructions (i.e., such as for example, non-transitory processor-executable instructions to effectuate the methods described with reference to FIGS. 12 and 13 may be persistently stored in nonvolatile memory 520 and executed by the N processing components in connection with RAM 524; see for example fig. 14, para. [0058]) to: sense one or more currents (i.e., such as the DAQ 104 senses the incoming currents/lines 112 from the ESC 108 via its electrodes 128 and 129; Controller 103 is also in two-way communication with data acquisition system (DAQ) 104. DAQ 104 comprises sensors for continuously sensing and recording values of real time operational and health parameters 105 from the electrostatic chucking system, and continuously streams the operational parameter values 105 to controller 103. In one implementation, DAQ 104 also comprises an analog to digital converter to convert analog signals received from its various sensors into digital signals to be input into controller 103. DAQ 104 further receives user definable inputs 118 which may be, for example, threshold settings of the various sensors of DAQ 104; see for example fig. 1, para. [0024]) through one or more electrodes (i.e., such as 128 and 129; System 100 of FIG. 1 comprises an electrostatic chuck (ESC) power supply 101 in communication, via conductors 112, with an ESC 108 housed within a plasma processing chamber 106 containing plasma 114. Power supply 101 provides clamp signals via conductors 112 to clamp electrodes 128, 129 of ESC 108 in order to chuck (clamp) a workpiece 110, such as a silicon wafer, to ESC 108; see for example fig. 1, para. [0022]) arranged in the pedestal (i.e., such as ESC 108; see for example fig. 1, para. [0022]); generate one or more metrics (i.e., such as the operational parameter values 105; As previously mentioned, controller 103 streams operational parameter values 105 sensed by DAQ 104 to advisor module 102, which comprises a neural network 150 that detects inconsistencies, faults or anything unique in incoming parameter values 105 as compared to trained parameter values; see for example fig. 1, para. [0035]; Note: the metrics are generated via integrating the incoming actual data/current measurement values from ESC 108 via lines 112 with the user data/desired threshold current values via lines 118 in order to incorporate the whole data into a standard that represents the operational parameter values 105, thereby creating a pattern/criterion to better evaluate the efficiency/health of the entire system 100) based on the one or more currents (i.e., such as the DAQ 104 senses the incoming currents/lines 112 from the ESC 108 via its electrodes 128 and 129; Controller 103 is also in two-way communication with data acquisition system (DAQ) 104. DAQ 104 comprises sensors for continuously sensing and recording values of real time operational and health parameters 105 from the electrostatic chucking system, and continuously streams the operational parameter values 105 to controller 103. In one implementation, DAQ 104 also comprises an analog to digital converter to convert analog signals received from its various sensors into digital signals to be input into controller 103. DAQ 104 further receives user definable inputs 118 which may be, for example, threshold settings of the various sensors of DAQ 104; see for example fig. 1, para. [0024]); and determine (i.e., such as 150 determines the ESC's health; Advisor module 102 uses neural network 150 to determine whether the parameter values are consistent with trained parameter values, and as described with reference to FIG. 7, continuously and automatically modifies the weighting of inputs to neural network 150 when the incoming parameter values are inconsistent with the training parameter values; see for example fig. 7, para. [0038]) a health (i.e., such as the ESC's health profile submitted by 150; Neural network 150, in addition to providing health/operational reports on specific parameters, also updates weights applied to the input parameter values based on inconsistencies with trained parameter values, and may also provide more general health reports on any weighted combination of parameters or on the entire system. This is illustrated in more detail in FIG. 7, which is a diagram showing the structure of an exemplary neural network 150; see for example fig. 7, para. [0040]) of the pedestal (i.e., such as ESC 108; see for example fig. 1, para. [0022]) based on the one or more metrics (i.e., such as the operational parameter values 105; As previously mentioned, controller 103 streams operational parameter values 105 sensed by DAQ 104 to advisor module 102, which comprises a neural network 150 that detects inconsistencies, faults or anything unique in incoming parameter values 105 as compared to trained parameter values; see for example fig. 1, para. [0035]; Note: the metrics are generated via integrating the incoming actual data/current measurement values from ESC 108 via lines 112 with the user data/desired threshold current values via lines 118 in order to incorporate the whole data into a standard that represents the operational parameter values 105, thereby creating a pattern/criterion to better evaluate the efficiency/health of the entire system 100).
Regarding claim 2, Herman discloses a system (i.e., a system 100; see for example fig. 1, para. [0022]- [0025]); wherein the electrodes (i.e., such as 128 and 129; System 100 of FIG. 1 comprises an electrostatic chuck (ESC) power supply 101 in communication, via conductors 112, with an ESC 108 housed within a plasma processing chamber 106 containing plasma 114. Power supply 101 provides clamp signals via conductors 112 to clamp electrodes 128, 129 of ESC 108 in order to chuck (clamp) a workpiece 110, such as a silicon wafer, to ESC 108; see for example fig. 1, para. [0022]) include clamping electrodes (i.e., such as 128 and 129; System 100 of FIG. 1 comprises an electrostatic chuck (ESC) power supply 101 in communication, via conductors 112, with an ESC 108 housed within a plasma processing chamber 106 containing plasma 114. Power supply 101 provides clamp signals via conductors 112 to clamp electrodes 128, 129 of ESC 108 in order to chuck (clamp) a workpiece 110, such as a silicon wafer, to ESC 108; see for example fig. 1, para. [0022]) that clamp a substrate (i.e., such as 110; Power supply 101 provides clamp signals via conductors 112 to clamp electrodes 128, 129 of ESC 108 in order to chuck (clamp) a workpiece 110, such as a silicon wafer, to ESC 108; see for example fig. 1, para. [0022]) to the pedestal (i.e., such as ESC 108; see for example fig. 1, para. [0022]) during processing (i.e., such as during workpiece processing; The clamping waveform may be optimized to address issues of minimum clamping time, variation in clamping force during workpiece processing, as well as workpiece charging control to minimize workpiece “sticking” to the platen, etc.; see for example para. [0032]) of the substrate (i.e., such as 110; Power supply 101 provides clamp signals via conductors 112 to clamp electrodes 128, 129 of ESC 108 in order to chuck (clamp) a workpiece 110, such as a silicon wafer, to ESC 108; see for example fig. 1, para. [0022]) in the processing chamber (i.e., such as a plasma processing chamber 106; see for example fig. 1, para. [0022]).
Regarding claim 3, Herman discloses a system (i.e., a system 100; see for example fig. 1, para. [0022]- [0025]); wherein the processor (i.e., such as ESC supply 101 is the main processor equipped with controller 103, sensors within DAQ 104, and neural network 150 within advisor 102; a processor 526 may include a video processor, digital signal processor (DSP), graphics processing unit (GPU), and other processing components. In operation, input component 532 may operate to receive user data input 118 of DAQ 104 and may also receive user input to enable the user to control various components of system 100. Output component 534 generally operates to provide one or more analog or digital signals to effectuate one or more operational aspects of system 100. see for example fig. 14, para. [0058]) is configured to detect (i.e., such as 104 senses and 150 detects; As previously mentioned, controller 103 streams operational parameter values 105 sensed by DAQ 104 to advisor module 102, which comprises a neural network 150 that detects inconsistencies, faults or anything unique in incoming parameter values 105 as compared to trained parameter values. A neural network (NN), or artificial neural network (ANN), is a subset of learning algorithms that are loosely based on the concept of biological neural networks. Essentially, controller 103 seeks continuous “advisement” about patterns of sensed operational parameters of system 100, and advisor module 102 through use of neural network 150 provides indications of any degrees of change in the patterns of the operational parameters as compared to existing trained parameters; In particular, FIG. 2 shows various operational parameters 105 that may be continuously sensed and streamed in real time from DAQ 104 to controller 103. Operational parameters 105 may include, for example, and without limitation, voltages, currents, temperatures, capacitances, resistances, humidity, shock, chemical and EMI noise; see for example fig. 2, para. [0026]), based on the one or more metrics (i.e., such as the operational parameter values 105; As previously mentioned, controller 103 streams operational parameter values 105 sensed by DAQ 104 to advisor module 102, which comprises a neural network 150 that detects inconsistencies, faults or anything unique in incoming parameter values 105 as compared to trained parameter values; see for example fig. 1, para. [0035]; Note: the metrics are generated via integrating the incoming actual data/current measurement values from ESC 108 via lines 112 with the user data/desired threshold current values via lines 118 in order to incorporate the whole data into a standard that represents the operational parameter values 105, thereby creating a pattern/criterion to better evaluate the efficiency/health of the entire system 100), a degradation (i.e., such as fault conditions/inconsistencies; In addition, as described in more detail below, controller 103 may automatically take action such as adjusting the clamp signal in response to fault conditions, i.e., inconsistencies between current parameter values and trained parameter values; see for example fig. 2, para. [0039]) of the pedestal (i.e., such as ESC 108; see for example fig. 1, para. [0022]) due to at least one of chemical (i.e., such as chemical; In particular, FIG. 2 shows various operational parameters 105 that may be continuously sensed and streamed in real time from DAQ 104 to controller 103. Operational parameters 105 may include, for example, and without limitation, voltages, currents, temperatures, capacitances, resistances, humidity, shock, chemical and EMI noise; see for example fig. 2, para. [0026]), electrical (i.e., such as electrical; In particular, FIG. 2 shows various operational parameters 105 that may be continuously sensed and streamed in real time from DAQ 104 to controller 103. Operational parameters 105 may include, for example, and without limitation, voltages, currents, temperatures, capacitances, resistances, humidity, shock, chemical and EMI noise; see for example fig. 2, para. [0026]), and thermal environment (i.e., such as thermal environment; In particular, FIG. 2 shows various operational parameters 105 that may be continuously sensed and streamed in real time from DAQ 104 to controller 103. Operational parameters 105 may include, for example, and without limitation, voltages, currents, temperatures, capacitances, resistances, humidity, shock, chemical and EMI noise; see for example fig. 2, para. [0026]) in the processing chamber (i.e., such as a plasma processing chamber 106; see for example fig. 1, para. [0022]).
