Control and prediction of multiple plasma coupled surfaces and corresponding power transfer

Abstract

Embodiments disclosed herein include a process power controller for a plasma processing tool. In one embodiment, a process power controller includes a process power source optimizer, a source predictor, and a process uniformity controller. In an embodiment, a source predictor is communicatively coupled to a process power source optimizer and a process uniformity controller.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. patent application No. 17 / 737,670, filed on May 5, 2022, the entire contents of which are hereby incorporated by reference herein. Technical Field

[0003] Embodiments relate to the field of semiconductor manufacturing, and in particular to adaptive control architectures for improved impedance matching. Background Art

[0004] In a plasma processing tool, the plasma is ignited by a cathode coupled to a process gas within the chamber. In most tools, a power source is coupled to the cathode through an impedance matching network (sometimes simply referred to as a "match"). Matching allows the impedance of the system to be adjusted to change the impedance of the load to which the cathode is coupled. The impedance matching network load has a wide range of impedances that are determined by parameters such as process conditions, chamber architecture, and the like. Matching the impedance is important to provide efficient power transfer from the power source to the load by adjusting the impedance of the impedance matching network to be equal to the transmission line impedance.

[0005] In some plasma systems, multiple antennas are used to control the plasma density at different locations within the plasma chamber. For example, an inner antenna and an outer antenna can control the plasma density at the center of a wafer and at the edge of a wafer, respectively. A change in plasma density changes the impedance of the system. As such, the matching needs to be adjusted to match the new load impedance of the system. In other words, the impedance changes, and the matching changes after the impedance changes. Such a configuration makes it difficult to quickly and accurately match impedance changes in the system because the impedance control follows the changes in the system. Summary of the invention

[0006] Embodiments disclosed herein include a process power controller for a plasma processing tool. In one embodiment, the process power controller includes a process power source optimizer, a source predictor, and a process uniformity controller. In one embodiment, the source predictor is communicatively coupled to the process power source optimizer and the process uniformity controller.

[0007] Embodiments may also include a method of controlling a plasma system. In one embodiment, the method includes: making a first parameter change in a first control loop; feeding the first parameter change forward to a second control loop; and making a second parameter change in the second control loop to anticipate changes to the plasma system resulting from the first parameter change.

[0008] Embodiments may also include a method of controlling a plasma system. In one embodiment, the method includes: changing a first variable reactance actuator; feeding forward the change of the first variable reactance actuator to a matching controller; and in response to the change of the first variable reactance actuator, using the matching controller to change an actuation setting of one or both of a second variable reactance actuator and a third variable reactance actuator. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 is a schematic diagram of a plasma processing tool including a centralized control architecture according to one embodiment.

[0010] Figure 2 is a process flow diagram of a process for feeding forward information from a first control loop to a second control loop according to an embodiment.

[0011] Figure 3 is a process flow diagram of a process for feeding forward changes to a third variable capacitor to a controller to control impedance in a plasma system according to an embodiment.

[0012] Figure 4 is a schematic diagram of a control architecture for a plasma processing tool according to one embodiment.

[0013] Figure 5A According to one embodiment Figure 4 Schematic diagram of the predictor in the control architecture.

[0014] Figure 5B is a flow chart illustrating data flow to / from a predictor according to one embodiment.

[0015] Figure 6 is a diagram of a process control architecture for a plasma processing tool according to one embodiment.

[0016] Fig. 7A is a schematic diagram of an RF matching system according to one embodiment.

[0017] Figure 7B is a process flow diagram of a process for finding a value for inductor L3 matching a system, according to an embodiment.

[0018] Figure 7C is a process flow diagram of a process for finding values ​​of inductor L1 and inductor L2 for a matching system according to one embodiment.

[0019] Figure 8 A block diagram illustrates an exemplary computer system that may be used in conjunction with a processing tool according to an embodiment. DETAILED DESCRIPTION

[0020] The system described herein includes an adaptive control architecture with supported models to improve process power control and associated plasma control at multiple sets of power coupling surfaces that affect plasma density and sheath. In the following description, many specific details are set forth in order to provide a thorough understanding of the embodiments. It will be apparent to those skilled in the art that the embodiments can be practiced without these specific details. In other cases, well-known aspects are not described in detail to avoid unnecessarily obscuring the embodiments. In addition, it should be understood that the various embodiments shown in the drawings are illustrative representations and are not necessarily drawn to scale.

