Control and Prediction of Multiple Plasma Bonding Surfaces and Corresponding Power Transmission

Abstract

The embodiments disclosed in this specification include a processing power controller for a plasma processing tool. In one embodiment, the processing power controller includes a processing power source optimizer, a source predictor, and a processing uniformity controller. In one embodiment, the source predictor is communicatively coupled to the processing power source optimizer and the processing uniformity controller.

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims priority to U.S. Patent Application No. 17 / 737,670, filed on May 5, 2022, the entire content of which is incorporated herein by reference.

[0002] Embodiments relate to the field of semiconductor manufacturing, and more particularly, to an adaptive control architecture for improving impedance matching.

Background Art

[0003] In a plasma processing tool, plasma is ignited by a cathode coupled to a process gas in a chamber. In most tools, the power supply is coupled to the cathode via an impedance matching network (sometimes simply referred to as a "matcher"). The matcher makes it possible to adjust the impedance of the system in order to vary the impedance of the load to which the cathode is coupled. The impedance matching network load has a wide range of impedances defined by parameters such as process conditions and chamber structure. Impedance matching is important for efficient power transfer from the power supply to the load and is achieved by adjusting the impedance of the impedance matching network to be equivalent to the transmission line impedance.

[0004] In some plasma systems, multiple antennas are used to control the plasma density at various positions within the plasma chamber. For example, an inner antenna and an outer antenna can control the plasma density at the center and the edge of the wafer, respectively. Changes in the plasma density change the impedance of the system. Therefore, it is necessary to adjust the matcher to match the new load impedance of the system. That is, the impedance changes and the matcher is changed after the impedance changes. In such a configuration, since impedance control follows the changes in the system, it becomes difficult to quickly and accurately match the impedance changes of the system. SUMMARY OF THE INVENTION

[0005] Embodiments disclosed herein include a processing power controller for a plasma processing tool. In one embodiment, the processing power controller includes a processing power source optimizer, a source predictor, and a processing uniformity controller. In one embodiment, the source predictor is communicatively coupled to the processing power source optimizer and the processing uniformity controller.

[0006] Embodiments may further include a method for controlling a plasma system. In one embodiment, the method includes changing a first parameter in a first control loop, feeding forward the change of the first parameter to a second control loop, and changing a second parameter in the second control loop to anticipate the change of the plasma system caused by the change of the first parameter.

[0007] Embodiments may further include a method for controlling a plasma system. In one embodiment, the method includes changing a first variable reactance actuator, feed-forwarding the change of the first variable reactance actuator to a matching controller, and using the matching controller to change the operating settings of one or both of a second variable reactance actuator and a third variable reactance actuator in response to the change of the first variable reactance actuator.

Brief Description of the Drawings

[0008]

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[0009] The systems described herein include an adaptive control architecture with supported models to improve process power control and related plasma control at multiple sets of power coupling surfaces that affect plasma density and sheath. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments. It will be apparent to one of ordinary skill in the art that the embodiments may be practiced without these specific details. In other instances, well-known aspects are not described in detail so as not to obscure the embodiments needlessly. Further, it should be understood that the various embodiments shown in the accompanying figures are illustrative representations and are not necessarily drawn to scale.

[0010] As described above, there is no cohesion in the control of plasma parameters in a plasma system. When a change is applied to one aspect of the plasma within the system, other aspects of the plasma may change. For example, a change in plasma density can result in a change in the impedance of the load. Since the impedance changes, the configuration of the matcher must be changed to match the new impedance. However, the embodiments disclosed herein include a feed-forward control system between control loops to achieve centralized and cohesive control of the plasma processing system.

[0011] The power coupled to the plasma source is generated by (1) multiple injection sites and (2) multiple, possibly distributed, antennas. Due to the electromagnetic interaction in these mechanisms, the power coupling from one site or antenna may affect the power transmission at a second site or antenna. Thus, in the embodiments disclosed herein, the control of power coupling is used to predict changes in power coupling and minimize disturbances and instabilities that can affect the performance and yield of the device.