Regarding claim 4, Herman discloses a system (i.e., a system 100; see for example fig. 1, para. [0022]- [0025]); wherein the processor (i.e., such as ESC supply 101 is the main processor equipped with controller 103, sensors within DAQ 104, and neural network 150 within advisor 102; a processor 526 may include a video processor, digital signal processor (DSP), graphics processing unit (GPU), and other processing components. In operation, input component 532 may operate to receive user data input 118 of DAQ 104 and may also receive user input to enable the user to control various components of system 100. Output component 534 generally operates to provide one or more analog or digital signals to effectuate one or more operational aspects of system 100. see for example fig. 14, para. [0058]) is configured to predict (i.e., such as to predict; Thus, in this manner, based on a dynamic weighted combination of operational parameters 105, neural network 150 continuously provides an indication of the general health of system 100, and is able to predict and react to fault or other unique conditions as they arise; see for example fig. 8, para. [0043]) a problem (i.e., such as a problem/fault; Thus, in this manner, based on a dynamic weighted combination of operational parameters 105, neural network 150 continuously provides an indication of the general health of system 100, and is able to predict and react to fault or other unique conditions as they arise; see for example fig. 8, para. [0043]) with the pedestal (i.e., such as ESC 108; see for example fig. 1, para. [0022]) based on the one or more metrics (i.e., such as the operational parameter values 105; As previously mentioned, controller 103 streams operational parameter values 105 sensed by DAQ 104 to advisor module 102, which comprises a neural network 150 that detects inconsistencies, faults or anything unique in incoming parameter values 105 as compared to trained parameter values; see for example fig. 1, para. [0035]; Note: the metrics are generated via integrating the incoming actual data/current measurement values from ESC 108 via lines 112 with the user data/desired threshold current values via lines 118 in order to incorporate the whole data into a standard that represents the operational parameter values 105, thereby creating a pattern/criterion to better evaluate the efficiency/health of the entire system 100).
Regarding claim 5, Herman discloses a system (i.e., a system 100; see for example fig. 1, para. [0022]- [0025]); wherein the processor (i.e., such as ESC supply 101 is the main processor equipped with controller 103, sensors within DAQ 104, and neural network 150 within advisor 102; a processor 526 may include a video processor, digital signal processor (DSP), graphics processing unit (GPU), and other processing components. In operation, input component 532 may operate to receive user data input 118 of DAQ 104 and may also receive user input to enable the user to control various components of system 100. Output component 534 generally operates to provide one or more analog or digital signals to effectuate one or more operational aspects of system 100. see for example fig. 14, para. [0058]) is configured to predict (i.e., such as to predict; Thus, in this manner, based on a dynamic weighted combination of operational parameters 105, neural network 150 continuously provides an indication of the general health of system 100, and is able to predict and react to fault or other unique conditions as they arise; see for example fig. 8, para. [0043]) a likelihood (i.e., such a risk; In the example of FIG. 10, with respect to TRAIN DATA 3, something about this combination of capacitance, resistance and voltage value resulted in a fault (0) output, and may be associated with a particular condition, such as, for example, clamp fault risk. Thus, if this particular combination of capacitance, resistance and voltage values occurs again after training, the issue can be safely handled in a predetermined manner; see for example fig. 10, para. [0047]) of occurrence (i.e., such as occurring; In this manner, a combination of multiple parameter values can be correlated with a particular condition, and by comparing those values at any given time with the trained values, neural network 150 can predict the likelihood of that condition occurring. Again, neural network 150 is continuously trained and updated as new combinations of parameter values are encountered, and weights applied by neural network 150 to those parameter values are modified based on inconsistencies with trained parameter values; see for example fig. 10, para. [0047]) of a defect (i.e., such as a clamp fault; In the example of FIG. 10, with respect to TRAIN DATA 3, something about this combination of capacitance, resistance and voltage value resulted in a fault (0) output, and may be associated with a particular condition, such as, for example, clamp fault risk; see for example fig. 10, para. [0047]) in a substrate (i.e., such as 110; Power supply 101 provides clamp signals via conductors 112 to clamp electrodes 128, 129 of ESC 108 in order to chuck (clamp) a workpiece 110, such as a silicon wafer, to ESC 108; see for example fig. 1, para. [0022]) processed on the pedestal (i.e., such as ESC 108; see for example fig. 1, para. [0022]) based on the one or more metrics (i.e., such as the operational parameter values 105; As previously mentioned, controller 103 streams operational parameter values 105 sensed by DAQ 104 to advisor module 102, which comprises a neural network 150 that detects inconsistencies, faults or anything unique in incoming parameter values 105 as compared to trained parameter values; see for example fig. 1, para. [0035]; Note: the metrics are generated via integrating the incoming actual data/current measurement values from ESC 108 via lines 112 with the user data/desired threshold current values via lines 118 in order to incorporate the whole data into a standard that represents the operational parameter values 105, thereby creating a pattern/criterion to better evaluate the efficiency/health of the entire system 100).
Regarding claim 6, Herman discloses a system (i.e., a system 100; see for example fig. 1, para. [0022]- [0025]); wherein the processor (i.e., such as ESC supply 101 is the main processor equipped with controller 103, sensors within DAQ 104, and neural network 150 within advisor 102; a processor 526 may include a video processor, digital signal processor (DSP), graphics processing unit (GPU), and other processing components. In operation, input component 532 may operate to receive user data input 118 of DAQ 104 and may also receive user input to enable the user to control various components of system 100. Output component 534 generally operates to provide one or more analog or digital signals to effectuate one or more operational aspects of system 100. see for example fig. 14, para. [0058]) is configured to detect (i.e., such as 104 senses and 150 detects; As previously mentioned, controller 103 streams operational parameter values 105 sensed by DAQ 104 to advisor module 102, which comprises a neural network 150 that detects inconsistencies, faults or anything unique in incoming parameter values 105 as compared to trained parameter values. A neural network (NN), or artificial neural network (ANN), is a subset of learning algorithms that are loosely based on the concept of biological neural networks. Essentially, controller 103 seeks continuous “advisement” about patterns of sensed operational parameters of system 100, and advisor module 102 through use of neural network 150 provides indications of any degrees of change in the patterns of the operational parameters as compared to existing trained parameters; In particular, FIG. 2 shows various operational parameters 105 that may be continuously sensed and streamed in real time from DAQ 104 to controller 103. Operational parameters 105 may include, for example, and without limitation, voltages, currents, temperatures, capacitances, resistances, humidity, shock, chemical and EMI noise; see for example fig. 2, para. [0026]) a problem (i.e., such as a problem/fault; Thus, in this manner, based on a dynamic weighted combination of operational parameters 105, neural network 150 continuously provides an indication of the general health of system 100, and is able to predict and react to fault or other unique conditions as they arise; see for example fig. 8, para. [0043]) with clamping of (i.e., such as a clamp fault; In the example of FIG. 10, with respect to TRAIN DATA 3, something about this combination of capacitance, resistance and voltage value resulted in a fault (0) output, and may be associated with a particular condition, such as, for example, clamp fault risk; see for example fig. 10, para. [0047]) a substrate (i.e., such as 110; Power supply 101 provides clamp signals via conductors 112 to clamp electrodes 128, 129 of ESC 108 in order to chuck (clamp) a workpiece 110, such as a silicon wafer, to ESC 108; see for example fig. 1, para. [0022]) to the pedestal (i.e., such as ESC 108; see for example fig. 1, para. [0022]) based on the one or more metrics (i.e., such as the operational parameter values 105; As previously mentioned, controller 103 streams operational parameter values 105 sensed by DAQ 104 to advisor module 102, which comprises a neural network 150 that detects inconsistencies, faults or anything unique in incoming parameter values 105 as compared to trained parameter values; see for example fig. 1, para. [0035]; Note: the metrics are generated via integrating the incoming actual data/current measurement values from ESC 108 via lines 112 with the user data/desired threshold current values via lines 118 in order to incorporate the whole data into a standard that represents the operational parameter values 105, thereby creating a pattern/criterion to better evaluate the efficiency/health of the entire system 100).