[0021] As described above, control of plasma parameters of a plasma system is not cohesive. A change to one aspect of the plasma in the system may change other aspects of the plasma. For example, a change in plasma density may cause the impedance of the load to change. Because the impedance changes, the matching configuration must be changed to match the new impedance. However, embodiments disclosed herein include a feedforward control system between control loops to provide centralized and cohesive control of a plasma processing system.

[0022] Power coupling to a plasma source occurs (1) through multiple injection sites; and (2) using multiple (sometimes distributed) antennas. Through electromagnetic interactions in these mechanisms, power coupling from one site or antenna can affect power delivery at a second site or antenna. Accordingly, embodiments disclosed herein use control of power coupling to predict power coupling changes to minimize perturbations and instabilities that can affect device performance and yield.

[0023] In one embodiment, centralized control enables predictive and adaptive control between plasma power coupling and power delivery. A general scheme is demonstrated where power coupling configured through a first process power plasma coupling point (e.g., internal) and a second process power plasma coupling point (e.g., external) is controlled with minimization of perturbations by impedance tuning prediction (power delivery) under the influence of absorbed power dynamics.

[0024] In general, embodiments described herein include two control loops coupled together by a feed-forward link. A first control loop changes process power flowing through a pair of injection sites (one for internal plasma and the other for external plasma). When the first control loop changes local plasma parameters (such as density), the impedance of the system changes. To allow optimal power transfer (i.e., minimize reflected power), changes to the first control loop are fed forward to the second control loop, which controls a second aspect of power coupling (such as impedance matching). As such, the second control loop can adjust (anticipate changes in impedance) to maximize power delivery.

[0025] More specifically, the first control loop includes a third variable capacitor and the second control loop includes the first variable capacitor and the second variable capacitor. Changes to the third variable capacitor are made to modify the plasma density within the plasma system. In one embodiment, changes to the third variable capacitor are fed forward to the second control loop so that changes are made to the first variable capacitor and / or the second variable capacitor to predict changes to the impedance.

[0026] In general, a plasma processing tool includes one or more power supplies. The power supplies are coupled to a cathode (or antenna) in a chamber through an impedance matching network. Process power sensors (e.g., voltage and current sensors (V / I sensors)) are provided upstream and downstream of the impedance matching network. The sensors help control power delivery to the cathode.

[0027] In one embodiment, the first control loop and the second control loop may be implemented in an impedance matching network. The impedance matching network may include a circuit system including at least a first variable capacitor, a second variable capacitor, and a third variable capacitor. The first variable capacitor and the second variable capacitor may be part of the second control loop, and the third variable capacitor may be part of the first control loop.

[0028] Reference now Figure 1 , a more detailed schematic diagram of a plasma processing tool 100 is shown according to one embodiment. In one embodiment, the plasma processing tool 100 includes a plasma chamber 120. The plasma chamber 120 includes a cathode 122 to couple received power to one or more gases flowing into the plasma chamber 120. In one embodiment, the plasma chamber 120 can be suitable for any plasma process typical of a semiconductor manufacturing environment. For example, the plasma chamber 120 can be a plasma etching chamber, a plasma deposition chamber, a plasma treatment chamber, or the like. In particular embodiments, the plasma chamber 120 can be a plasma enhanced chemical vapor deposition (PECVD) chamber, a physical vapor deposition (PVD) chamber, or a plasma enhanced atomic layer deposition (PEALD) chamber.

[0029] In one embodiment, the plasma chamber 120 may be coupled to a power delivery architecture. For example, the power delivery architecture may include one or more power supplies 1321-132 nIn the illustrated embodiment, multiple power supplies 132 are shown. However, it should be understood that in some embodiments, a single power supply 132 may be used. In one embodiment, the power supply 132 may include any type of power supply. For example, the power supply 132 may be an RF power supply, a microwave power supply, a direct current (DC) power supply, a pulsed DC power supply, or the like, or any combination of these power supply types.

[0030] In one embodiment, the power source 132 may be coupled to the cathode 122 via a process power optimizer actuator 130. The process power optimizer actuator 130 may sometimes be referred to as an impedance matching network. The process power optimizer actuator 130 changes the impedance of the power delivery architecture in order to match the load in the chamber 120. Due to changes in processing conditions (e.g., gas flow rate, pressure, temperature, etc.), the impedance of the load may change. As such, the process power optimizer actuator 130 is used to match the changing impedance in order to provide efficient and optimal power delivery (i.e., no or minimal reflected power) into the chamber. In one embodiment, the circuit system for matching impedance may include a first variable reactance actuator and a second variable reactance actuator. For example, in some embodiments, the variable reactance actuator may be a variable capacitor.