[0012] In one embodiment, centralized control enables predictive and adaptive control between plasma power coupling and power transmission. In a generalized approach, impedance tuning prediction (power transmission) affected by the absorbed power characteristics is accompanied by disturbance minimization, and it has been demonstrated that plasma power coupling is controlled by the configuration of the first processing power plasma coupling point (e.g., the inner one) and the configuration of the second processing power plasma coupling point (e.g., the outer one).

[0013] Generally, the embodiments described herein include two control loops coupled together by a feed-forward link. The first control loop varies the processing power flowing through a pair of injection sites (one for the inner plasma and the other for the outer plasma). When the first control loop varies local plasma parameters, such as density, the impedance of the system changes. To enable optimal power transmission (i.e., minimized reflected power), the change in the first control loop is feed-forwarded to a second aspect of power coupling, e.g., a second control loop that controls impedance matching. Thus, the second control loop can be adjusted to maximize power supply (predicting changes in impedance).

[0014] More specifically, the first control loop includes a third variable capacitor, and the second control loop includes a first variable capacitor and a second variable capacitor. The change in the third variable capacitor is made to vary the plasma density within the plasma system. In one embodiment, the change in the third variable capacitor is feed-forwarded to the second control loop to make changes to the first variable capacitor and / or the second variable capacitor in anticipation of impedance changes.

[0015] Generally, a plasma processing tool includes one or more power supplies. The power supplies are connected to a cathode (or antenna) in the chamber via an impedance matching network. Processing power sensors (e.g., voltage and current sensors (V / I sensors)) are provided upstream and downstream of the impedance matching network. The sensors assist in controlling the power supply to the cathode.

[0016] In one embodiment, the first control loop and the second control loop may be implemented within the impedance matching network. The impedance matching network may include a circuit 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.

[0017] Referring now to FIG. 1, a more detailed schematic diagram of a plasma processing tool 100 according to an embodiment is shown. In one embodiment, the plasma processing tool 100 includes a plasma chamber 120. The plasma chamber 120 includes a cathode 122 for coupling the received power to one or more gases flowing into the plasma chamber 120. In one embodiment, the plasma chamber 120 may be suitable for any plasma processing typical in a semiconductor manufacturing environment. For example, the plasma chamber 120 may be a plasma etching chamber, a plasma deposition chamber, a plasma processing chamber, etc. In a particular embodiment, the plasma chamber 120 may be a plasma enhanced chemical vapor deposition (PECVD) chamber, a physical vapor deposition (PVD) chamber, or a plasma enhanced atomic layer deposition (PEALD) chamber.

[0018] In one embodiment, the plasma chamber 120 may be connected to a power supply architecture. For example, the power supply architecture may include one or more power supplies 132 1 -132 nIt may include. In the illustrated embodiment, a plurality of 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, etc., or any combination of these power supply types.

[0019] In one embodiment, the power supply 132 may be connected to the cathode 122 via the processing power source optimizer actuator 130. The processing power source optimizer actuator 130 may also be referred to as an impedance matching network. The processing power source optimizer actuator 130 changes the impedance of the power supply architecture to match the load within the chamber 120. Due to changes in processing conditions (such as gas flow rate, pressure, temperature, etc.), the impedance of the load may vary. Therefore, the processing power source optimizer actuator 130 is used to match the varying impedance in order to provide an efficient and optimal (i.e., a state with no or minimal reflected power) power supply within the chamber. In one embodiment, the circuit for impedance matching may include a first variable reactance actuator and a second variable reactance actuator. For example, the variable reactance actuator may be a variable capacitor in some embodiments.

[0020] The processing power source optimizer actuator 130 may further include a circuit for controlling the plasma density within the plasma chamber 120. For example, a third variable reactance actuator can control the processing power through the first of two outputs. The power from each output couples to the plasma and determines the plasma density. In one embodiment, a circuit having a first variable reactance actuator, a second variable reactance actuator, and a 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 leap from the first variable reactance actuator and the second variable reactance actuator.