Regarding claim 9, Herman discloses a system (i.e., a system 100; see for example fig. 1, para. [0022]- [0025]); wherein the processor (i.e., such as ESC supply 101 is the main processor equipped with controller 103, sensors within DAQ 104, and neural network 150 within advisor 102; a processor 526 may include a video processor, digital signal processor (DSP), graphics processing unit (GPU), and other processing components. In operation, input component 532 may operate to receive user data input 118 of DAQ 104 and may also receive user input to enable the user to control various components of system 100. Output component 534 generally operates to provide one or more analog or digital signals to effectuate one or more operational aspects of system 100. see for example fig. 14, para. [0058]) is configured to sense the currents (i.e., such as the DAQ 104 senses the incoming currents/lines 112 from the ESC 108 via its electrodes 128 and 129; Controller 103 is also in two-way communication with data acquisition system (DAQ) 104. DAQ 104 comprises sensors for continuously sensing and recording values of real time operational and health parameters 105 from the electrostatic chucking system, and continuously streams the operational parameter values 105 to controller 103. In one implementation, DAQ 104 also comprises an analog to digital converter to convert analog signals received from its various sensors into digital signals to be input into controller 103. DAQ 104 further receives user definable inputs 118 which may be, for example, threshold settings of the various sensors of DAQ 104; see for example fig. 1, para. [0024]) during one or more processing steps (i.e., such as the steps of clamp, hold, and plasma processing in the plasma processing chamber 106 containing plasma 114; The clamping waveform may be optimized to address issues of minimum clamping time, variation in clamping force during workpiece processing, as well as workpiece charging control to minimize workpiece “sticking” to the platen, etc. Importantly, according to the present invention, the shape and characteristics of the clamp waveform may be modified by controller 103 in response to detections of inconsistencies between trained parameter values and field parameter values detected by advisor module 102; see for example fig. 1, para. [0032]) performed on a substrate (i.e., such as 110; Power supply 101 provides clamp signals via conductors 112 to clamp electrodes 128, 129 of ESC 108 in order to chuck (clamp) a workpiece 110, such as a silicon wafer, to ESC 108; see for example fig. 1, para. [0022]) arranged on the pedestal (i.e., such as ESC 108; see for example fig. 1, para. [0022]).
Regarding claim 13, Herman discloses a system (i.e., a system 100; see for example fig. 1, para. [0022]- [0025]); wherein the processor (i.e., such as ESC supply 101 is the main processor equipped with controller 103, sensors within DAQ 104, and neural network 150 within advisor 102; a processor 526 may include a video processor, digital signal processor (DSP), graphics processing unit (GPU), and other processing components. In operation, input component 532 may operate to receive user data input 118 of DAQ 104 and may also receive user input to enable the user to control various components of system 100. Output component 534 generally operates to provide one or more analog or digital signals to effectuate one or more operational aspects of system 100. see for example fig. 14, para. [0058]) is configured to determine (i.e., such as 150 determines the ESC's health; Advisor module 102 uses neural network 150 to determine whether the parameter values are consistent with trained parameter values, and as described with reference to FIG. 7, continuously and automatically modifies the weighting of inputs to neural network 150 when the incoming parameter values are inconsistent with the training parameter values; see for example fig. 7, para. [0038]) the health (i.e., such as the ESC's health profile submitted by 150; Neural network 150, in addition to providing health/operational reports on specific parameters, also updates weights applied to the input parameter values based on inconsistencies with trained parameter values, and may also provide more general health reports on any weighted combination of parameters or on the entire system. This is illustrated in more detail in FIG. 7, which is a diagram showing the structure of an exemplary neural network 150; see for example fig. 7, para. [0040]) of the pedestal (i.e., such as ESC 108; see for example fig. 1, para. [0022]) by comparing (i.e., compared to; Controller 103, in turn, streams parameter values 105 received from DAQ 104 to advisor module 102, which uses a trained neural network to detect inconsistencies or faults in parameter values 105 as compared to previously trained parameter values. Advisor module 102 continuously reports its detected results back to controller 103, which then takes appropriate action; see for example fig. 1, para. [0025]) the one or more metrics (i.e., such as the operational parameter values 105; As previously mentioned, controller 103 streams operational parameter values 105 sensed by DAQ 104 to advisor module 102, which comprises a neural network 150 that detects inconsistencies, faults or anything unique in incoming parameter values 105 as compared to trained parameter values; see for example fig. 1, para. [0035]; Note: the metrics are generated via integrating the incoming actual data/current measurement values from ESC 108 via lines 112 with the user data/desired threshold current values via lines 118 in order to incorporate the whole data into a standard that represents the operational parameter values 105, thereby creating a pattern/criterion to better evaluate the efficiency/health of the entire system 100) to respective thresholds (i.e., such as the respective thresholds desired by the user and to be input via lines 118; DAQ 104 further receives user definable inputs 118 which may be, for example, threshold settings of the various sensors of DAQ 104; see for example fig. 1, para. [0024]).
Regarding claim 14, Herman discloses a system (i.e., a system 100; see for example fig. 1, para. [0022]- [0025]); wherein the processor (i.e., such as ESC supply 101 is the main processor equipped with controller 103, sensors within DAQ 104, and neural network 150 within advisor 102; a processor 526 may include a video processor, digital signal processor (DSP), graphics processing unit (GPU), and other processing components. In operation, input component 532 may operate to receive user data input 118 of DAQ 104 and may also receive user input to enable the user to control various components of system 100. Output component 534 generally operates to provide one or more analog or digital signals to effectuate one or more operational aspects of system 100. see for example fig. 14, para. [0058]) is configured to determine (i.e., such as 150 determines the ESC's health; Advisor module 102 uses neural network 150 to determine whether the parameter values are consistent with trained parameter values, and as described with reference to FIG. 7, continuously and automatically modifies the weighting of inputs to neural network 150 when the incoming parameter values are inconsistent with the training parameter values; see for example fig. 7, para. [0038]) the respective thresholds (i.e., such as the respective thresholds desired by the user and to be input via lines 118; DAQ 104 further receives user definable inputs 118 which may be, for example, threshold settings of the various sensors of DAQ 104; see for example fig. 1, para. [0024]) based on data (i.e., such as the pure incoming data from 106 to 104 via 112 to create outgoing operational data 105; Operational parameters 105 may include data that is provided by waveform generators 120, 121 to DAQ 104, such as voltages and currents within or output by waveform generators 120, 121; see for example fig. 3, para. [0027]) received from one or more processing chambers (i.e., such as a plasma processing chamber 106; see for example fig. 1, para. [0022]).