[0031] The process power optimizer actuator 130 may also include circuitry for controlling the plasma density within the plasma chamber 120. For example, a third variable reactance actuator may control the process power through the first of the two outputs. The power from each output is coupled to the plasma and determines the plasma density. In one embodiment, the circuitry having the first variable reactance actuator, the second variable reactance actuator, and the third variable reactance actuator may be connected together as part of a larger circuit. However, the third variable reactance actuator may be on a different control loop than the first variable reactance actuator and the second variable reactance actuator.

[0032] In one embodiment, sensor 151 and sensor 152 may be provided at opposite ends of impedance matching network 130. For example, sensors 1511-151 n The sensor 152 may be on the upstream side of the impedance matching network 130, while the sensor 154 may be on the downstream side of the impedance matching network 130. The "upstream" side may refer to the input side of the matching network 130, and the "downstream" side may refer to the output side of the matching network 130. As shown, multiple sensors 1511-151 nis provided on the upstream side of the impedance matching network 130. The number of sensors 151 may be equal to the number of power sources 132. In other words, each power source 132 may have a dedicated sensor 151. The downstream side of the impedance matching network 130 may have a single sensor 152. However, it should be understood that when there is more than one output from the matching network 130, there may be additional sensors 152. For example, where there are two outputs (e.g., for the center of the chamber 120 and the edge of the chamber 120), there may be two sensors 152.

[0033] In the case of multiple sensors 151, multiple sensors 151 can be manufactured on a single PCB. 1-n That is, a single module may include multiple sensors. In general, embodiments described herein include electrical shielding techniques that limit cross-coupling between sensors on a single PCB.

[0034] exist Figure 1 , sensors 151 and 152 are generally shown as boxes. However, it should be understood that sensors 151 and 152 can be similar to any suitable sensor architecture. For example, each sensor 151 and / or sensor 152 can be a voltage and current (i.e., V / I) sensor. The voltage can be detected by an embedded voltage loop, and the current can be detected by a current loop. The sensor can have a hole through which a cable (e.g., an RF cable) passes.

[0035] In one embodiment, sensor 151 and sensor 152 may be communicatively coupled to processing module 134. As shown, Figure 1 130. A process power control module 134 is provided in the tool 100. However, it should be understood that the processing module can be a microwave processing module 134, a DC processing module, and an RF processing module or the like, depending on what type of power supply 132 is included in the tool 100. In one embodiment, the sensors 151 and 152 deliver voltage and / or current to the process power control module 134. The process power control module 134 can also provide control of a first control loop and a second control loop for changing the impedance in the impedance matching network 130 and / or the plasma density within the chamber 120. In one embodiment, the processing module 134 can have external links for different physical layers and protocols, such as Ethernet (ENET) and standardized industrial connections, such as EtherCAT.

[0036] In one embodiment, the processing module 134 can be coupled to the impedance matching network. The processing module 134 can be capable of sending control signals to the impedance matching network 130. For example, the control signal can be used to adjust a variable reactance actuator within the impedance matching network 130. Additionally, the processing module 134 can be coupled to the power supply 132. As such, the processing module 134 can coordinate impedance tuning for optimal power transfer.

[0037] It will be appreciated that changes to the first control loop will result in changes to the impedance. As such, changes in the first control loop will necessitate changes in the second control loop. In conventional systems, the first control loop changes, and the second control loop will change later in time to account for the new impedance. That is, the control of the impedance lags behind the changes in the system. As such, the dynamic control response of the impedance matching is less than ideal.

[0038] Accordingly, embodiments disclosed herein include a feed-forward architecture between a first control loop and a second control loop. Changes to the first control loop are fed forward to the second control loop. In this way, the second control loop can make changes in anticipation of changes in system impedance. As a result, the second control loop no longer lags behind the first control loop, and power delivery performance is improved.

[0039] Reference now Figure 2 , a process flow diagram of a process 270 for controlling a semiconductor processing tool is shown, according to an embodiment. In an embodiment, the process 270 can be used to link two or more control loops together to provide improved control of a system. For example, the process 270 can be used to improve the efficiency of power delivery to a plasma processing tool, such as the tool described in more detail above.

[0040] In one embodiment, process 270 may begin with operation 271, which includes making a first parameter change in a first control loop. In one embodiment, the first parameter change may be a change to any parameter within a plasma processing tool. For example, the first parameter change may be a change in plasma density in one or more regions within a plasma processing tool. In a specific embodiment, the first parameter change may be a change in a variable reactance actuator in an impedance matching network. The variable capacitor may control current flowing through two or more inductors, which dictate plasma density in one or more regions of a plasma chamber.