[0021] In one embodiment, sensors 151 and 152 may be provided on both sides of the impedance matching network 130. For example, sensor 151 1 -151 n may be upstream of the impedance matching network 130, and sensor 152 may be downstream 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 151 1 -151 n are provided upstream of the impedance matching network 130. The number of sensors 151 may be equal to the number of power supplies 132. That is, each power supply 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 additional sensors 152 may exist if there is more than one output from the matching network 130. For example, if there are two outputs (e.g., for the center of chamber 120 and the end of chamber 120), there may be two sensors 152.

[0022] When there are multiple sensors 151, the multiple sensors 151 1-nIt can be manufactured on a single PCB. That is, a single module can include multiple sensors. Generally, the embodiments described herein include electrical shielding techniques that limit the mutual coupling between sensors on a single PCB.

[0023] In FIG. 1, sensors 151 and 152 are generally shown as blocks. 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 152 can be a voltage and current (i.e., V / I) sensor. The voltage can be detected by an embedded voltage ring, and the current can be detected by a current loop. The sensor can have an aperture through which a cable (e.g., an RF cable) passes.

[0024] In one embodiment, sensors 151 and 152 can be communicatively coupled to a processing module 134. As shown in the figure, in FIG. 1, a processing power control module 134 is provided. However, it should be understood that the processing module can be a microwave processing module 134, a DC processing module, an RF processing module, etc., depending on the type of power supply 132 included in tool 100. In one embodiment, sensors 151 and 152 supply voltage and / or current to the processing power control module 134. The processing power control module 134 can also implement the control of a first control loop and a second control loop used to change the impedance within the impedance matching network 130 and / or the plasma density within the chamber 120. In one embodiment, the processing module 134 can have external connections for various physical layers and protocols such as standard industrial connections like Ethernet (ENET) and EtherCAT.

[0025] In one embodiment, the processing module 134 can be connected to an impedance matching network. The processing module 134 may be capable of transmitting a control signal to the impedance matching network 130. For example, the control signal may be used to adjust a variable reactance actuator within the impedance matching network 130. Further, the processing module 134 can be connected to the power supply 132. Thus, the processing module 134 enables cooperative impedance adjustment for optimal power transmission.

[0026] It should be understood that a change in the first control loop results in a change in impedance. Therefore, a change in the first control loop requires a change in the second control loop. In a conventional system, when the first control loop is changed, the second control loop is changed with a time delay to account for the new impedance. That is, impedance control lags behind system changes. Therefore, the dynamic control response of impedance matching is suboptimal.

[0027] Accordingly, the embodiments disclosed herein include a feed-forward architecture between a first control loop and a second control loop. A change in the first control loop is feed-forwarded to the second control loop. In this way, the second control loop can make changes anticipating changes in the impedance of the system. Thus, the second control loop no longer lags behind the first control loop, improving power supply performance.

[0028] Referring now to FIG. 2, a process flow diagram for a process 270 for controlling a semiconductor processing tool is shown. In one embodiment, the process 270 can be used to connect two or more control loops to improve system control. For example, the process 270 can be used to improve the power supply efficiency to a plasma processing tool as described in more detail above.

[0029] In one embodiment, process 270 may start at operation 271. Operation 271 includes changing a first parameter in a first control loop. In one embodiment, the change of the first parameter can be a change of any parameter within the plasma processing tool. For example, the change of the first parameter can be a change of the plasma density in one or more regions within the plasma processing tool. In a particular embodiment, the change of the first parameter can be a change of a variable reactance actuator within an impedance matching network. The variable capacitor can control the current flow of two or more inductors that define the plasma density in one or more regions of the plasma chamber.

