KR20170103660A - Systems and methods for tuning an impedance matching network in a step-wise fashion - Google Patents

Abstract

Described are systems for tuning an impedance matching network in a step-wise type and methods thereof. An impedance matching network is tuned in a step-wise type instead of immediately achieving optimum values of radio frequencies (RF) and coupled variable capacitance, thereby realizing processing of a wafer using tuned optimum values.

Description

[0001] SYSTEMS AND METHODS FOR TUNING IMPEDANCE MATCHING NETWORKS IN A STEP-BY-STEP METHOD [0002] FIELD OF THE INVENTION [0003]

The embodiments are directed to systems and methods for tuning an impedance matching network in a step-wise manner.

Plasma systems are used to control plasma processes. The plasma system includes a plurality of RF (radio frequency) sources, an impedance matching unit, and a plasma reactor. The workpiece is disposed within the plasma chamber and the plasma is generated within the plasma chamber to process the workpiece. It is important that the workpieces are processed in a similar or uniform manner. It is important that the RF sources and the impedance matching portion are tuned to process the workpiece in a similar or uniform manner.

The embodiments described in this disclosure occur in this context.

Embodiments of the present disclosure provide apparatus, methods, and computer programs for tuning an impedance matching network in a step-wise fashion. It is to be understood that the embodiments may be implemented in numerous ways, e.g., in a process, apparatus, system, piece of hardware, or on a computer-readable medium. Some embodiments are described below.

The plasma tool has an RF matched network tune algorithm. The plasma tool has one or two RF generators, and each of the RF generators is connected to a 50 Ω coaxial RF cable. The RF cables are connected to an impedance matching network that is connected to the plasma chamber via an RF transmission line. The RF generators are designed to operate using 50 + 0 jΩ, or nearly 50 + 0 jΩ, load impedances. One purpose of the impedance matching network is to convert the load impedance of the plasma chamber and the RF transmission line to 50 + 0 jΩ or nearly 50 + 0 jΩ, rather than about 50 + 0 jΩ. The target impedance of 50 + 0 jΩ or nearly 50 + 0 jΩ has a real part that should be two parts, 50 Ω or nearly 50 Ω, and an imaginary part that should be 0 Ω or nearly 0 Ω. Therefore, the branch circuit of the impedance matching network connected to one RF generator of two RF generators has two variable elements. The two variable elements include a variable RF frequency output from one RF generator of the RF generators and one motor-driven variable capacitance.

The variable capacitance is preset in the recipe and does not vary in the recipe step. The variable capacitance is changed by modifying the recipe. The variable RF frequency is controlled internally by process execution within the RF generator. The process operates according to the voltage reflection coefficient. If the reflection coefficient is higher than the threshold value, the process increases or decreases the RF frequency and, in this manner, changes the RF frequency in one direction or another direction based on the reflection coefficient. The sensor in the RF generator detects the reflected voltage using a narrow band filter and detects some of the reflected voltage at the fundamental frequency, but there can be large reflected wave amplitudes at the intermodulation frequencies, which are not detected. When the following inputs are provided to the matching network model as inputs, e.g., values of RF power, variable capacitance and variable RF frequency, and measured values of the RF load impedance at the output of the RF generator, etc., The matching network model for the matching network is used to predict the RF voltage, the current, and the phase between the RF voltage and current or the load impedance at the output of the impedance matching network. The matching network model is extended to predict the RF voltage and current between the output of the impedance matching network and the chuck. In various embodiments, the matching network model includes a series of modules all having the same form, as described in the patent application with application number 14 / 245,803.

In some embodiments, the load impedance at the output of the RF generator is propagated in a forward direction through the matching network model to calculate the load impedance at the output of the matching network model from the variable capacitance and variable RF frequency, Is then propagated in the reverse direction to determine optimal values for the variable capacitance and the variable RF frequency. When determining optimal values, the RF generator and impedance matching network are tuned to achieve optimal values of variable capacitance and RF variable frequency. The variable RF frequency can be varied much more quickly to achieve the optimum value of the variable RF frequency than the variable capacitance can be varied to achieve the optimum value of the variable capacitance. For example, the variable RF frequency can be varied to approximately microseconds when compared to approximately seconds for varying the variable capacitance. Thus, it is difficult to set the RF generator directly to operate at the optimum value of the variable RF frequency and to set the impedance matching network to operate at the optimum value of the variable capacitance. Instead of tuning the impedance matching network to achieve the optimum value of the variable capacitance and tuning the RF generator to achieve the optimum value of the variable RF frequency, the impedance matching network is optimized for the variable capacitance Value is tuned in a stepwise fashion to produce a step variable capacitance value and a local optimal value of the variable RF frequency for the step variable capacitance is calculated. For example, the impedance matching network is tuned to have a value of the variable capacitance in the direction of the optimal value of the variable capacitance and a local optimum value of the variable RF frequency determined for the value of the variable capacitance. In this way, instead of directly achieving the optimum value of the variable capacitance and the optimal value of the variable RF frequency, the optimal value of the variable capacitance and the optimum value of the variable RF frequency are achieved.

Some advantages of the systems and methods described herein include applying a staged approach to tuning the variable capacitance of the impedance matching network. In a step-wise fashion, the local optimum value of the variable RF frequency at which the reflection coefficient at the input of the matching network model is the minimum value is calculated for the step value of the variable capacitance. The step value is then incremented and another value of the variable RF frequency at which the reflection coefficient at the input of the matching network model is the minimum value is calculated for the incremental step value of the variable capacitance. The step value is incremented until the optimum value of the variable capacitance is reached. It is difficult to achieve the optimum value of the variable capacitance directly from the value at which the impedance matching circuit is operated while the optimum value of the variable RF frequency is achieved. This is because it is difficult to control one or more variable capacitors of the impedance matching network at the same rate as controlling the RF generator. By using the step-by-step approach, the optimum value of the variable capacitance and the optimum value of the RF frequency are achieved.