[0041] In one embodiment, process 270 may continue with operation 272, which includes feeding forward the first parameter change to the second control loop. In one embodiment, the feedforward operation may occur before the first parameter change occurs. In other embodiments, the feedforward operation may occur substantially simultaneously with the occurrence of the first parameter change. In one embodiment, the second control loop may include a pair of variable capacitors. The second control loop may be responsible for setting the impedance of the impedance matching network. In particular, the first parameter change may cause a change in the load impedance. As such, the pair of variable capacitors may need to change their capacitance to match the new impedance of the system. Because the feedforward signal is delivered to the second control loop before or simultaneously with the first parameter change, the second control loop can anticipate changes in the system impedance and provide improved system control.

[0042] In one embodiment, process 270 may continue with operation 273, which includes making a second parameter change in the second control loop to anticipate the system change resulting from the first parameter change. In particular, the second parameter change may be a mechanical change to one or more variable capacitors to change the impedance of the impedance matching network. As described above, the system change may be a change in the impedance of the system. Accordingly, rapid changes to the second control loop may be used to improve control of the system.

[0043] Reference now Figure 3 , according to one embodiment, a process flow diagram of a process 380 for controlling an impedance matching network in a plasma tool is shown. In one embodiment, the process 380 may include a first control loop and a second control loop. The first control loop may be used to change the amount of current flowing through a pair of inductors in the impedance matching network, and the second control loop may be used to change the impedance of the impedance matching network.

[0044] In one embodiment, process 380 may begin with operation 381, which includes changing the capacitance of a third variable capacitor in the circuit. In one embodiment, the third variable capacitor may be a capacitor that controls the flow of current through a pair of inductors. For example, a first inductor may be responsible for generating plasma in the center of the chamber, and a second inductor may be responsible for generating plasma at the edge of the chamber. Thus, changes in the capacitance of the third capacitor may be used to change the plasma density within the chamber at the center and edge of the chamber.

[0045] In one embodiment, process 380 may continue with operation 382, ​​which includes feeding forward the capacitance change of the third capacitor to the matching controller. In one embodiment, the matching controller may be responsible for adjusting the capacitance of the variable capacitor within the impedance matching network. For example, the matching controller may be responsible for controlling the third capacitor, the first capacitor, and the second capacitor. The first capacitor and the second capacitor may be used to change the impedance of the matching network.

[0046] In one embodiment, the feed-forward operation may occur when the change to the third capacitor is implemented. In other embodiments, the feed-forward operation may occur before the change to the third capacitor is implemented. As such, the change to the impedance of the matching network may anticipate the change to the impedance in the system. Accordingly, the control of the impedance matching is improved.

[0047] In one embodiment, process 380 may continue with operation 383, which includes using a matching controller to change the capacitance of one or both of the first variable capacitor and the second variable capacitor in the circuit in response to or in anticipation of a change in the capacitance of the third variable capacitor 383. In one embodiment, the matching controller may send a control signal to the impedance matching network to change the capacitance of the first variable capacitor and the second variable capacitor. The control signal may be sent to one or more motor controllers that physically change the variable capacitor. In other embodiments, the first variable capacitor and the second variable capacitor are solid-state capacitors and the control signal may not necessarily change the physical structure of the first variable capacitor and the second variable capacitor.

[0048] Reference now Figure 4 , a schematic diagram of a control architecture 400 for a plasma processing tool is shown according to one embodiment. As shown, the control architecture 400 may include a source power supply 410. Although a single source power supply is shown, it should be understood that additional source power supplies 410 may be included in other embodiments. In addition, the control architecture 400 may include one or more bias power supplies 4401-4402. N .Although Figure 4 4. A pair of bias power supplies 440 are shown, but it should be understood that any number of bias power supplies 440 (i.e., one or more) may be used according to an embodiment. In an embodiment, source power supply 410 may include process power controller 411. Similarly, bias power supply 440 may include process power controllers 4411-441 N .

[0049] In one embodiment, source power supply 410 may be electrically coupled to one or more bias / cathode devices A1-A1 in a plasma processing tool. N In one embodiment, the process power optimizer actuator 412 may be coupled between the source power supply 410 and the array 430. The actuator 412 may include actuation of a first capacitor and a second capacitor for impedance matching. In one embodiment, an input sensor S may be provided before the process power optimizer actuator 412. i 1 and an output sensor S may be provided after the process power optimizer actuator 412 o1 The bias power supply 440 may provide power to the array 430 via a bias power optimizer actuator 442 .