[0030] In one embodiment, process 270 may subsequently proceed to operation 272. Operation 272 includes feed-forwarding the change of the first parameter to a second control loop. In one embodiment, the feed-forward operation can occur before the change of the first parameter occurs. In other embodiments, the feed-forward operation can occur substantially simultaneously with the occurrence of the change of the first parameter. In one embodiment, the second control loop may include a pair of variable capacitors. The second control loop can play a role in setting the impedance of the impedance matching network. In particular, the change of the first parameter can result in a change in the impedance of the load. Therefore, the pair of variable capacitors may need to change their capacitance to match the new impedance of the system. Since the feed-forward signal is sent to the second control loop before or simultaneously with the change of the first parameter, the second control loop can predict the change in the impedance of the system, and the control of the system is improved.

[0031] In one embodiment, process 270 may subsequently proceed to operation 273. Operation 273 includes changing a second parameter in a second control loop to anticipate a system change caused by a change in the first parameter. In particular, the second parameter may be a mechanical change of 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. Therefore, in order to improve the control of the system, a rapid change can be applied to the second control loop.

[0032] Referring now to FIG. 3, it is a flowchart of a process 380 for controlling an impedance matching network within a plasma tool according to an embodiment. In one embodiment, 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 within the impedance matching network, and the second control loop may be used to change the impedance of the impedance matching network.

[0033] In one embodiment, process 380 may start at operation 381. Operation 381 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, the first inductor may be responsible for generating plasma at the center of the chamber, and the second inductor may be responsible for generating plasma at the end of the chamber. Therefore, changing the capacitance of the third capacitor can be used to change the plasma density within the chamber at the center and end of the chamber.

[0034] In one embodiment, process 380 may start with operation 382. Operation 382 includes feeding forward a change in the capacitance of a third capacitor to a matcher controller. In one embodiment, the matcher controller may be responsible for adjusting the capacitance of a variable capacitor within an impedance matching network. For example, the matcher 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.

[0035] In one embodiment, the feed-forward operation may occur when the change in the third capacitor is being implemented. In other embodiments, the feed-forward operation may occur before the change in the third capacitor is implemented. Therefore, the change in the impedance of the matching network can anticipate the change in the impedance of the system. Thus, the control of impedance matching is improved.

[0036] In one embodiment, process 380 may then proceed to operation 383. Operation 383 includes using the matcher 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 matcher 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 and second variable capacitors.

[0037] Referring now to FIG. 4, a schematic diagram of a control architecture 400 within a plasma processing tool according to an embodiment is shown. As illustrated, the control architecture 400 may include a source power supply 410. Although a single source power supply is shown, it should be understood that in other embodiments, additional source power supplies 410 may be included. Further, the control architecture 400 may include one or more bias power supplies 440 1 -440 N . As shown in FIG. 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 bias power supplies) may be used according to an embodiment. In one embodiment, the source power supply 410 may include a process power supply controller 411. Similarly, the bias power supply 440 may include a process power supply controller 441 1 -441 N .

[0038] In one embodiment, the source power supply 410 may be electrically coupled to an array 430 of one or more bias / cathode devices A 1 -A N within the plasma processing tool. In one embodiment, a process power source optimizer actuator 412 may be coupled between the source power supply 410 and the array 430. The actuator 412 may include the operation of a first capacitor and a second capacitor used to enable impedance matching. In one embodiment, an input sensor S i 1 is provided in front of the process power source optimizer actuator 412, and an output sensor S o 1 may be provided after the process power source optimizer actuator 412. The bias power supply 440 may supply power to the array 430 through a bias power optimizer actuator 442

[0039] In one embodiment, the processing power controller 415 may be communicatively coupled to the source power supply 410, the processing power source optimizer actuator, and the bias power supply 440. The processing power controller may include a processing power source optimizer 416. The processing power source optimizer 416 may be coupled to the processing power supply controller 411. The processing power source optimizer 416 may supply data on power, voltage, and current to the processing power supply controller 411. Further, a frequency operating signal may be supplied from the processing power source optimizer 416 to the processing power supply controller 411. To adjust the impedance, a control signal may be further supplied from the processing power source optimizer 416 to the processing power source optimizer actuator 412. In one embodiment, the processing power source optimizer 416 uses sensors S i 1 , S o 1 , S o 2 , and S o n to receive sensor data from. That is, the processing power source optimizer may be referred to as an impedance tuning algorithm.