Other aspects will become apparent from the following detailed description, taken in conjunction with the accompanying drawings.

The embodiments are understood with reference to the following description taken in conjunction with the accompanying drawings.
1 is a diagram of an embodiment of a plasma system for illustrating the generation of a load impedance ZL1 using a matching network model.
2 is a diagram of an embodiment of a matching network model that initializes radio frequency RF1 and variable capacitance C1 to produce a reflection coefficient Γi at the input of the matching network model.
3 is a diagram of an embodiment of a plasma system to illustrate the use of a capacitance Coptimum1 to produce a stepped variable capacitance value Cstep1 and the use of a value RFoptimum1 @ C1 to produce a load impedance ZL2.
4 is a diagram of an embodiment of a matching network model set to a radio frequency RFoptimum1 @ C1 and an associated variable capacitance Cstep1 to produce a minimum value of the reflection coefficient Γi at the input of the matching network model.
5 is a diagram of an embodiment of a plasma system to illustrate the use of a capacitance value Coptimum2 for generating another stepped variable capacitance value Cstep2 and the use of a value RFoptimum1 @ Cstep1 to generate a load impedance ZL3.
6 is a diagram of an embodiment of a matching network model set to a radio frequency RFoptimum1 @ Cstep1 and a combined variable capacitance Cstep2 to produce a minimum value of the reflection coefficient? I at the input of the matching network model.
7 is a diagram of an embodiment of a plasma system to illustrate the use of a capacitance value Coptimum3 and the use of a value RFoptimum1 @ Cstep2 to produce a load impedance ZL4.
FIG. 8 is a diagram of an embodiment of a matching network model that is set to a radio frequency RFoptimum1 @ Cstep2 and a combined variable capacitance Coptimum3 to produce a minimum value of the reflection coefficient? I at the input of the matching network model.
9 is a diagram of an embodiment of a plasma system to illustrate the use of the capacitance value Coptimum3 and the use of the value RFoptimum1 @ Coptimum to process the wafer W. FIG.
FIG. 10 is an example of a graph for illustrating step-by-step tuning of an impedance matching network and step-by-step tuning of an RF generator of a plasma system.

The following embodiments describe systems and methods for tuning an impedance matching network in a step-wise fashion. It is to be understood that these embodiments may be practiced without some or all of these specific details. In other instances, well-known process operations have not been described in detail so as not to unnecessarily obscure the embodiments.

FIG. 1 is a diagram of an embodiment of a plasma system 100 for illustrating the generation of a load impedance ZL1 using a matching network model 102. FIG. The plasma system 100 includes a radio frequency (RF) generator 104, an impedance matching network 106, and a plasma chamber 108. Plasma system 100 includes a host computer system 110, a drive assembly 112, and one or more connection mechanisms 114.

The plasma chamber 108 includes an upper electrode 116, a chuck 118, and a wafer W. The upper electrode 116 faces the chuck 118 and is grounded, for example, coupled to a reference voltage, coupled to a zero voltage, coupled to a negative voltage, and so on. Examples of chuck 118 include an electrostatic chuck (ESC) and a magnetic chuck. The lower electrode of the chuck 118 is made of a metal, for example, anodized aluminum, an alloy of aluminum, or the like. In various embodiments, the lower electrode of the chuck 118 is a thin layer of metal covered by a layer of ceramic. Further, the upper electrode 116 is made of a metal, for example, an alloy of aluminum, aluminum, or the like. In some embodiments, the top electrode 116 is made of silicon. The upper electrode 116 is located opposite the lower electrode of the chuck 118 and faces the lower electrode of the chuck 118. The wafer W may be subjected to processing, for example, deposition of materials on the wafer W, or cleaning of the wafer W, or etching of layers deposited on the wafer W, or wafer W doping, The top surface of the chuck 118 (e.g., the top surface of the chuck 118) for implanting ions onto the wafer W, or creating a photolithographic pattern on the wafer W, or wafer W etching, or wafer W sputtering, 120).

In some embodiments, the plasma chamber 108 may include additional components, such as an upper electrode extension surrounding the upper electrode 116, a chuck 108 surrounding the upper electrode 116, A dielectric ring between the upper electrode 116 and the upper electrode extension, a dielectric ring between the lower electrode and the lower electrode extension, and a dielectric ring between the lower electrode extension and the edges of the upper electrode 116, And is formed using constrained rings and chuck 118 positioned,

The impedance matching network 106 may include one or more circuit components, e.g., one or more inductors, or one or more capacitors, or one or more resistors, or a combination of two or more thereof, And the like. For example, the impedance matching network 106 includes a series circuit that includes an inductor coupled in series with a capacitor. The impedance matching network 106 further includes a shunt circuit connected to the series circuit. The shunt circuit includes a capacitor connected in series with the inductor. The impedance matching network 106 includes one or more capacitors and the corresponding capacitances of one or more capacitors, e.g., all variable capacitors, etc., are varied and varied using, for example, a drive assembly, . The impedance matching network 106 includes one or more capacitors having fixed capacitances that can not be changed using, for example, a drive assembly 112, The combined variable capacitance of the one or more variable capacitors of the impedance matching network 106 is the value C1. For example, corresponding oppositely positioned plates of one or more variable capacitors are adjusted to be in a fixed position to set variable capacitance C1. For purposes of illustration, the combined capacitance of two or more capacitors connected in parallel with one another is the sum of the capacitances of the capacitors. As another example, the combined capacitance of two or more capacitors connected in series with each other is a reciprocal of the sum of the reciprocals of the capacitances of the capacitors. An example of an impedance matching network 106 is provided in a patent application having application number 14 / 245,803.