Regarding claim 15, Herman discloses a system (i.e., a system 100; see for example fig. 1, para. [0022]- [0025]); wherein the processor (i.e., such as ESC supply 101 is the main processor equipped with controller 103, sensors within DAQ 104, and neural network 150 within advisor 102; a processor 526 may include a video processor, digital signal processor (DSP), graphics processing unit (GPU), and other processing components. In operation, input component 532 may operate to receive user data input 118 of DAQ 104 and may also receive user input to enable the user to control various components of system 100. Output component 534 generally operates to provide one or more analog or digital signals to effectuate one or more operational aspects of system 100. see for example fig. 14, para. [0058]) is configured to determine (i.e., such as 150 determines the ESC's health; Advisor module 102 uses neural network 150 to determine whether the parameter values are consistent with trained parameter values, and as described with reference to FIG. 7, continuously and automatically modifies the weighting of inputs to neural network 150 when the incoming parameter values are inconsistent with the training parameter values; see for example fig. 7, para. [0038]) the health (i.e., such as the ESC's health profile submitted by 150; Neural network 150, in addition to providing health/operational reports on specific parameters, also updates weights applied to the input parameter values based on inconsistencies with trained parameter values, and may also provide more general health reports on any weighted combination of parameters or on the entire system. This is illustrated in more detail in FIG. 7, which is a diagram showing the structure of an exemplary neural network 150; see for example fig. 7, para. [0040]) of the pedestal (i.e., such as ESC 108; see for example fig. 1, para. [0022]) by comparing (i.e., compared to; Controller 103, in turn, streams parameter values 105 received from DAQ 104 to advisor module 102, which uses a trained neural network to detect inconsistencies or faults in parameter values 105 as compared to previously trained parameter values. Advisor module 102 continuously reports its detected results back to controller 103, which then takes appropriate action; see for example fig. 1, para. [0025]) the one or more metrics (i.e., such as the operational parameter values 105; As previously mentioned, controller 103 streams operational parameter values 105 sensed by DAQ 104 to advisor module 102, which comprises a neural network 150 that detects inconsistencies, faults or anything unique in incoming parameter values 105 as compared to trained parameter values; see for example fig. 1, para. [0035]; Note: the metrics are generated via integrating the incoming actual data/current measurement values from ESC 108 via lines 112 with the user data/desired threshold current values via lines 118 in order to incorporate the whole data into a standard that represents the operational parameter values 105, thereby creating a pattern/criterion to better evaluate the efficiency/health of the entire system 100) to respective predetermined ranges (i.e., such as the train data is to be set within a predetermined table; In the example of FIG. 10, with respect to TRAIN DATA 3, something about this combination of capacitance, resistance and voltage value resulted in a fault (0) output, and may be associated with a particular condition, such as, for example, clamp fault risk. Thus, if this particular combination of capacitance, resistance and voltage values occurs again after training, the issue can be safely handled in a predetermined manner. In this manner, a combination of multiple parameter values can be correlated with a particular condition, and by comparing those values at any given time with the trained values, neural network 150 can predict the likelihood of that condition occurring. Again, neural network 150 is continuously trained and updated as new combinations of parameter values are encountered, and weights applied by neural network 150 to those parameter values are modified based on inconsistencies with trained parameter values; see for example fig. 10, para. [0047]).
Regarding claim 16, Herman discloses a system (i.e., a system 100; see for example fig. 1, para. [0022]- [0025]); wherein the processor (i.e., such as ESC supply 101 is the main processor equipped with controller 103, sensors within DAQ 104, and neural network 150 within advisor 102; a processor 526 may include a video processor, digital signal processor (DSP), graphics processing unit (GPU), and other processing components. In operation, input component 532 may operate to receive user data input 118 of DAQ 104 and may also receive user input to enable the user to control various components of system 100. Output component 534 generally operates to provide one or more analog or digital signals to effectuate one or more operational aspects of system 100. see for example fig. 14, para. [0058]) is configured to determine (i.e., such as 150 determines the ESC's health; Advisor module 102 uses neural network 150 to determine whether the parameter values are consistent with trained parameter values, and as described with reference to FIG. 7, continuously and automatically modifies the weighting of inputs to neural network 150 when the incoming parameter values are inconsistent with the training parameter values; see for example fig. 7, para. [0038]) the respective predetermined ranges (i.e., such as the train data is to be set within a predetermined table; In the example of FIG. 10, with respect to TRAIN DATA 3, something about this combination of capacitance, resistance and voltage value resulted in a fault (0) output, and may be associated with a particular condition, such as, for example, clamp fault risk. Thus, if this particular combination of capacitance, resistance and voltage values occurs again after training, the issue can be safely handled in a predetermined manner. In this manner, a combination of multiple parameter values can be correlated with a particular condition, and by comparing those values at any given time with the trained values, neural network 150 can predict the likelihood of that condition occurring. Again, neural network 150 is continuously trained and updated as new combinations of parameter values are encountered, and weights applied by neural network 150 to those parameter values are modified based on inconsistencies with trained parameter values; see for example fig. 10, para. [0047]) based on data (i.e., such as the pure incoming data from 106 to 104 via 112 to create outgoing operational data 105; Operational parameters 105 may include data that is provided by waveform generators 120, 121 to DAQ 104, such as voltages and currents within or output by waveform generators 120, 121; see for example fig. 3, para. [0027]) received from one or more processing chambers (i.e., such as a plasma processing chamber 106; see for example fig. 1, para. [0022]).
Regarding claim 19, Herman discloses a system (i.e., a system 100; see for example fig. 1, para. [0022]- [0025]); wherein at least one of the electrodes (i.e., such as 128 and 129; System 100 of FIG. 1 comprises an electrostatic chuck (ESC) power supply 101 in communication, via conductors 112, with an ESC 108 housed within a plasma processing chamber 106 containing plasma 114. Power supply 101 provides clamp signals via conductors 112 to clamp electrodes 128, 129 of ESC 108 in order to chuck (clamp) a workpiece 110, such as a silicon wafer, to ESC 108; see for example fig. 1, para. [0022]) is exposed (i.e., such as the electrodes are exposed to the plasma 114 in order to account for the plasma factor in terms of the processed workpiece, for instance; the workpiece capacitance value analysis; see for example fig. 9, para. [0045]) to plasma (i.e., such as plasma 114; System 100 of FIG. 1 comprises an electrostatic chuck (ESC) power supply 101 in communication, via conductors 112, with an ESC 108 housed within a plasma processing chamber 106 containing plasma 114. Power supply 101 provides clamp signals via conductors 112 to clamp electrodes 128, 129 of ESC 108 in order to chuck (clamp) a workpiece 110, such as a silicon wafer, to ESC 108; see for example fig. 1, para. [0022]) during processing (i.e., such as the steps of clamp, hold, and plasma processing in the plasma processing chamber 106 containing plasma 114; The clamping waveform may be optimized to address issues of minimum clamping time, variation in clamping force during workpiece processing, as well as workpiece charging control to minimize workpiece “sticking” to the platen, etc. Importantly, according to the present invention, the shape and characteristics of the clamp waveform may be modified by controller 103 in response to detections of inconsistencies between trained parameter values and field parameter values detected by advisor module 102; see for example fig. 1, para. [0032]) of a substrate (i.e., such as 110; Power supply 101 provides clamp signals via conductors 112 to clamp electrodes 128, 129 of ESC 108 in order to chuck (clamp) a workpiece 110, such as a silicon wafer, to ESC 108; see for example fig. 1, para. [0022]) on the pedestal (i.e., such as ESC 108; see for example fig. 1, para. [0022]) and wherein one of the currents (i.e., such as the DAQ 104 senses the incoming currents/lines 112 from the ESC 108 via its electrodes 128 and 129; Controller 103 is also in two-way communication with data acquisition system (DAQ) 104. DAQ 104 comprises sensors for continuously sensing and recording values of real time operational and health parameters 105 from the electrostatic chucking system, and continuously streams the operational parameter values 105 to controller 103. In one implementation, DAQ 104 also comprises an analog to digital converter to convert analog signals received from its various sensors into digital signals to be input into controller 103. DAQ 104 further receives user definable inputs 118 which may be, for example, threshold settings of the various sensors of DAQ 104; see for example fig. 1, para. [0024]) through the at least one of the electrodes (i.e., such as 128 and 129; System 100 of FIG. 1 comprises an electrostatic chuck (ESC) power supply 101 in communication, via conductors 112, with an ESC 108 housed within a plasma processing chamber 106 containing plasma 114. Power supply 101 provides clamp signals via conductors 112 to clamp electrodes 128, 129 of ESC 108 in order to chuck (clamp) a workpiece 110, such as a silicon wafer, to ESC 108; see for example fig. 1, para. [0022]) includes a plasma component (i.e., such as a plasma 114; System 100 of FIG. 1 comprises an electrostatic chuck (ESC) power supply 101 in communication, via conductors 112, with an ESC 108 housed within a plasma processing chamber 106 containing plasma 114. Power supply 101 provides clamp signals via conductors 112 to clamp electrodes 128, 129 of ESC 108 in order to chuck (clamp) a workpiece 110, such as a silicon wafer, to ESC 108; see for example fig. 1, para. [0022]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Herman (US Publication No. 20220367225) in view of Valcore, JR. (US Publication No. 20140173158).