[0040] In one embodiment, the processing power controller 415 may further include a source predictor 417. Using the source predictor 417, control actions can be predicted in order to anticipate changes in the loads within the array 430. The source predictor 417 may be communicatively coupled to the processing uniformity controller 418. The source predictor 417 may further receive a feed-forward signal from the processing power bias optimizer 419. That is, changes to the control loop for the bias power supply 440 may be feed-forwarded to the control loop for the source power supply 410. In one embodiment, the processing uniformity controller 418 uses sensors S i 1 , S o 1 , S o 2 , and S o ncan further receive an input from. The processing uniformity controller 418 can supply an operating signal to a variable inductor that controls the current to the bias / cathode device A 1 -A N In one embodiment, the processing power controller 415 can further include a bias control loop. In one embodiment, the bias control loop can include a processing power bias optimizer 419. To control the impedance, the processing power bias optimizer 419 can supply a control signal to a bias power optimizer actuator 442. The processing power bias optimizer 419 can further receive feedback from a sensor S

[0041] i 2 -S i N As described above, the processing power bias optimizer 419 can further supply a feedforward signal to a source predictor 417.

[0042]

[0043]

[0044] In one embodiment, the processing power bias optimizer 419 can further receive information from a bias predictor 420. The bias predictor 420 can be communicably coupled to a processing bias controller 421. The processing bias controller 421 interfaces with a processing power supply controller 441. In one embodiment, the control architecture 400 can further include an external connector such as an ENET link 422 and / or an ECAT link 423. The external connector can be coupled to a tool / server controller 424 to provide tool processing information (e.g., temperature, pressure, etc.) to the control architecture 400.​In one embodiment, the source predictor 417 (and the bias predictor 420) may include means for calibrating the influence exerted on the processing power source optimizer 416 (or the processing power bias optimizer 419). Generally, the predictors 417 and 420 must know how they are influencing the optimizers 416 and 419. In one embodiment, the information for calibrating the predictors 417 and 420 can be obtained from the processing uniformity controller 418 and the processing bias controller 421, respectively. The information received by the predictors 417 and 420 can inform the model of the system 400 and enable the information supplied to the optimizers 416 and 419 to be in the correct format.

[0045] Referring now to FIG. 5A, a diagram is shown that more clearly illustrates the functionality of the predictor 517 according to an embodiment. Although referred to as the predictor 517, it should be understood that the predictor depicted in FIG. 5A may 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.

[0046] In one embodiment, control path A estimates a continuous influence to minimize the transient response. As shown in the graph below control path A, the X command is a time-repeating function, indicating that continuous control is executed over time to continuously adapt to changing plant dynamics (perturbation and transient) parameters. In one embodiment, control path B estimates the response and operation of the system to the (digital twin) and includes optimized control performance using feed-forward (ff) and feedback (ffb) signals from the control architecture. As shown in the graph below control path B, the control of the X command starts as an iterative function and continues with a dashed line, representing that the control anticipates the end target condition and plots an efficient path to the end condition of that control objective.

[0047] Referring now to FIG. 5B, there is shown a diagram illustrating data processing of predictor 517 according to an embodiment. In one embodiment, predictor 517 can be the bias predictor or the source predictor described above. As shown, predictor 517 can be communicatively coupled to processing power source optimizer 516. In particular, the predictor can have two different input options (path A or path B) to processing power source optimizer 516. Path A and path B can be similar to the embodiments described above with respect to FIG. 5A. In one embodiment, predictor 517 can also be provided with a connection to processing controller 518. For example, path B can be provided between predictor 517 and processing controller 518. Predictor 517 can further receive processing information from ECAT / ENET links 522 and 523.