[0050] In one embodiment, the process power controller 415 can be communicatively coupled to the source power supply 410, the process power optimizer actuator, and the bias power supply 440. The process power controller can include a process power source optimizer 416. The process power source optimizer 416 can be coupled to the process power supply controller 411. The process power source optimizer 416 can provide power, voltage, and current data to the process power supply controller 411. In addition, a frequency actuation signal can be provided from the process power source optimizer 416 to the process power supply controller 411. A control signal can also be transmitted from the process power source optimizer 416 to the process power optimizer actuator 412 to adjust the impedance. In one embodiment, the process power source optimizer 416 receives the impedance data from the sensor S i 1 , S o 1 , S o 2 and S o n The sensor data is received to inform control signals supplied to the process power supply controller 411 and the process power optimizer actuator 412. In other words, the process power supply optimizer may be referred to as an impedance tuning algorithm.

[0051] In one embodiment, the process power controller 415 may further include a source predictor 417. The source predictor 417 may be used to predict control actuation in anticipation of changes in the load in the array 430. The source predictor 417 may be communicatively coupled to a process uniformity controller 418. The source predictor 417 may also receive a feed-forward signal from a process power bias optimizer 419. In other words, changes to the control loop for the bias power supply 440 may be fed-forward to the control loop for the source power supply 410. In one embodiment, the process uniformity controller 418 may also receive a signal from the sensor S i 1 , S o 1 , S o 2 and S o n The process uniformity controller 418 may deliver an actuation signal to a variable inductor that controls the input to the bias / cathode arrangement A1-A N The current in.

[0052] In one embodiment, the process power controller 415 may also include a bias control loop. In one embodiment, the bias control loop may include a process power bias optimizer 419. The process power bias optimizer 419 may provide a control signal to the bias power optimizer actuator 442 to control the impedance. The process power bias optimizer 419 may also obtain a bias control signal from the sensor S i 2 -S i N Feedback is received to inform control of the bias power optimizer actuator 442. The process power bias optimizer 419 also provides a feed-forward signal to the source predictor 417 as described above.

[0053] In one embodiment, the process power bias optimizer 419 also receives information from the bias predictor 420. The bias predictor 420 may be communicatively coupled with a process bias controller 421. The process bias controller 421 interfaces with a process power controller 441.

[0054] In one embodiment, the control architecture 400 may further include external connectors, such as an ENET link 422 and / or an ECAT link 423. The external connectors may be coupled to a tool / server controller 424 to provide tool process information (eg, temperature, pressure, etc.) to the control architecture 400.

[0055] In one embodiment, the source predictor 417 (and the bias predictor 420) may include means for calibrating the influence provided to the process power optimizer 416 (or the process power bias optimizer 419). In general, the predictors 417 and 420 need to know how to influence the optimizers 416 and 419. In one embodiment, the information used to calibrate the predictors 417 and 420 may be obtained from the process uniformity controller 418 and the process bias controller 421, respectively. The information received by the predictors 417 and 420 may inform the model of the system 400 and allow the information to be delivered to the optimizers 416 and 419 in the correct format.

[0056] Reference now Figure 5A , according to one embodiment, a diagram is shown that more clearly depicts the function of the predictor 517. Although referred to as the predictor 517, it should be understood that Figure 5A The predictor described in can be used for one or both of the source power and the bias power. In one embodiment, the predictor includes two different control paths, labeled A and B.

[0057] In one embodiment, control path A estimates continuous impact to minimize transistor response. As shown in the diagram below path A, the X command is a time iteration function and represents continuous control provided over the course of time to continuously adjust to changing device dynamic (disturbance and transient) parameters. In one embodiment, control path B estimates the response and behavior of the system to (digital twin) and includes optimized control performance using feedforward (ff) and feedback (fb) signals from the control architecture. As shown in the diagram below path B, the control of the X command starts with an iteration function and then continues with a dashed line to represent the control expected end target condition and draw an effective path to this control target end condition.

[0058] Reference now Figure 5B , according to one embodiment, a diagram showing data processing from a predictor 517 is shown. In one embodiment, the predictor 517 can be a bias predictor or a source predictor as described above. As shown, the predictor 517 can be communicatively coupled to the process power optimizer 516. In particular, the predictor can have two different input options to the process power optimizer 516: an A path; or a B path. The A path and the B path can be similar to those described above in Figure 5A In one embodiment, the predictor 517 may also be provided with a connection to the process controller 518. For example, a B path may be provided between the predictor 517 and the process controller 518. The predictor 517 may also receive process information from the ECAT / ENET links 522 and 523.