The matching network model 102 is derived from a branch of the impedance matching network 106 and represents, for example, a branch of the impedance matching network 106, and so on. For example, when an x MHz (megahertz) RF generator is coupled to a branch circuit of the impedance matching network 106, the matching network model 102 is a computer-generated model of the circuit of the branch circuit of the impedance matching network 106, For example, a computer-generated model. As another example, the matching network model 102 does not have the same number of circuit components as the impedance matching network 106. In some embodiments, the matching network model 102 has fewer circuit elements than the number of circuit components of the impedance matching network 106. For purposes of illustration, the matching network model 102 is a simplified form of the branch circuit of the impedance matching network 106. The variable capacitances of the plurality of variable capacitors of the branch circuit of the impedance matching network 106 are combined into a combined variable capacitance represented by one or more variable capacitive elements of the matching network model 102, The fixed capacitances of the plurality of fixed capacitors of the branch circuit of the network 106 are coupled to the combined fixed capacitance represented by one or more fixed capacitive elements of the matching network model 102 and / The inductances of the plurality of fixed inductors of the branch circuit are coupled to the combined inductance represented by one or more inductive elements of the matching network model 102 and / or the resistance of the plurality of resistors of the branch circuit of the impedance matching network 106 The matching network Is coupled to a fixed resistance represented by one or more resistive elements of the model (102). For further illustration, the capacitances of the series capacitors may be varied by inverting each of the capacitances to produce a plurality of inverted capacitances, summing the inverted capacitances to produce the inverted combined capacitances, and summing the combined capacitances Lt; RTI ID = 0.0 > inverted < / RTI > As another example, a plurality of inductances of inductors coupled in series are summed to produce a combined inductance, and a plurality of resistances of the series resistors are combined to produce a combined resistance. All of the fixed capacitances of all the fixed capacitors in the portion of the impedance matching network 106 are combined into the combined fixed capacitance of one or more fixed capacitive elements of the matching network model 102. [ Other examples of a matching network model 102 are provided in a patent application having application number 14 / 245,803. A method for generating a matching network model from an impedance matching network is also described in a patent application having application number 14 / 245,803.

In some embodiments, the matching network model 102 is generated from a schematic for an impedance matching network 106 with three branches, each of the branches comprising an x MHz RF generator, a y MHz RF generator, and z MHz RF generator. The three branches join each other at the output 140 of the impedance matching network 106. The wiring diagram initially includes a plurality of inductors and capacitors in various combinations. To separately consider one of the three branches, the matching network model 102 represents one of the three branches. Circuit elements are added to the matching network model 102 via an input device, examples of which are provided below. Examples of the added circuit elements include a wiring diagram for indicating the inductance of the resistors, various connecting RF straps (straps) not previously included in the wiring diagram for considering the power losses of the branch of the impedance matching network 106 Inductors, and capacitors that were not previously included in the wiring diagram to represent the parasitic capacitances. In addition, some circuit elements are added to the wiring diagram through the input device to indicate the transmission line characteristics of the branch of the impedance matching network 106 due to the physical size of the impedance matching network 106. [ For example, the uncoiled length of one or more inductors in the branch of the impedance matching network 106 can not be compared with the wavelength of the RF signal passing through one or more inductors. To account for this effect, the inductor of the wiring diagram is divided into two or more inductors. Thereafter, some circuit elements are removed through the input device from the wiring diagram to create the matching network model 102.

In various embodiments, the matching network model 102 has the same topology as the branch circuit of the impedance matching network 106, e.g., connections between circuit elements, number of circuit elements, and so on. For example, if the branch circuit of the impedance matching network 106 includes a capacitor coupled in series with the inductor, the matching network model 102 includes a capacitor coupled in series with the inductor. In this example, the inductors of the branch circuit of the impedance matching network 106 and the inductors of the matching network model 102 have the same value, and the capacitors of the branch circuit of the impedance matching network 106 and the matching network model 102, Have the same value. As another example, if the branch circuit of the impedance matching network 106 includes a capacitor coupled in parallel with the inductor, the matching network model 102 includes a capacitor coupled in parallel with the inductor. In this example, the inductors of the branch circuit of the impedance matching network 106 and the inductors of the matching network model 102 have the same value, and the capacitors of the branch circuit of the impedance matching network 106 and the matching network model 102, Have the same value. As another example, the matching network model 102 has circuit elements of the same number and type as the circuit components of the impedance matching network 106, and connections between circuit elements of the same type between the circuit components . Examples of types of circuit elements include resistors, inductors, and capacitors. Examples of types of connections include serial, parallel, and so on.

In addition, the RF generator 104 includes an RF power supply 122 for generating an RF signal. The RF generator 104 includes a sensor 124 coupled to an output 126 of the RF generator 104 and coupled to a sensor 124 such as a complex impedance sensor, a complex current and voltage sensor, a complex reflection coefficient sensor, a complex voltage sensor, Current sensors, and the like. The output 126 is connected to the input 128 of the branch circuit of the impedance matching network 106 via the RF cable 130. The impedance matching network 106 is coupled to the plasma chamber 108 via an RF transmission line 132 that includes a RF rod and an RF outer conductor surrounding the RF load.