Regarding claim 7, Herman discloses the system (i.e., a system 100; see for example fig. 1, para. [0022]- [0025]); wherein the processor (i.e., such as ESC supply 101 is the main processor equipped with controller 103, sensors within DAQ 104, and neural network 150 within advisor 102; a processor 526 may include a video processor, digital signal processor (DSP), graphics processing unit (GPU), and other processing components. In operation, input component 532 may operate to receive user data input 118 of DAQ 104 and may also receive user input to enable the user to control various components of system 100. Output component 534 generally operates to provide one or more analog or digital signals to effectuate one or more operational aspects of system 100. see for example fig. 14, para. [0058]) is configured to predict (i.e., such as to predict; Thus, in this manner, based on a dynamic weighted combination of operational parameters 105, neural network 150 continuously provides an indication of the general health of system 100, and is able to predict and react to fault or other unique conditions as they arise; see for example fig. 8, para. [0043]), based on the one or more metrics (i.e., such as the operational parameter values 105; As previously mentioned, controller 103 streams operational parameter values 105 sensed by DAQ 104 to advisor module 102, which comprises a neural network 150 that detects inconsistencies, faults or anything unique in incoming parameter values 105 as compared to trained parameter values; see for example fig. 1, para. [0035]; Note: the metrics are generated via integrating the incoming actual data/current measurement values from ESC 108 via lines 112 with the user data/desired threshold current values via lines 118 in order to incorporate the whole data into a standard that represents the operational parameter values 105, thereby creating a pattern/criterion to better evaluate the efficiency/health of the entire system 100), a likelihood (i.e., such a risk; In the example of FIG. 10, with respect to TRAIN DATA 3, something about this combination of capacitance, resistance and voltage value resulted in a fault (0) output, and may be associated with a particular condition, such as, for example, clamp fault risk. Thus, if this particular combination of capacitance, resistance and voltage values occurs again after training, the issue can be safely handled in a predetermined manner; see for example fig. 10, para. [0047]) of occurrence (i.e., such as occurring; In this manner, a combination of multiple parameter values can be correlated with a particular condition, and by comparing those values at any given time with the trained values, neural network 150 can predict the likelihood of that condition occurring. Again, neural network 150 is continuously trained and updated as new combinations of parameter values are encountered, and weights applied by neural network 150 to those parameter values are modified based on inconsistencies with trained parameter values; see for example fig. 10, para. [0047]) of arcing (i.e., such as a clamp fault; In the example of FIG. 10, with respect to TRAIN DATA 3, something about this combination of capacitance, resistance and voltage value resulted in a fault (0) output, and may be associated with a particular condition, such as, for example, clamp fault risk; see for example fig. 10, para. [0047]) in the processing chamber (i.e., such as a plasma processing chamber 106; see for example fig. 1, para. [0022]) when a substrate (i.e., such as 110; Power supply 101 provides clamp signals via conductors 112 to clamp electrodes 128, 129 of ESC 108 in order to chuck (clamp) a workpiece 110, such as a silicon wafer, to ESC 108; see for example fig. 1, para. [0022]) is processed (i.e., such as the steps of clamp, hold, and plasma processing in the plasma processing chamber 106 containing plasma 114; The clamping waveform may be optimized to address issues of minimum clamping time, variation in clamping force during workpiece processing, as well as workpiece charging control to minimize workpiece “sticking” to the platen, etc. Importantly, according to the present invention, the shape and characteristics of the clamp waveform may be modified by controller 103 in response to detections of inconsistencies between trained parameter values and field parameter values detected by advisor module 102; see for example fig. 1, para. [0032]) on the pedestal (i.e., such as ESC 108; see for example fig. 1, para. [0022]).
Herman does not explicitly disclose arcing.
Valcore, JR. discloses a rate of data transfer within a plasma system (i.e., a system 100; see for example fig. 1, para. [0024]- [0075]); wherein arcing (i.e., such as arcing; FIG. 5 is a diagram of embodiments of graphs 502, 504, and 506 used to illustrate that the variables help in determining an event, e.g., unconfinement of plasma within the plasma chamber 128 (FIG. 1). Examples of other events include arcing, change in load impedance, change in health of plasma chamber 128, etc.; see for example fig. 5, para. [0109]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have optionally included the ESC arcing study in Herman, as taught by Valcore, JR., as it provides the advantage of optimizing the circuit design towards better assessment of the plasma processing system efficiency.
Claims 8, 11-12, 17-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Herman (US Publication No. 20220367225) in view of Jafarian-Tehrani et al (US Publication No. 20080297971).
Regarding claim 8, Herman discloses the system (i.e., a system 100; see for example fig. 1, para. [0022]- [0025]); wherein the processor (i.e., such as ESC supply 101 is the main processor equipped with controller 103, sensors within DAQ 104, and neural network 150 within advisor 102; a processor 526 may include a video processor, digital signal processor (DSP), graphics processing unit (GPU), and other processing components. In operation, input component 532 may operate to receive user data input 118 of DAQ 104 and may also receive user input to enable the user to control various components of system 100. Output component 534 generally operates to provide one or more analog or digital signals to effectuate one or more operational aspects of system 100. see for example fig. 14, para. [0058]) is configured to detect (i.e., such as 104 senses and 150 detects; As previously mentioned, controller 103 streams operational parameter values 105 sensed by DAQ 104 to advisor module 102, which comprises a neural network 150 that detects inconsistencies, faults or anything unique in incoming parameter values 105 as compared to trained parameter values. A neural network (NN), or artificial neural network (ANN), is a subset of learning algorithms that are loosely based on the concept of biological neural networks. Essentially, controller 103 seeks continuous “advisement” about patterns of sensed operational parameters of system 100, and advisor module 102 through use of neural network 150 provides indications of any degrees of change in the patterns of the operational parameters as compared to existing trained parameters; In particular, FIG. 2 shows various operational parameters 105 that may be continuously sensed and streamed in real time from DAQ 104 to controller 103. Operational parameters 105 may include, for example, and without limitation, voltages, currents, temperatures, capacitances, resistances, humidity, shock, chemical and EMI noise; see for example fig. 2, para. [0026]) an imbalance (i.e., such as a problem/fault; Thus, in this manner, based on a dynamic weighted combination of operational parameters 105, neural network 150 continuously provides an indication of the general health of system 100, and is able to predict and react to fault or other unique conditions as they arise; see for example fig. 8, para. [0043]) in the currents (i.e., such as the DAQ 104 senses the incoming currents/lines 112 from the ESC 108 via its electrodes 128 and 129; Controller 103 is also in two-way communication with data acquisition system (DAQ) 104. DAQ 104 comprises sensors for continuously sensing and recording values of real time operational and health parameters 105 from the electrostatic chucking system, and continuously streams the operational parameter values 105 to controller 103. In one implementation, DAQ 104 also comprises an analog to digital converter to convert analog signals received from its various sensors into digital signals to be input into controller 103. DAQ 104 further receives user definable inputs 118 which may be, for example, threshold settings of the various sensors of DAQ 104; see for example fig. 1, para. [0024]) based on the one or more metrics (i.e., such as the operational parameter values 105; As previously mentioned, controller 103 streams operational parameter values 105 sensed by DAQ 104 to advisor module 102, which comprises a neural network 150 that detects inconsistencies, faults or anything unique in incoming parameter values 105 as compared to trained parameter values; see for example fig. 1, para. [0035]; Note: the metrics are generated via integrating the incoming actual data/current measurement values from ESC 108 via lines 112 with the user data/desired threshold current values via lines 118 in order to incorporate the whole data into a standard that represents the operational parameter values 105, thereby creating a pattern/criterion to better evaluate the efficiency/health of the entire system 100) and wherein the imbalance (i.e., such as a problem/fault; Thus, in this manner, based on a dynamic weighted combination of operational parameters 105, neural network 150 continuously provides an indication of the general health of system 100, and is able to predict and react to fault or other unique conditions as they arise; see for example fig. 8, para. [0043]) is indicative (i.e., such as indicative; Thus, in this manner, based on a dynamic weighted combination of operational parameters 105, neural network 150 continuously provides an indication of the general health of system 100, and is able to predict and react to fault or other unique conditions as they arise; see for example fig. 8, para. [0043]) of a degradation (i.e., such as fault conditions/inconsistencies; In addition, as described in more detail below, controller 103 may automatically take action such as adjusting the clamp signal in response to fault conditions, i.e., inconsistencies between current parameter values and trained parameter values; see for example fig. 2, para. [0039]) of the pedestal (i.e., such as ESC 108; see for example fig. 1, para. [0022]) due to at least one of chemical (i.e., such as chemical; In particular, FIG. 2 shows various operational parameters 105 that may be continuously sensed and streamed in real time from DAQ 104 to controller 103. Operational parameters 105 may include, for example, and without limitation, voltages, currents, temperatures, capacitances, resistances, humidity, shock, chemical and EMI noise; see for example fig. 2, para. [0026]), electrical (i.e., such as electrical; In particular, FIG. 2 shows various operational parameters 105 that may be continuously sensed and streamed in real time from DAQ 104 to controller 103. Operational parameters 105 may include, for example, and without limitation, voltages, currents, temperatures, capacitances, resistances, humidity, shock, chemical and EMI noise; see for example fig. 2, para. [0026]), and thermal environment (i.e., such as thermal environment; In particular, FIG. 2 shows various operational parameters 105 that may be continuously sensed and streamed in real time from DAQ 104 to controller 103. Operational parameters 105 may include, for example, and without limitation, voltages, currents, temperatures, capacitances, resistances, humidity, shock, chemical and EMI noise; see for example fig. 2, para. [0026]) in the processing chamber (i.e., such as a plasma processing chamber 106; see for example fig. 1, para. [0022]).