[0059] Reference now Figure 6 , a process control architecture 600 is shown according to an additional embodiment. In one embodiment, the architecture 600 may include a first loop and a second loop. The first control loop may be provided in a process power optimizer 616, and the second control loop may be provided below the process power optimizer. The two control loops may be coherently coupled together via a predictor 617. In one embodiment, the process power optimizer actuator (PPO A i ) feeds forward the signal from power 665 and is coupled with the signal from power 665. The power signal can be generated by sensor S i The combined signal is then added to the first branch extending from the top of the predictor 617. The first branch can be the A path, similar to the embodiment described in more detail above. After combining with the predictor signal, the first control loop proceeds to the PPO actuator to adjust the settings of the tool. The predictor 617 can be composed of a sensor S o 1 -S o Nand process actuation signal notification routed from PID 662 to process actuator 664 .

[0060] In the second control loop, the set point is provided to a profile 666. Profile 666 may be a set of process conditions (e.g., temperature, pressure, gas flow rate, etc.) currently implemented (or to be implemented) in the tool. Profile 666 may then have a signal added to the sensor feedback signal from sensor feedback block 663. Sensor feedback block 663 may be provided by sensor S o 1 -S o N notify.

[0061] In one embodiment, PID 662 may receive a signal from profile 666. The output of PID 662 may be combined with a signal from a feed forward (FF) distortion block 661. The signal from feed forward distortion block 661 may first be combined with a signal from predictor 617. The combined signal (i.e., predictor 617 signal and FF distortion 661 signal) may then be propagated to process actuator 664 to control impedance and / or power delivery in the system.

[0062] Reference now Figure 7A-7C , a process for performing a non-invasive way to measure circuit parameters within an RF matching system is described. The ability to non-invasively measure circuit parameters is useful in device manufacturing. In addition, during use, circuit parameters may drift. In order to properly calibrate the matching system, it is desirable to know which component or components have drifted, and the degree of drift.

[0063] Reference now Fig. 7A , according to one embodiment, a schematic diagram of a typical L-type network 760 for RF impedance matching is shown. In one embodiment, the matching system 761 is emphasized by the dotted box. The matching system includes inductors L1, L2 and L3, together with capacitors C1 and C2. Capacitors C1 and C2 can be variable capacitors. Voltage and current (VI) sensors 762 are installed at the input of the matching system 761. VI sensor 762 can provide an input impedance measurement value Zm. The load impedance of the network is given by R0+jX0. Therefore, by changing the capacitance of variable capacitors C1 and C2, the input impedance from the generator (Vs and Rs) to the load (Zm) can be controlled. Ideally, in order to better control the matching system 761, the exact values ​​of L1, L2 and L3 are known parameters.

[0064] Accordingly, an embodiment includes a process for determining L1, L2, and L3 in a non-invasive manner. The method begins with a first process flow for finding the inductance of L3. The first process flow may include sending a sinusoidal signal into a first variable capacitor. The method continues with a second process flow, which determines a process for calculating the values ​​of L1 and L2 using the known inductance of L3.

[0065] Reference now Figure 7B , according to one embodiment, a process flow diagram depicting a process 770 for determining the inductance of L3 is shown. As will be appreciated by one skilled in the art, Yin is the admittance of Zin. Since Zin is of the form Zm-jωL3, Yin will also have a real component (G1). G1 does not depend on C1, and this property can be used to determine L3.

[0066] In one embodiment, the process 770 may begin 771 with operation 772, which includes setting a load configuration and bringing the matching system to a tuned position. In one embodiment, any suitable tuning mechanism may be used to obtain the tuned position, such as some of those mechanisms described in more detail herein. In one embodiment, the process 770 may continue with operation 773, which includes injecting a low frequency sinusoidal signal into a first capacitor C1 of the matching system. In one embodiment, the sinusoidal signal has a frequency between 1 Hz and 100 Hz. In other embodiments, the sinusoidal signal has a frequency between 1 Hz and 10 Hz.

[0067] In one embodiment, process 770 may then continue with operation 774, which includes recording the Zm value with a VI sensor (or probe). In one embodiment, process 770 may then continue with operation 775, which includes starting from an initial L3 estimate. The L3 estimate (in conjunction with the Zm value) is then used to calculate the change in the G1 value. When the estimate of L3 is correct, the change in the G1 value will be minimized. That is, a series of L3 estimates may be used to detect the G1 minimum, as indicated by the loop from operations 776 and 777. For example, the L3 estimate may start from 0 and progress upward from 0. In other embodiments, the first estimate of L3 may be a previously known value of L3 or an expected value of L3. When the change in the G1 signal is minimized, the process continues to the Yes branch of operation 776, and the estimate of L3 at operation 778 is correct.