[0048] Referring now to FIG. 6, there is shown a processing control architecture 600 according to an additional embodiment. In one embodiment, architecture 600 can include a first loop and a second loop. The first control loop can be provided within processing power optimizer 616, and the second control loop can be provided below the processing power optimizer. The two control loops can be coherently coupled via predictor 617. In one embodiment, the processing power optimizer actuator (PPO A i ) is feedforwarded and coupled to a signal from power 665. The power signal can be notified by sensor S i . The combined signal is added to a first branch extending from the top of predictor 617. The first branch can be path A, similar to the embodiments 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. Predictor 617 can be notified by sensor S o 1 -S o N , and a processing actuation signal routed from PID 662 to processing actuator 664.

[0049] In the second control loop, the setpoint is supplied to Profile 666. Profile 666 can be a set of processing conditions (e.g., temperature, pressure, gas flow rate, etc.) currently implemented (or to be implemented shortly) in the tool. Profile 666 can then have a signal added to the sensor feedback signal from Sensor Feedback Block 663. Sensor Feedback Block 663 can be notified from Sensor S o 1 -S o N as may be.

[0050] In one embodiment, PID 662 can receive a signal from Profile 666. The output of PID 662 can be combined with a signal from Feed Forward (FF) Distortion Block 661. The signal from Feed Forward Distortion Block 661 can first be combined with a signal from Predictor 617. The combined signal (i.e., the Predictor 617 signal and the FF Distortion 661 signal) can then be propagated to Processing Actuator 664 to control the impedance and / or power supply within the system.

[0051] Referring now to FIGS. 7A - 7C, a process for performing a non-invasive approach for measuring circuit parameters within an RF matching system is described. The ability to non-invasively measure circuit parameters is useful in device manufacturing. Further, during use, the circuit parameters may drift. To properly calibrate the matching system, it is desirable to know which one or more components have drifted and by how much.

[0052] Referring now to FIG. 7A, a schematic diagram of a typical L-type network 760 for RF impedance matching according to an embodiment is shown. In one embodiment, a matching system 761 is highlighted by the dashed box. The matching system includes inductors L1, L2, and L3 along capacitors C1 and C2. Capacitors C1 and C2 may be variable capacitors. A voltage and current (VI) sensor 762 is attached to the input of the matching system 761. The 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 capacitances of the variable capacitors C1 and C2, the input impedance seen from the generator (Vs and Rs) towards the load (Zm) can be controlled. Ideally, for better control of the matching system 761, the exact values of L1, L2, and L3 are known parameters.

[0053] Accordingly, the embodiment includes a process for determining L1, L2, and L3 in a non-invasive approach. This approach begins with a first process flow for determining the inductance of L3. The first process flow may include transmitting a sine wave signal to a first variable capacitor. This approach continues with a second process flow that defines a process for calculating the values of L1 and L2 using the known inductance of L3.

[0054] Referring now to FIG. 7B, a process flow diagram showing a process 770 for determining the inductance of L3 is shown. As would be understood by one of ordinary skill in the art, Yin is the admittance of Zin. Since Zin is in the form of Zm - jωL3, Yin will also have a real component (G1). G1 is not dependent on C1, and this property can be utilized to determine L3.

[0055] In one embodiment, process 770 may start (771) at operation 772. Operation 772 includes setting a load configuration and moving the alignment system to a tuned position. In one embodiment, the tuned position can be obtained using any suitable tuning mechanism (e.g., some of the mechanisms described in more detail herein). In one embodiment, process 770 may start at operation 773. Operation 773 includes injecting a low-frequency sine wave signal into the first capacitor C1 of the alignment system. In one embodiment, the sine wave signal has a frequency between 1 Hz and 100 Hz. In other embodiments, the sine wave signal has a frequency between 1 Hz and 10 Hz.