Herman does not explicitly disclose the imbalance between currents.
Jafarian-Tehrani discloses a plasma processing system (i.e., see for example fig. 2A, para. [0042]- [0054]); wherein currents imbalance (i.e., such as the imbalance between the positive load current 215 and the negative load current associated with negative high voltage 399 in the plasma processing system leads to the imbalance between the first force and the second force of the ESC clamping force, consequently leads to unhealthy condition, thereby, reducing the remaining life of the ESC; see for example para. [0066]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have optionally included ESC current study in Herman, as taught by Jafarian-Tehrani, as it provides the advantage of optimizing the circuit design towards evaluating the plasma processing system efficiency.
Regarding claim 11, Herman in view of Jafarian-Tehrani and the teachings of Herman as modified by Jafarian-Tehrani have discussed above.
Herman further discloses the system (i.e., a system 100; see for example fig. 1, para. [0022]- [0025]); wherein the processor (i.e., such as ESC supply 101 is the main processor equipped with controller 103, sensors within DAQ 104, and neural network 150 within advisor 102; a processor 526 may include a video processor, digital signal processor (DSP), graphics processing unit (GPU), and other processing components. In operation, input component 532 may operate to receive user data input 118 of DAQ 104 and may also receive user input to enable the user to control various components of system 100. Output component 534 generally operates to provide one or more analog or digital signals to effectuate one or more operational aspects of system 100. see for example fig. 14, para. [0058]) is configured to generate the one or more metrics (i.e., such as the operational parameter values 105; As previously mentioned, controller 103 streams operational parameter values 105 sensed by DAQ 104 to advisor module 102, which comprises a neural network 150 that detects inconsistencies, faults or anything unique in incoming parameter values 105 as compared to trained parameter values; see for example fig. 1, para. [0035]; Note: the metrics are generated via integrating the incoming actual data/current measurement values from ESC 108 via lines 112 with the user data/desired threshold current values via lines 118 in order to incorporate the whole data into a standard that represents the operational parameter values 105, thereby creating a pattern/criterion to better evaluate the efficiency/health of the entire system 100).
Jafarian-Tehrani furthermore discloses the plasma processing system (i.e., see for example fig. 2A, para. [0042]- [0054]); wherein based on at least one of averages of the currents (i.e., such as the average current calculated based upon the intermediate terms X and Y, particularly, current ILP equation 243; see for example fig. 2C, para. [0055]) and differences between the currents (i.e., such as the difference between the current of plot 408 and the current of plot 406 and that is 6.6 micro-A, which corresponds to minus 800 volts, more specifically the amount of minus 800 volts is the voltage needed to securely balanced clamping of the processed workpiece; see for example fig. 4, para. [0065]).
Regarding claim 12, Herman in view of Jafarian-Tehrani and the teachings of Herman as modified by Jafarian-Tehrani have discussed above.
Herman further discloses the system (i.e., a system 100; see for example fig. 1, para. [0022]- [0025]); wherein the processor (i.e., such as ESC supply 101 is the main processor equipped with controller 103, sensors within DAQ 104, and neural network 150 within advisor 102; a processor 526 may include a video processor, digital signal processor (DSP), graphics processing unit (GPU), and other processing components. In operation, input component 532 may operate to receive user data input 118 of DAQ 104 and may also receive user input to enable the user to control various components of system 100. Output component 534 generally operates to provide one or more analog or digital signals to effectuate one or more operational aspects of system 100. see for example fig. 14, para. [0058]) is configured to generate the one or more metrics (i.e., such as the operational parameter values 105; As previously mentioned, controller 103 streams operational parameter values 105 sensed by DAQ 104 to advisor module 102, which comprises a neural network 150 that detects inconsistencies, faults or anything unique in incoming parameter values 105 as compared to trained parameter values; see for example fig. 1, para. [0035]; Note: the metrics are generated via integrating the incoming actual data/current measurement values from ESC 108 via lines 112 with the user data/desired threshold current values via lines 118 in order to incorporate the whole data into a standard that represents the operational parameter values 105, thereby creating a pattern/criterion to better evaluate the efficiency/health of the entire system 100).
Jafarian-Tehrani furthermore discloses the plasma processing system (i.e., see for example fig. 2A, para. [0042]- [0054]); wherein based on a statistical analysis of the currents (i.e., such as the mathematical calculations of the current in the ESC circuit, for instance; current 202, current 204, current 206, current 215, etc.; see for example fig. 2A, para. [0048]- [0059]).
Regarding claim 17, Herman in view of Jafarian-Tehrani and the teachings of Herman as modified by Jafarian-Tehrani have discussed above.