[0068] Reference now Figure 7C, according to one embodiment, a process flow diagram depicting a process 790 for determining values ​​of L1 and L2 is shown. In one embodiment, process 790 begins at 791 and continues with a first iteration (i=1) at box 792. In the first iteration, the process begins at operation 793, which includes setting the angular frequency ω to the value of ω1 from the angular frequency list. Once the angular frequency is set, the matching system is brought to a tuned position. In one embodiment, once the matching system is tuned, the process continues with operation 794, which includes recording matching system parameters. For example, matching system parameters may include values ​​such as ωi, Zin, R0, X0, C1, and C2. Matching system parameters may be known values. For example, R0 and X0 may be pre-selected values ​​or measured by additional sensors. C1 and C2 are variable capacitors that cause the tuned state. The value of Zin can be calculated based on the measurement results of Zm and the previously determined value of L3.

[0069] The process proceeds to decision block 795 which determines whether the end of the angular frequency list has been reached. If the answer is no, another iteration is performed where i is updated to i=i+1 as indicated by block 796. The next ωi+1 will then be used and the matching system will be retuned. By repeating the process, a system of equations can be developed to computationally solve for L1 and L2.

[0070] At the end of the angular frequency list, decision block 795 continues to operation 797. In one embodiment, operation 797 includes comparing the real part of the admittance and solving for L2. In some embodiments, a least mean square (LMS) process may be used to solve for L2, although other solution techniques may also be used.

[0071] The process can then proceed to operation 798, which includes comparing the imaginary part of the admittance and solving for L1. In one embodiment, the previously solved value for L2 can be used in conjunction with a solver process (e.g., LMS) to derive the value for L1, although other solving techniques can also be used. The process 790 can then end at block 799.

[0072] Accordingly, a process for determining circuit parameters (eg, L1 , L2 , and L3 ) within an RF matching system may be performed in a non-invasive manner.

[0073] Reference now Figure 8, a block diagram of an exemplary computer system 800 of a processing tool according to an embodiment is illustrated. In one embodiment, the computer system 800 is coupled to the processing tool and controls the processing in the processing tool. The computer system 800 can be connected (e.g., network connection) to other machines in a local area network (LAN), an intranet, an extranet, or the Internet. The computer system 800 can operate in the capacity of a server or a client machine in a client-server network environment, or can be used as a peer-to-peer machine in a peer-to-peer (or distributed) network environment. The computer system 800 can be a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a cellular phone, a web page device (web applicance), a server, a network router, a switch or a bridge, or any machine that can perform a set of instructions specifying that this machine will (sequentially or otherwise) take an action. In addition, although only a single machine is illustrated for the computer system 800, the term "machine" should also be considered to include a set of any machines (e.g., computers) that execute a set of (or multiple sets of) instructions to execute any one or more methods described herein individually or jointly.

[0074] The computer system 800 may include a computer program product or software 822, and the computer system 800 has a non-transitory machine-readable medium, and the non-transitory machine-readable medium has instructions stored on the non-transitory machine-readable medium, and the instructions can be used to program the computer system 800 (or other electronic devices) to perform a process according to an embodiment. Machine-readable media include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer). For example, machine-readable (e.g., computer-readable) media include machine-readable (e.g., computer) storage media (e.g., read-only memory ("ROM"), random access memory ("RAM"), disk storage media, optical storage media, flash memory devices, etc.), machine-readable (e.g., computer) transmission media (electrical, optical, acoustic or other forms of propagation signals (e.g., infrared signals, digital signals, etc.)), etc.

[0075] In an embodiment, the computer system 800 includes a system processor 802, a main memory 804 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM), such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM)), etc.), a static memory 806 (e.g., flash memory, static random access memory (SRAM), etc.), and an auxiliary memory 818 (e.g., a data storage device), which communicate with each other via a bus 830.

[0076] The system processor 802 represents one or more general-purpose processing devices, such as a microsystem processor, a central processing unit, or the like. More particularly, the system processor may be a complex instruction set computing (CISC) microsystem processor, a reduced instruction set computing (RISC) microsystem processor, a very long instruction word (VLIW) microsystem processor, a system processor implementing other instruction sets, or a system processor implementing a combination of instruction sets. The system processor 802 may also be one or more special-purpose processing devices, such as an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal system processor (DSP), a network system processor, or the like. The system processor 802 is configured to execute processing logic 826 to perform the operations described herein.