The drive assembly 112 includes a driver, e.g., one or more transistors, etc., and a motor, and the motor is coupled to a variable capacitor of the impedance matching network 106 via a connection mechanism 114. Examples of the connection mechanism 114 include one or more rods, or rods connected to one another via a gear, or the like. The connection mechanism 114 is connected to the variable capacitor of the impedance matching network 106. For example, the connection mechanism 114 is connected to a variable capacitor that is part of a branch circuit coupled to the RF generator 104 via an input 128.

In a case where the impedance matching network 106 includes two or more variable capacitors in a branch circuit coupled to the RF generator 104, the drive assembly 112 includes individual motors for controlling two or more variable capacitors, And each of the motors is connected to a corresponding variable capacitor through a corresponding connection mechanism. In this case, the plurality of connection mechanisms is referred to as a connection mechanism 114.

The RF generator 104 is an x ㎒ RF generator or a y ㎒ RF generator or a z ㎒ RF generator. In some embodiments, an example of an x MHz RF generator includes a 2 MHz RF generator, an example of a y MHz RF generator includes a 27 MHz RF generator, and an example of a z MHz RF generator includes a 60 MHz RF generator do. In various embodiments, an example of an x MHz RF generator includes a 400 MHz RF generator, an example of a y MHz RF generator includes a 27 MHz RF generator, and an example of a z MHz RF generator includes a 60 MHz RF generator do.

When two RF generators, e.g., x and y MHz RF generators, etc., are used in the plasma chamber 100, one RF generator of the two RF generators is connected to the input 128, It should be noted that another RF generator of the RF generators is connected to another input of the impedance matching network 106. Similarly, when three RF generators, e.g., x, y, and z ㎒ RF generators, etc., are used in the plasma chamber 100, the first one of the three RF generators is coupled to the input A second RF generator of the RF generators is coupled to a second input of the impedance matching network 106 and a third RF generator of the RF generators is coupled to a third input of the impedance matching network 106, Lt; / RTI > The output unit 140 is connected to the input unit 128 through a branch circuit of the impedance matching network 106. In embodiments where a plurality of RF generators are used, the output 140 is connected to the second input through a second branch of the impedance matching network 106 and the output 140 is connected to the impedance matching network 106, Lt; RTI ID = 0.0 > 3 < / RTI >

The host computer system 110 includes a processor 134 and a memory device 137. Examples of the host computer system 110 include a laptop computer or a desktop computer or a tablet or smart phone, and the like. As used herein, a central processing unit (CPU), a controller, an application specific integrated circuit (ASIC), or a programmable logic device (PLD) is used instead of a processor and these terms are used interchangeably herein Is used. Examples of memory devices include read-only memory (ROM), random access memory (RAM), hard disk, volatile memory, non-volatile memory, redundant arrays of storage disks, flash memory, and the like. The sensor 124 is connected to the host computer system 110 via a network cable 136. As used herein, examples of network cables are cables used to transmit data in a serial manner, in a parallel manner, or using a USB protocol, and the like.

The RF generator 104 operates at radio frequency RF1. For example, the processor 134 provides a recipe to the RF generator 104 that includes a radio frequency RF1 and a power value. The RF generator 104 receives the recipe via the network cable 138 connected to the host computer system 110 and the RF generator 104 and the digital signal processor (DSP) of the RF generator 104 converts the recipe into an RF And supplies it to the power supply unit 122. The RF power supply 122 generates an RF signal having radio frequency RF1 and power prescribed in the recipe.

The impedance matching network 106 is initialized to have a combined variable capacitance C1. For example, the processor 134 sends a signal to the driver of the drive assembly 112 to generate one or more current signals. One or more current signals are generated by the driver and transmitted to the corresponding one or more stator (s) of the corresponding one or more motors of the drive assembly 112. The corresponding one or more rotors of the drive assembly 112 rotate to move the coupling mechanism 114 to change the combined variable capacitance of the branch circuit of the impedance matching network 106 to Cl. The branch circuit of the impedance matching network 106 with the combined variable capacitance C1 receives the RF signal with the radio frequency RF1 from the output 126 via the input 128 and RF cable 130, Matching the impedance of the source coupled to the impedance matching network 106 to the impedance of the load coupled to the impedance matching network 106 to generate the signal. Examples of loads include a plasma chamber 108 and an RF transmission line 132. Examples of sources include an RF cable 130 and an RF generator 104. The modified signal is provided from the output 140 of the branch circuit of the impedance matching network 106 to the chuck 118 via the RF transmission line 132. When the modified signal is provided to the chuck 118 with one or more process gases, such as an oxygen containing gas, a fluorine containing gas, etc., a plasma is generated in the gap between the chuck 118 and the top electrode 116 Or maintained.