Herman further discloses the system (i.e., a system 100; see for example fig. 1, para. [0022]- [0025]); wherein the processor (i.e., such as ESC supply 101 is the main processor equipped with controller 103, sensors within DAQ 104, and neural network 150 within advisor 102; a processor 526 may include a video processor, digital signal processor (DSP), graphics processing unit (GPU), and other processing components. In operation, input component 532 may operate to receive user data input 118 of DAQ 104 and may also receive user input to enable the user to control various components of system 100. Output component 534 generally operates to provide one or more analog or digital signals to effectuate one or more operational aspects of system 100. see for example fig. 14, para. [0058]) is configured to detect (i.e., such as 104 senses and 150 detects; As previously mentioned, controller 103 streams operational parameter values 105 sensed by DAQ 104 to advisor module 102, which comprises a neural network 150 that detects inconsistencies, faults or anything unique in incoming parameter values 105 as compared to trained parameter values. A neural network (NN), or artificial neural network (ANN), is a subset of learning algorithms that are loosely based on the concept of biological neural networks. Essentially, controller 103 seeks continuous “advisement” about patterns of sensed operational parameters of system 100, and advisor module 102 through use of neural network 150 provides indications of any degrees of change in the patterns of the operational parameters as compared to existing trained parameters; In particular, FIG. 2 shows various operational parameters 105 that may be continuously sensed and streamed in real time from DAQ 104 to controller 103. Operational parameters 105 may include, for example, and without limitation, voltages, currents, temperatures, capacitances, resistances, humidity, shock, chemical and EMI noise; see for example fig. 2, para. [0026]) an imbalance (i.e., such as a problem/fault; Thus, in this manner, based on a dynamic weighted combination of operational parameters 105, neural network 150 continuously provides an indication of the general health of system 100, and is able to predict and react to fault or other unique conditions as they arise; see for example fig. 8, para. [0043]) in the currents (i.e., such as the DAQ 104 senses the incoming currents/lines 112 from the ESC 108 via its electrodes 128 and 129; Controller 103 is also in two-way communication with data acquisition system (DAQ) 104. DAQ 104 comprises sensors for continuously sensing and recording values of real time operational and health parameters 105 from the electrostatic chucking system, and continuously streams the operational parameter values 105 to controller 103. In one implementation, DAQ 104 also comprises an analog to digital converter to convert analog signals received from its various sensors into digital signals to be input into controller 103. DAQ 104 further receives user definable inputs 118 which may be, for example, threshold settings of the various sensors of DAQ 104; see for example fig. 1, para. [0024]) based on the one or more metrics (i.e., such as the operational parameter values 105; As previously mentioned, controller 103 streams operational parameter values 105 sensed by DAQ 104 to advisor module 102, which comprises a neural network 150 that detects inconsistencies, faults or anything unique in incoming parameter values 105 as compared to trained parameter values; see for example fig. 1, para. [0035]; Note: the metrics are generated via integrating the incoming actual data/current measurement values from ESC 108 via lines 112 with the user data/desired threshold current values via lines 118 in order to incorporate the whole data into a standard that represents the operational parameter values 105, thereby creating a pattern/criterion to better evaluate the efficiency/health of the entire system 100) and wherein the imbalance (i.e., such as a problem/fault; Thus, in this manner, based on a dynamic weighted combination of operational parameters 105, neural network 150 continuously provides an indication of the general health of system 100, and is able to predict and react to fault or other unique conditions as they arise; see for example fig. 8, para. [0043]) is indicative (i.e., such as indicative; Thus, in this manner, based on a dynamic weighted combination of operational parameters 105, neural network 150 continuously provides an indication of the general health of system 100, and is able to predict and react to fault or other unique conditions as they arise; see for example fig. 8, para. [0043]) of a degradation (i.e., such as fault conditions/inconsistencies; In addition, as described in more detail below, controller 103 may automatically take action such as adjusting the clamp signal in response to fault conditions, i.e., inconsistencies between current parameter values and trained parameter values; see for example fig. 2, para. [0039]) of the pedestal (i.e., such as ESC 108; see for example fig. 1, para. [0022]) due to at least one of chemical (i.e., such as chemical; In particular, FIG. 2 shows various operational parameters 105 that may be continuously sensed and streamed in real time from DAQ 104 to controller 103. Operational parameters 105 may include, for example, and without limitation, voltages, currents, temperatures, capacitances, resistances, humidity, shock, chemical and EMI noise; see for example fig. 2, para. [0026]), electrical (i.e., such as electrical; In particular, FIG. 2 shows various operational parameters 105 that may be continuously sensed and streamed in real time from DAQ 104 to controller 103. Operational parameters 105 may include, for example, and without limitation, voltages, currents, temperatures, capacitances, resistances, humidity, shock, chemical and EMI noise; see for example fig. 2, para. [0026]), and thermal environment (i.e., such as thermal environment; In particular, FIG. 2 shows various operational parameters 105 that may be continuously sensed and streamed in real time from DAQ 104 to controller 103. Operational parameters 105 may include, for example, and without limitation, voltages, currents, temperatures, capacitances, resistances, humidity, shock, chemical and EMI noise; see for example fig. 2, para. [0026]) in the processing chamber (i.e., such as a plasma processing chamber 106; see for example fig. 1, para. [0022]).
Jafarian-Tehrani furthermore discloses the plasma processing system (i.e., see for example fig. 2A, para. [0042]- [0054]); wherein two of the currents (i.e., such as the positive load current 215 and the negative load current associated with negative high voltage 399; see for example fig. 2A, para. [0042]- [0054]) through two of the electrodes (i.e., such as electrode 299 and electrode 399; see for example fig. 2A, para. [0042]- [0054]) flow in opposite directions (i.e., such as in opposite directions because terminal 299 is positive while terminal 399 is negative; see for example fig. 2A, para. [0042]- [0054]), currents imbalance (i.e., such as the imbalance between the positive load current 215 and the negative load current associated with negative high voltage 399 in the plasma processing system leads to the imbalance between the first force and the second force of the ESC clamping force, consequently leads to unhealthy condition, thereby, reducing the remaining life of the ESC; see for example para. [0066]).
Regarding claim 18, Herman in view of Jafarian-Tehrani and the teachings of Herman as modified by Jafarian-Tehrani have discussed above.
Jafarian-Tehrani further discloses the plasma processing system (i.e., see for example fig. 2A, para. [0042]- [0054]); wherein the directions (i.e., such as the polarity of the ESC between terminals; the positive +HV terminal and the negative -HV terminal; see for example fig. 2A, para. [0042]- [0054]) of the currents (i.e., such as the positive load current 215 and the negative load current associated with negative high voltage 399; see for example fig. 2A, para. [0042]- [0054]) through the two electrodes (i.e., such as electrode 299 and electrode 399; see for example fig. 2A, para. [0042]- [0054]) are reversed (i.e., such as reversed; The first ESC voltage may be positive, and the second ESC voltage may be negative. In one or more embodiments, for example, when the polarities of the positive terminal and the negative terminals are reversed, the first ESC voltage may be negative, and the second ESC voltage may be positive; see for example para. [0029]) during processing (i.e., such as during processing; FIG. 2A illustrates a schematic representation of an arrangement for measuring a value/amperage I.sub.LP of a positive load current 215 applied to a positive terminal of an electrostatic chuck in a plasma processing system (similar to plasma processing system 160 illustrated in the example of FIG. 1A) in accordance with one or more embodiments of the invention; see for example fig. 2A, para. [0042]- [0054]) of each successive substrate (i.e., such as of each secured and balanced clamped wafer, the amperage of the positive load current and/or the negative load current obtained may be utilized in determining the health condition and/or the life expectancy of the electrostatic chuck; see for example fig. 2A, para. [0042]- [0054]).
Regarding claim 20, Herman in view of Jafarian-Tehrani and the teachings of Herman as modified by Jafarian-Tehrani have discussed above.
Herman further discloses the system (i.e., a system 100; see for example fig. 1, para. [0022]- [0025]); wherein the processor (i.e., such as ESC supply 101 is the main processor equipped with controller 103, sensors within DAQ 104, and neural network 150 within advisor 102; a processor 526 may include a video processor, digital signal processor (DSP), graphics processing unit (GPU), and other processing components. In operation, input component 532 may operate to receive user data input 118 of DAQ 104 and may also receive user input to enable the user to control various components of system 100. Output component 534 generally operates to provide one or more analog or digital signals to effectuate one or more operational aspects of system 100. see for example fig. 14, para. [0058]) is configured to determine (i.e., such as 150 determines the ESC's health; Advisor module 102 uses neural network 150 to determine whether the parameter values are consistent with trained parameter values, and as described with reference to FIG. 7, continuously and automatically modifies the weighting of inputs to neural network 150 when the incoming parameter values are inconsistent with the training parameter values; see for example fig. 7, para. [0038]) the plasma component (i.e., such as a plasma 114; System 100 of FIG. 1 comprises an electrostatic chuck (ESC) power supply 101 in communication, via conductors 112, with an ESC 108 housed within a plasma processing chamber 106 containing plasma 114. Power supply 101 provides clamp signals via conductors 112 to clamp electrodes 128, 129 of ESC 108 in order to chuck (clamp) a workpiece 110, such as a silicon wafer, to ESC 108; see for example fig. 1, para. [0022]) and to offset (i.e., such as to offset; FIG. 5 shows a second example of a clamp waveform 140. Waveform 140 is tailored to offset the effect that plasma in a process chamber may create a voltage offset at the ESC. This voltage in conjunction with the electrode voltage changes the potential difference across the ESC 108 and can cause a discharge across the chuck dielectric or to workpiece 110 itself. Either can result in damage. To account for this offset, a bias voltage is applied to each of the signals at electrodes 128, 129 during the time that plasma is present. As can be seen in FIG. 5, this adds to phase A at region 142, and subtracts from phase B at region 144. According to the present invention, the bias voltage may be adjusted by controller 103 in response to inconsistencies between trained parameter values and field parameter values detected by advisor module 102; see for example fig. 5, para. [0034]) the currents (i.e., such as the DAQ 104 senses the incoming currents/lines 112 from the ESC 108 via its electrodes 128 and 129; Controller 103 is also in two-way communication with data acquisition system (DAQ) 104. DAQ 104 comprises sensors for continuously sensing and recording values of real time operational and health parameters 105 from the electrostatic chucking system, and continuously streams the operational parameter values 105 to controller 103. In one implementation, DAQ 104 also comprises an analog to digital converter to convert analog signals received from its various sensors into digital signals to be input into controller 103. DAQ 104 further receives user definable inputs 118 which may be, for example, threshold settings of the various sensors of DAQ 104; see for example fig. 1, para. [0024]) through two of the electrodes (i.e., such as 128 and 129; System 100 of FIG. 1 comprises an electrostatic chuck (ESC) power supply 101 in communication, via conductors 112, with an ESC 108 housed within a plasma processing chamber 106 containing plasma 114. Power supply 101 provides clamp signals via conductors 112 to clamp electrodes 128, 129 of ESC 108 in order to chuck (clamp) a workpiece 110, such as a silicon wafer, to ESC 108; see for example fig. 1, para. [0022]) by the plasma component (i.e., such as a plasma 114; System 100 of FIG. 1 comprises an electrostatic chuck (ESC) power supply 101 in communication, via conductors 112, with an ESC 108 housed within a plasma processing chamber 106 containing plasma 114. Power supply 101 provides clamp signals via conductors 112 to clamp electrodes 128, 129 of ESC 108 in order to chuck (clamp) a workpiece 110, such as a silicon wafer, to ESC 108; see for example fig. 1, para. [0022]).