[0056] In one embodiment, process 770 may then proceed to operation 774. Operation 774 includes recording the Zm value with a VI sensor (or probe). In one embodiment, process 770 may then proceed to operation 775. Operation 775 includes starting with an initial L3 estimate value. Then, the variation of the value of G1 is calculated using the L3 estimate value (in combination with the Zm value). If the estimate value of L3 is correct, the variation of the value of G1 is minimized. That is, as shown by the loop from operations 776 and 777, a series of L3 estimate values may be used to detect the minimum value of G1. For example, the estimate value of L3 may start from 0 and may increase from 0. In other embodiments, the first estimate value of L3 may be a known value of L3 or an expected value of L3. When the variation of the G1 signal is minimized, the process proceeds to the Yes branch of operation 776, and it is assumed that the estimate of L3 is correct in operation 778.

[0057] Referring now to FIG. 7C, a process flow diagram is shown that illustrates a process 790 for determining the values of L1 and L2 according to an embodiment. In one embodiment, process 790 begins at 791 and is continued by a first iteration (i = 1) at box 792. In the first iteration, the process begins with operation 793. Operation 793 includes setting the angular frequency ω to the value ω1 from a list of angular frequencies. Once the angular frequency is set, the matching system is brought to the tuned position. In one embodiment, once the matching system is tuned, the process may then proceed to operation 794. Operation 794 includes recording the matching system parameters. For example, the matching system parameters may include values such as ωi, Zin, R0, X0, C1, C2. The matching system parameters may be known values. For example, R0 and X0 may be preselected values or values measured by additional sensors. C1 and C2 are variable capacitances that result in a tuned state. The value of Zin can be calculated from the measured value of Zm and the pre-determined value of L3.

[0058] The process proceeds to decision block 795. In decision block 795, it is determined whether the end of the angular frequency list has been reached. If the answer is no, as shown in block 796, i is updated to i = i + 1 and another iteration is executed. Then, the next ωi+1 is used and the matching system is tuned again. By repeating the process, a system of simultaneous equations for computationally solving for L1 and L2 can be obtained.

[0059] At the end of the angular frequency list, decision block 795 proceeds to operation 797. In one embodiment, operation 797 includes comparing the real part of the admittance and determining the value of L2. In some embodiments, a least mean squared (LMS) process may be utilized to determine the value of L2, although other solution techniques may also be used.

[0060] The process can then proceed to operation 798. Operation 798 includes comparing the imaginary part of the admittance and determining the value of L1. In one embodiment, to derive the value of L1, the previously determined value for L2 can be used in combination with a solution process (e.g., LMS), although other solution techniques can also be used. The process 790 can then end at block 799.

[0061] Accordingly, the process for determining circuit parameters (e.g., L1, L2, and L3) inside the RF matching system can be implemented in a non-invasive manner.

[0062] Referring now to FIG. 8, a block diagram of an exemplary computer system 800 of a processing tool is shown in accordance with an embodiment. In one embodiment, the computer system 800 is coupled to the processing tool and controls the processing within the processing tool. The computer system 800 can be connected (e.g., networked) to other machines in a local area network (LAN), intranet, extranet, or the Internet. The computer system 800 can operate in the role of a server or client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The computer system 800 can be any machine capable of executing a set of (sequential or otherwise) instructions to specify actions to be taken by that machine, such as a personal computer (PC), tablet PC, set-top box (STB), personal digital assistant (PDA), cellular phone, web appliance, server, network router, switch or bridge, or the like. Further, although only a single machine is shown as the computer system 800, the term "machine" should be further interpreted to include any collection of machines (e.g., computers) that individually or jointly execute a set of (or multiple sets of) instructions to perform any one or more of the methods described herein.

[0063] The computer system 800 may include a computer program product or software 822 having a non-transitory machine-readable medium storing instructions that may be used to program a computer system 800 (or other electronic device) to perform the processing according to the embodiments. The machine-readable medium includes any mechanism for storing or transmitting information in a form readable by a machine, e.g., a computer. For example, the machine-readable (e.g., computer-readable) medium includes machine-readable storage media (e.g., read-only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, etc.), machine-readable transmission media (in the form of electrical, optical, acoustic, or other propagated signals, e.g., infrared signals, digital signals, etc.), and the like.