When the RF signal with radio frequency RF1 is generated and the impedance matching network 106 has a combined variable capacitance C1, the sensor 124 senses the voltage reflection coefficient Γmi1 at the output 126, To the processor 134. The voltage reflection coefficient < RTI ID = 0.0 > An example of the voltage reflection coefficient includes the ratio of the power reflected from the plasma chamber 108 toward the RF generator 104 and the power supplied in the RF signal generated by the RF generator 104. The processor 134 calculates the impedance Zmi1 from the voltage reflection coefficient Gmi1. For example, the processor 134 applies Equation (1) with Γmi1 = (Zmi1-Zo) / (Zmi1 + Zo) and solves for Zmi1 to calculate the impedance Zmi1, where Zo is the impedance of the RF transmission line 132 Characteristic impedance. The impedance Zo is an input device, e.g., a mouse, a keyboard, a stylus, a keyboard, etc., which is connected to the processor 134 through an input / output interface, e.g., a serial interface, a parallel interface, a universal serial bus A keypad, a button, a touch screen, and the like. In some embodiments, the sensor 124 measures the impedance Zmi1 and provides the impedance Zmi1 to the processor 134 via the network cable 136.

The impedance Zmi1 is applied by the processor 134 to the input 142 of the matching network model 102 and the matching network model 102 to calculate the load impedance ZL1 at the output 144 of the matching network model 102 Lt; / RTI > For example, the impedance Z1 propagates forward through one or more circuit elements of the matching network model 102 by the processor 134 to produce a load impedance ZL1. For illustrative purposes, matching network model 102 is initialized to have radio frequency RF1. When the matching network model 102 includes a series combination of a resistive element, an inductive element, a fixed capacitive element, and a variable capacitive element, the processor 134 may cause the input of the matching network model 102 to produce a load impedance ZL1 142, the complex impedance across the variable capacitance element with the variable capacitance C1, and the directional sum of the complex impedance over the fixed capacitance element are calculated .

In some embodiments, the RF generator 104 operates in a continuous wave mode rather than a pulsed wave mode. For example, the RF generator 104 does not have the pulsed states S1 and S2, and all the power values of the RF signal in state S2 exclude the power values of the RF signal in state S1. State S2 has power values that are lower than the power values in state S1. As another example, in the continuous wave mode, the difference between at least one power value of state S2 and at least one power value of state S1 so as to eliminate the difference between states S1 and S2 to produce a state, .

In various embodiments, instead of measuring the voltage reflection coefficient at the output 126, the voltage reflection coefficient may include an output 126, such as an arbitrary value on the RF cable 130 from the output 126 to the input 128, Lt; / RTI > For example, the sensor 124 is connected to a point between the RF power supply 122 and the impedance matching network 106 to measure the voltage reflection coefficient.

2 is a diagram of an embodiment of a matching network model 102 initiated with a radio frequency RF1 and variable capacitance C1 to produce a voltage reflection coefficient Γi at the input 142. The processor 134 computes the radio frequency value RFoptimum and the combined variable capacitance value Coptimum1 whose voltage reflection coefficient Γi is zero from the load impedance ZL1 and the matching network model 102. [ For example, the processor 134 propagates the load impedance ZL1 in the reverse direction through the matching network model 102 to produce an input impedance Zi corresponding to a voltage reflection coefficient < RTI ID = 0.0 > Reverse propagation is the same as forward propagation except that the reverse propagation direction is opposite to the forward propagation direction. In some embodiments, the nonlinear least-squares optimization routine may be performed by the processor 134 to compute a radio frequency value RFoptimum and a combined variable capacitance value Coptimum1 with a voltage reflection coefficient? I of zero from the load impedance ZL1 and matching network model 102 . In various embodiments, the predetermined equations are applied by the processor 134 to calculate the radio frequency value RFoptimum and the combined variable capacitance value Coptimum1 with a voltage reflection coefficient? I of the load impedance ZL1 and the matching network model 102 from zero .

In addition, the processor 134 varies the radio frequency values applied to the matching network model 102 from RFoptimum1 @ C1 to RFoptimum @ C1 and sets the load impedance ZL1 to determine the radio frequency RFoptimum1 @ C1, Propagating in the opposite direction, where n is an integer greater than one. For example, the processor 134 may select a matching network model 102 having a variable capacitance C1 when the matching network model 102 has a radio frequency RFoptimum1 @ C1 to determine that the voltage reflection coefficient? I has a first value, Lt; RTI ID = 0.0 > ZL1 < / RTI > In addition, in this example, the processor 134 may determine that the matching network model 102 having a variable capacitance C1 when the matching network model 102 has a radio frequency RFoptimum2 @ C1 to determine that the voltage reflection coefficient? I has a second value 102 to reverse the load impedance ZL1. The processor 134 determines that the first value is the minimum of the first value and the second value so that the RFoptimum1 @ C1 also determines that the voltage reflection coefficient? I is the radio frequency value with the minimum value. In some embodiments, the nonlinear squared optimization routine is used to find a radio frequency value RFoptimum1 @ C1 with a voltage reflection coefficient G i having a minimum value.

In various embodiments, the value of the radio frequency whose voltage reflection coefficient is at the minimum value is referred to herein as a suitable RF value.

In some embodiments, the RF value is sometimes referred to herein as a "parameter value ". In addition, the capacitance is sometimes referred to herein as a "measurable factor ".