[0077] The computer system 800 may also include a system network interface device 808 for communicating with other devices or machines. The computer system 800 may also include a video display unit 810 (e.g., a liquid crystal display (LCD), a light emitting diode display (LED), or a cathode ray tube (CRT)), an alphanumeric input device 812 (e.g., a keyboard), a cursor control device 814 (e.g., a mouse), and a signal generating device 816 (e.g., a speaker).

[0078] The secondary memory 818 may include a machine-accessible storage medium 832 (or more specifically, a computer-readable storage medium) on which is stored one or more sets of instructions (e.g., software 822) embodying any one or more of the methodologies or functions described herein. The software 822 may also reside, in whole or in part, within the main memory 804 and / or within the system processor 802 during execution by the computer system 800, the main memory 804 and the system processor 802 also constituting machine-readable storage media. The software 822 may also be transmitted or received over the network 820 via the system network interface device 808. In one embodiment, the network interface device 808 may operate using RF coupling, optical coupling, acoustic coupling, or inductive coupling.

[0079] Although the machine-accessible storage medium 832 is shown as a single medium in the exemplary embodiment, the term "machine-readable storage medium" should be considered to include a single medium or multiple media (such as a centralized or distributed database, and / or associated caches and servers) that store one or more sets of instructions. The term "machine-readable storage medium" should also be considered to include any medium capable of storing or encoding a set of instructions that are to be executed by a machine and cause the machine to perform any one or more of the methods. Accordingly, the term "machine-readable storage medium" should be considered to include, but is not limited to, solid-state memories, and optical and magnetic media.

[0080] In the foregoing specification, specific exemplary embodiments have been described. It will be apparent that various modifications may be made to the embodiments without departing from the scope of the appended claims. The specification and drawings are accordingly to be regarded as illustrative rather than restrictive.

Claims

1. A process power controller for a plasma processing tool, comprising: Process power source optimizer; a source predictor; and A process uniformity controller is provided wherein the source predictor is communicatively coupled to the process power source optimizer and the process uniformity controller.

2. The process power controller of claim 1, wherein the source predictor estimates continuous impacts to minimize temporary responses.

3. The process power controller of claim 1, wherein the source predictor estimates system response and behavior to optimize control performance.

4. The process power controller of claim 3, wherein feedforward and feedback signals are used by the source predictor.

5. The process power controller of claim 1, wherein the process power source optimizer is communicatively coupled to a process power optimizer actuator.

6. The process power controller of claim 5, wherein the process power optimizer actuator comprises a variable reactance actuator.

7. The process power controller of claim 6, wherein the variable reactance actuator is a variable capacitor.

8. The process power controller of claim 1, further comprising a bias power control.

9. The process power controller of claim 8, wherein the bias power control further comprises a process power bias optimizer, a bias predictor, and a process bias controller.

10. The process power controller of claim 9, wherein the source predictor receives a feed-forward signal from the process power bias optimizer.

11. A method for controlling a plasma system, the method comprising the steps of: making a first parameter change in a first control loop; feeding forward the first parameter change to a second control loop; and A second parameter change is made in the second control loop to anticipate the change to the plasma system resulting from the first parameter change.

12. The method of claim 11, wherein the first parameter change is a change to a first variable reactance actuator, and wherein the second parameter change is a change to a second variable reactance actuator.

13. The method of claim 12, wherein the first parameter change is a change to a plasma density within the plasma system.

14. The method of claim 13, wherein the plasma density change is a change to a central plasma density and an edge plasma density.

15. The method of claim 13, wherein the second parameter changes impedance in an impedance matching network of the plasma system.

16. A method of controlling a plasma system, the method comprising the steps of: varying a first variable reactance actuator; feeding forward the change of the first variable reactance actuator to a matching controller; and In response to the change in the first variable reactance actuator, an actuation setting of one or both of a second variable reactance actuator and a third variable reactance actuator is changed using the matching controller.

17. The method of claim 16, wherein changing the first variable reactance actuator is a change to a plasma density within the plasma system.

18. The method of claim 17, wherein the plasma density change is a change to a central plasma density and an edge plasma density.

19. The method of claim 16, wherein the first variable reactance actuator is part of a first control loop, and wherein the second variable reactance actuator and the third variable reactance actuator are part of a second control loop.

20. The method of claim 16, wherein changing the second variable reactance actuator and the third variable reactance actuator changes impedance in an impedance matching network of the plasma system.

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