[0064] In one embodiment, the computer system 800 includes a system processor 802, a main memory 804 (e.g., dynamic random access memory (DRAM) such as read-only memory (ROM), flash memory, synchronous DRAM (SDRAM), or Rambus DRAM (RDRAM)), a static memory 806 (e.g., flash memory, static random access memory (SRAM), etc.), and a secondary memory 818 (e.g., a data storage device), which communicate with each other via a bus 830.

[0065] The system processor 802 represents one or more general-purpose processing devices such as a micro-system processor or a central processing unit. More specifically, the system processor can be a complex instruction set computing (CISC) micro-system processor, a reduced instruction set computing (RISC) micro-system processor, a very long instruction word (VLIW) micro-system processor, a system processor that executes other instruction sets, or a system processor that executes a combination of instruction sets. The system processor 802 may 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, etc. The system processor 802 is configured to execute processing logic 826 for performing the operations described herein.

[0066] The computer system 800 may further include a system network interface device 808 for communicating with other devices or machines. The computer system 800 may further 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 generation device 816 (e.g., a speaker).

[0067] The secondary memory 818 may include a machine-accessible storage medium 832 (or, more specifically, a computer-readable storage medium) in which one or more sets of instructions (e.g., software 822) that embody any one or more of the methods or functions described herein are stored. The software 822 may also reside, in whole or at least in part, within the main memory 804 and / or the system processor 802 while being executed by the computer system 800, and the main memory 804 and the system processor 802 may also constitute a machine-readable storage medium. The software 822 may be further transmitted and 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.

[0068] In the exemplary embodiments, the machine-accessible storage medium 832 is shown as a single medium, but the term "machine-readable storage medium" should be understood to include a single medium or multiple media (e.g., a centralized database or a distributed database, and / or associated caches and servers) that store one or more sets of instructions. Further, the term "machine-readable storage medium" should be interpreted to include any medium that is capable of storing or encoding a set of instructions executable by a machine and that causes a machine to execute any one or more of the methods. Thus, the term "machine-readable storage medium" should be interpreted to include, but not be limited to, solid-state memory, optical media, and magnetic media.

[0069] In the foregoing specification, specific exemplary embodiments have been described. It will be apparent that various modifications may be made to the specific exemplary embodiments without departing from the scope of the following claims. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a limiting sense.

Claims

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

2. The processing power controller according to claim 1, wherein the source predictor estimates a continuous influence to minimize a transient response.

3. The processing power controller according to claim 1, wherein the source predictor estimates the response and behavior of the system for optimized control performance.

4. The processing power controller according to claim 3, wherein a feedforward signal and a feedback signal are used by the source predictor.

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

6. The processing power controller according to claim 5, wherein the processing power source optimizer actuator comprises a variable reactance actuator.

7. The processing power controller according to claim 6, wherein the variable reactance actuator is a variable capacitor.

8. The processing power controller according to claim 1, further comprising bias power control.

9. The processing power controller according to claim 8, wherein the bias power control further comprises a processing power bias optimizer, a bias predictor, and a processing bias controller.

10. The processing power controller according to claim 9, wherein the source predictor receives a feedforward signal from the processing power bias optimizer.

11. A method for controlling a plasma system, comprising: changing a first parameter in a first control loop; feeding forward the change of the first parameter to a second control loop; changing a second parameter in the second control loop to anticipate the change of the plasma system caused by the change of the first parameter A method comprising.

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

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

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

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

16. A method for controlling a plasma system, comprising: changing a first variable reactance actuator; feeding forward the change of the first variable reactance actuator to a matcher controller; in response to the change of the first variable reactance actuator, using the matcher controller to change the operating settings of one or both of a second variable reactance actuator and a third variable reactance actuator; The method includes.

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

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

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

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

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