3 illustrates the use of the capacitance value Coptimum1 to generate a stepped variable capacitance value Cstep1 and the use of a value RFoptimum1 @ C1 to produce a load impedance ZL2 at the output 144 of the matching network model 102 ≪ / RTI > is a diagram of an embodiment of a plasma system (100). The processor 134 modifies the recipe to include the radio frequency value RFoptimum1 @ C1 and provides the radio frequency value RFoptimum1 @ C1 to the RF generator 104. In addition, the processor 134 determines a step variable capacitance value Cstep1 that is one step in the direction of the value Coptimum1 from the value C1. Although one or more capacitances of the corresponding one or more variable capacitors of the impedance matching network 106 are modified to vary from Cl to Coptimum1, the one or more variable capacitors may be varied by changing the RF frequency of the RF signal generated by the RF generator 104 Lt; RTI ID = 0.0 > and / or < / RTI >

Instead of setting the combined variable capacitance of the impedance matching network 106 to the value Coptimum1 and instead of setting the RF generator 104 to generate an RF signal with a radio frequency RFoptimum, the processor 134 may use the impedance matching network 106 Controls the drive generator 112 to set the combined variable capacitance of the RF generator 104 to a value Cstep1 and controls the RF generator 104 to operate with the radio frequency RFoptimum1 @ C1. The impedance matching network 106 takes longer to achieve the variable capacitance Coptimum1 than the time taken by the RF generator 104 to generate an RF signal with a radio frequency RFoptimum, for example, approximately seconds, and so on. For example, the RF generator 104 takes approximately microseconds to achieve the radio frequency RFoptimum from the radio frequency RF1. As a result, it is difficult to achieve the value RFoptimum from the value RF1 such that the voltage reflection coefficient at the input 126 of the RF generator 104 is zero, while simultaneously achieving the value Coptimum1 from the value C1. Therefore, the variable capacitance of the impedance matching network 106 is adjusted to the steps, e.g., Cstep1, in the direction toward the variable capacitance Coptimum1.

The RF generator 104 is coupled to an RF (radio frequency) RFoptimum1 @ C1 radio frequency RFoptimum1 @ C1 that passes through the impedance matching network 106 to generate a modified signal provided to the lower electrode 118. The radio frequency RFoptimum1 @ C1 and the variable capacitance Cstep1 Signal. When the RF generator 104 generates an RF signal with a radio frequency RFoptimum1 @ C1 and the combined variable capacitance is Cstep1, the sensor 124 measures the voltage reflection coefficient Γmi2 at the output 126, Generates an impedance Zmi2 from the voltage reflection coefficient Gmi2 in the same manner as described above, in which the impedance Zmi1 is generated from the voltage reflection coefficient Gmi1. In addition, the impedance Zmi1 is a load impedance at the output 144 of the matching network model 102 in the same way that the load impedance ZL1 is generated at the output 144 from the impedance Zmi1 of the input 142 of the matching network model 102 Lt; RTI ID = 0.0 > ZL2. ≪ / RTI >

4 is a diagram of an embodiment of a matching network model 102 that is set to a radio frequency RFoptimum1 @ C1 and an associated variable capacitance Cstep1 to produce a minimum value of the voltage reflection coefficient Γi at the input 142. For example, the processor 134 applies the radio frequency RFoptimum1 @ C1 and the combined variable capacitance Cstep1 to the matching network model 102. As another example, the processor 134 sets the values of the parameters of the matching network model 102 as having a radio frequency value RFoptimum1 @ C1 and a combined variable capacitance value Cstep1.

In the same manner as described above for calculating the combined variable capacitance Coptimum1, the processor 134 calculates the combined variable capacitance value Coptimum2 with the voltage reflection coefficient? I of the load impedance ZL2 and the matching network model 102 from zero. The processor 134 reverses the load impedance ZL2 so as to vary the radio frequency values applied to the matching network model 102 from RFoptimum1 @ Cstep1 to RFoptimumn @ Cstep1 and to determine the radio frequency RFoptimum1 @ Cstep1, the voltage reflection coefficient? Where n is an integer greater than one. For example, when the matching network model 102 has the radio frequency RFoptimum1 @ Cstep1 to determine that the voltage reflection coefficient [theta] i has a first value, the processor 134 determines the matching network model 102 with the variable capacitance Cstep1, To propagate the impedance ZL2 in the reverse direction. In addition, in this example, the processor 134 may select a matching network model with a variable capacitance Cstep1 (e.g., when the matching network model 102 has a radio frequency RFoptimum2 @ Cstep1 to determine that the voltage reflection coefficient? I has a second value 102 in the opposite direction. The processor 134 determines that the first value is the minimum of the first value and the second value to further determine that the radio frequency value with the minimum value of the voltage reflection coefficient < RTI ID = 0.0 >

5 is a diagram of an embodiment of system 100 for illustrating the use of a capacitance value Coptimum2 to generate another stepped variable capacitance value Cstep2 and the use of a value RFoptimum1 @ Cstep1 to generate load impedance ZL3. The processor 134 modifies the recipe to include the radio frequency value RFoptimum1 @ Cstep1 and provides the radio frequency value RFoptimum1 @ Cstep1 to the RF generator 104. [ In addition, the processor 134 determines a step variable capacitance value Cstep2 that is one additional step in the direction of the value Coptimum2 from the value Cstep1. For example, of the variable capacitance values Cstep1, Cstep2, and Coptimum2, the variable capacitance value Cstep2 is greater than the value Cstep1 and less than the value Coptimum2, and the values Cstep2 and Cstep1 are greater than the value C1. As another example, of the variable capacitance values Cstep1, Cstep2, and Coptimum2, the variable capacitance value Cstep2 is smaller than the value Cstep1 and larger than the value Coptimum2, and the values Cstep1 and Cstep2 are smaller than the value C1.