Jafarian-Tehrani furthermore discloses the plasma processing system (i.e., see for example fig. 2A, para. [0042]- [0054]); to offset the currents (i.e., such as to offset the currents; The plasma processing system may also include logic or a program (e.g., included in a bias control/sensor input unit similar to bias control or sensor input 198 illustrated in the example of FIG. 1A) for adjusting a bias voltage such that the value of the positive load current and the value of the negative load current may have an equal magnitude or may have an offset that corrects an error of an estimated plasma induced bias voltage. Accordingly, the first force and the second force may be balanced, and the wafer may be secured on the ESC; see for example para. [0026]).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Herman (US Publication No. 20220367225) in view of Ye et al (US Publication No. 20160049323).
Regarding claim 10, Herman discloses the system (i.e., a system 100; see for example fig. 1, para. [0022]- [0025]); wherein the processor (i.e., such as ESC supply 101 is the main processor equipped with controller 103, sensors within DAQ 104, and neural network 150 within advisor 102; a processor 526 may include a video processor, digital signal processor (DSP), graphics processing unit (GPU), and other processing components. In operation, input component 532 may operate to receive user data input 118 of DAQ 104 and may also receive user input to enable the user to control various components of system 100. Output component 534 generally operates to provide one or more analog or digital signals to effectuate one or more operational aspects of system 100. see for example fig. 14, para. [0058]) is configured to sense the currents (i.e., such as the DAQ 104 senses the incoming currents/lines 112 from the ESC 108 via its electrodes 128 and 129; Controller 103 is also in two-way communication with data acquisition system (DAQ) 104. DAQ 104 comprises sensors for continuously sensing and recording values of real time operational and health parameters 105 from the electrostatic chucking system, and continuously streams the operational parameter values 105 to controller 103. In one implementation, DAQ 104 also comprises an analog to digital converter to convert analog signals received from its various sensors into digital signals to be input into controller 103. DAQ 104 further receives user definable inputs 118 which may be, for example, threshold settings of the various sensors of DAQ 104; see for example fig. 1, para. [0024]) during at least one of the following operations (i.e., such as during workpiece processing; The clamping waveform may be optimized to address issues of minimum clamping time, variation in clamping force during workpiece processing, as well as workpiece charging control to minimize workpiece “sticking” to the platen, etc.; see for example para. [0032]) performed on a substrate (i.e., such as 110; Power supply 101 provides clamp signals via conductors 112 to clamp electrodes 128, 129 of ESC 108 in order to chuck (clamp) a workpiece 110, such as a silicon wafer, to ESC 108; see for example fig. 1, para. [0022]) arranged on the pedestal (i.e., such as ESC 108; see for example fig. 1, para. [0022]): clamping (i.e., such as clamping; see for example fig. 5, para. [0034]) the substrate (i.e., such as 110; Power supply 101 provides clamp signals via conductors 112 to clamp electrodes 128, 129 of ESC 108 in order to chuck (clamp) a workpiece 110, such as a silicon wafer, to ESC 108; see for example fig. 1, para. [0022]) to the pedestal (i.e., such as ESC 108; see for example fig. 1, para. [0022]); holding (i.e., such as clamping; see for example fig. 5, para. [0034]) the clamped (i.e., such as during workpiece processing; The clamping waveform may be optimized to address issues of minimum clamping time, variation in clamping force during workpiece processing, as well as workpiece charging control to minimize workpiece “sticking” to the platen, etc.; see for example para. [0032]) substrate (i.e., such as 110; Power supply 101 provides clamp signals via conductors 112 to clamp electrodes 128, 129 of ESC 108 in order to chuck (clamp) a workpiece 110, such as a silicon wafer, to ESC 108; see for example fig. 1, para. [0022]) on the pedestal (i.e., such as ESC 108; see for example fig. 1, para. [0022]).
Herman does not explicitly disclose supplying a process gas to the processing chamber; and processing the substrate using the process gas by striking plasma in the processing chamber.
Ye discloses an ESC apparatus system (i.e., a PECVD system 100; see for example fig. 1, para. [0026]- [0037]); wherein supplying a process gas (i.e., such as supplying a process gas; The chamber lid 104 may comprise one or more gas distribution systems 108 disposed therethrough for delivering reactant and cleaning gases into the processing region 120; see for example fig. 1, para. [0026]) to the processing chamber (i.e., such as the processing chamber; A chamber liner 127 made of ceramic or the like is disposed in the processing region 120 to protect the sidewalls 112 from the corrosive processing environment. The chamber liner 127 may be supported by a ledge 129 formed in the sidewalls 112. A plurality of exhaust ports 131 may be formed on the chamber liner 127. The plurality of exhaust ports 131 is configured to connect the processing region 120 to the pumping channel 125; see for example fig. 1, para. [0027]); and processing the substrate (i.e., such as the electrostatic chuck 128 is configured for supporting and holding a substrate being processed. In one embodiment, the electrostatic chuck 128 may comprise at least one electrode 123 to which a voltage is applied to electrostatically secure a substrate thereon; see for example fig. 1, para. [0031]) using the process gas (i.e., such as supplying a process gas; The chamber lid 104 may comprise one or more gas distribution systems 108 disposed therethrough for delivering reactant and cleaning gases into the processing region 120; see for example fig. 1, para. [0026]) by striking plasma (i.e., such as a connection may be formed by striking and sustaining plasma between the substrate 121 and the chamber sidewalls (e.g., sidewalls 112 shown in FIG. 1) which behave as a conductive media to close the electric current loop to supply the chucking voltage and charges to the contact gap 230. Releasing the substrate from the electrostatic chuck is achieved by removing the chucking voltage supplied to the electrode 223, together with the charges contained in the body 228, and in the meantime the plasma is kept running until the charges on the substrate 121 is drained; see for example fig. 1, para. [0041])in the processing chamber (i.e., such as the processing chamber; A chamber liner 127 made of ceramic or the like is disposed in the processing region 120 to protect the sidewalls 112 from the corrosive processing environment. The chamber liner 127 may be supported by a ledge 129 formed in the sidewalls 112. A plurality of exhaust ports 131 may be formed on the chamber liner 127. The plurality of exhaust ports 131 is configured to connect the processing region 120 to the pumping channel 125; see for example fig. 1, para. [0027]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have optionally included the ESC operation-sequence in Herman, as taught by Ye, as it provides the advantage of optimizing the circuit design towards assessing the efficiency of the ESC system.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MUAAMAR Q AL-TAWEEL whose telephone number is (571)270-0339. The examiner can normally be reached 0730-1700.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Thienvu V Tran can be reached at (571) 270- 1276. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MUAAMAR QAHTAN AL-TAWEEL/Examiner, Art Unit 2838
/THIENVU V TRAN/ Supervisory Patent Examiner, Art Unit 2838