Instead of setting the combined variable capacitance of the impedance matching network 106 with the value Coptimum2 and instead of setting the RF generator 104 to generate an RF signal with a radio frequency RFoptimum, Controls the drive assembly 112 such that the combined variable capacitance of the RF generator 104 is set to the value Cstep2 and controls the RF generator 104 to operate at the radio frequency RFoptimum1 @ Cstep1. For radio frequency RFoptimum1 @ Cstep1 and variable capacitance Cstep2, RF generator 104 has a radio frequency RFoptimum1 @ Cstep1 passing through impedance matching network 106 to produce a modified signal provided to lower electrode 118 Thereby generating an RF signal. For radio frequencies RFoptimum1 @ Cstep1 and variable capacitance Cstep2, the sensor 124 measures the voltage reflection coefficient Γmi3 at the output 126 and the processor 134 measures the impedance Zmi1 at the output 126 in the same manner that the impedance Zmi1 is generated from the voltage reflection coefficient Γmi1 And generates an impedance Zmi3 from the reflection coefficient. The impedance Zmi3 is set such that the load impedance ZL1 is applied to the output 144 of the matching network model 102 in the same manner that it is generated at the output 144 from the impedance Zmi1 of the input 142 of the matching network model 102 Propagates through the matching network model 102 in a forward direction to produce an impedance ZL3.

In some embodiments, the radio frequency RFoptimum1 @ Cstep1 equals the optimum radio frequency value RFoptimum and the combined variable capacitance of Cstep2 equals the value Coptimum2. In these embodiments, the operations described below with reference to Figs. 6 to 9 are not performed.

6 is a diagram of an embodiment of a matching network model 102 that is set to a radio frequency RFoptimum1 @ Cstep1 and a combined variable capacitance Cstep2 to produce a minimum value of the voltage reflection coefficient Γi at the input 142. In the same manner as described above for calculating the combined variable capacitance Coptimum1, the processor 134 computes the combined variable capacitance value Coptimum3 whose voltage reflection coefficient? I is zero from the load impedance ZL3 and the matching network model 102. [

In addition, the processor 134 may vary the radio frequency values applied to the matching network model 102 from RFoptimum1 @ Cstep2 to RFoptimumn @ Cstep2 and the load impedance ZL3 to determine the radio frequency RFoptimum1 @ Cstep2, Propagating in the opposite direction, where n is an integer greater than one. For example, when the matching network model 102 has the radio frequency RFoptimum1 @ Cstep2 to determine that the voltage reflection coefficient [gamma] i has a first value, the processor 134 determines the matching network model with the combined variable capacitance Cstep2 And propagates the impedance ZL3 in the reverse direction through the antenna 102. [ In addition, in this example, the processor 134 determines whether the matching network model 102 has a radio frequency RFoptimum2 @ Cstep2 to determine that the voltage reflection coefficient? I has a second value, And propagates the impedance ZL3 in the reverse direction through the network model 102. [ The processor 134 determines that the minimum of the first value and the second value is the first value so as to also determine that the radio frequency value with the minimum value of the voltage reflection coefficient Γi is RFoptimum1 @ Cstep2.

In some embodiments, either one of the capacitance values Coptimum2 and Coptimum3 is equal to the capacitance value Coptimum1, where the voltage reflection coefficient? I is zero.

7 is a diagram of an embodiment of the plasma system 100 to illustrate the use of the capacitance value Coptimum3 and the use of the value RFoptimum1 @ Cstep2 to produce the load impedance ZL4. The processor 134 modifies the recipe to include the radio frequency value RFoptimum1 @ Cstep2 and provides the radio frequency value RFoptimum1 @ Cstep2 to the RF generator 104. [ In addition, the processor 134 determines a step variable capacitance value Cstep3 that is one additional step in the direction of the value Coptimum3 from the value Cstep2. For example, the value Cstep3 is the value Coptimum3. For a further example, of the variable capacitance values Cstep1, Cstep2, and Coptimum3, the variable capacitance value Coptimum3 is greater than the value Cstep2, the value Cstep2 is greater than the value Cstep1, and the value Cstep1 is greater than the capacitance value C1. As another example, of the variable capacitance values Cstep1, Cstep2, and Coptimum3, the variable capacitance value Coptimum3 is smaller than the value Cstep2, the value Cstep2 is smaller than the value Cstep1, and the value Cstep1 is smaller than the value C1.

The processor 134 controls the drive assembly 112 such that the combined variable capacitance of the impedance matching network 106 is set to the value Coptimum3. In addition, instead of setting the RF generator 104 to generate an RF signal with a radio frequency RFoptimum, the processor 134 controls the RF generator 104 to operate with the radio frequency RFoptimum1 @ Cstep2.

The RF generator 104 is coupled to an RF with radio frequency RFoptimum1 @ Cstep2 passing through the impedance matching network 106 to generate a modified signal provided to the lower electrode 118. The radio frequency RFoptimum1 @ Cstep2 and the variable capacitance Coptimum3, Signal. For radio frequency RFoptimum1 @ Cstep2 and variable capacitance Coptimum3, the sensor 124 measures the voltage reflection coefficient Γmi4 at the output 126 and the processor 134 measures the impedance Zmi1 in the same way that the impedance Zmi1 is generated from the voltage reflection coefficient Γmi1 And generates an impedance Zmi4 from the reflection coefficient Gmi4. The impedance Zmi4 is a load impedance at the output 144 of the matching network model 102 in the same manner that the load impedance ZL1 is generated at the output 144 from the impedance Zmi1 of the input 142 of the matching network model 102. [ Lt; RTI ID = 0.0 > ZL4. ≪ / RTI >

In some embodiments, the value RFoptimum1 @ Cstep2 is equal to the radio frequency value RFoptimum. In these embodiments, the operations described below with reference to Figs. 8 and 9 are not performed.

In various embodiments, each of the optimum values Coptimum1, Coptimum2, and Coptimum3 is obtained after the processor 134 is programmed to calculate the optimal capacitance value within predetermined capacitance value boundaries. For example, the processor 134 is programmed to determine the optimal capacitance value Coptimum1 in the manner described above with respect to FIG. 2, except that the capacitance value Coptimum1 is between a predetermined upper limit and a predetermined lower limit value. The predetermined boundaries are the same as the operational boundaries of the one or more variable capacitors of the impedance matching network 106 (FIG. 1). For example, one or more variable capacitors can not physically operate outside operational boundaries. As another example, the processor 134 is programmed to determine the optimal capacitance value Coptimum2 in the manner described above with respect to FIG. 4, except that the capacitance value Coptimum2 is between a predetermined upper limit value and a predetermined lower limit value. As another example, the processor 134 is programmed to determine the optimal capacitance value Coptimum3 in the manner described above with respect to FIG. 6, except that the capacitance value Coptimum3 is between a predetermined upper limit and a predetermined lower limit value.

In some embodiments, each of the values RFoptimum1 @ C1, RFoptimum1 @ Cstep1, RFoptimum1 @ Cstep2, and RFoptimum1 @ Coptimum is obtained after the processor 134 is programmed to calculate an optimal RF value that is within predetermined limits . For example, the processor 134 is programmed to determine the RF value RFoptimum1 @ C1 in the manner described above for FIG. 2, except that the RF value RFoptimum1 @ C1 is between a predetermined upper boundary and a predetermined lower boundary . The predetermined limit values are the same as the operational boundaries of the RF generator 104 (FIG. 1). For example, the RF generator 104 can not physically operate outside operating borders. As another example, the processor 134 may be programmed to determine the RF value RFoptimum1 @ Cstep1 in the manner described above with respect to FIG. 4, except that the RF value RFoptimum1 @ Cstep1 is between a predetermined upper boundary and a predetermined lower boundary. do. As another example, the processor 134 determines an optimal RF value RFoptimum1 @ Cstep2 in the manner described above with respect to FIG. 6, except that the RF value RFoptimum1 @ Cstep2 is between a predetermined upper boundary and a predetermined lower boundary . As another example, the processor 134 determines an optimal RF value RFoptimum1 @ Coptimum in the manner described above with respect to FIG. 8, except that the RF value RFoptimum1 @ Coptimum is between a predetermined upper boundary and a predetermined lower boundary .

FIG. 8 is a diagram of an embodiment of a matching network model 102 that is set to a radio frequency RFoptimum1 @ Cstep2 and a combined variable capacitance Coptimum3 to produce a minimum value of the voltage reflection coefficient? I at the input 142. The processor 134 reverses the load impedance ZL4 so as to vary the radio frequency values applied to the matching network model 102 from RFoptimum1 @ Coptimum to RFoptimum @ Coptimum and to determine the radio frequency RFoptimum1 @ Coptimum, the voltage reflection coefficient? Where n is an integer greater than one. For example, when the matching network model has a radio frequency RFoptimum1 @ Cstep2 to determine that the voltage reflection coefficient? I has a first value, the processor 134 determines the load Impedance ZL4 is propagated in the reverse direction. Further, in this example, the processor 134 may select a matching network model 102 with a variable capacitance Coptimum3 when the matching network model has a radio frequency RFoptimum2 @ Cstep2 to determine that the voltage reflection coefficient? I has a second value The load impedance ZL4 is propagated in the reverse direction. The processor 134 determines that the minimum of the first value and the second value is the first value so as to also determine that the radio frequency value having the minimum value of the voltage reflection coefficient? I is RFoptimum1 @ Coptimum.

In some embodiments, the value RFoptimum1 @ Coptimum is equal to the value RFoptimum.

9 is a diagram of an embodiment of the system 100 for illustrating the use of the capacitance value Coptimum3 to process the wafer W and the use of the value RFoptimum1 @ Coptimum. The processor 134 modifies the recipe to include the radio frequency value RFoptimum1 @ Coptimum and provides the radio frequency value RFoptimum1 @ Coptimum to the RF generator 104. [ In addition, the processor 134 continues to control the drive assembly 112 such that the combined variable capacitance of the impedance matching network 106 is set to the value Coptimum3. In addition, instead of setting the RF generator 104 to generate an RF signal with a radio frequency RFoptimum, the processor 134 controls the RF generator 104 to operate at the radio frequency RFoptimum1 @ Coptimum.

The RF generator 104 is coupled to the impedance matching network 106 to pass through the impedance matching network 106 to generate a modified signal that is provided to the lower electrode 118 for processing the wafer W. For the radio frequency RFoptimum1 @ Coptimum and the variable capacitance Coptimum3, Generates RF signal with radio frequency RFoptimum1 @ Coptimum. In this way, instead of applying the radio frequency RFoptimum directly from the radio frequency RF1 and instead of applying the combined variable capacitance value Coptimum1 directly from the combined variable capacitance value C1, the combined variable capacitance value Cstep1 and the radio frequency RFoptimum1 @ And then applies the combined variable capacitance value Cstep1 and the radio frequency RFoptimum1 @ C1 for the first time and then the combined variable capacitance value Cstep2 and the radio frequency RFoptimum1 @ Cstep1 for the second time, and then the combined variable capacitance value Coptimum3 and wireless A step approach is provided in which the frequency RFoptimum1 @ Cstep2 is applied for the third time, and finally the combined variable capacitance value Coptimum3 and the radio frequency RFoptimum1 @ Coptimum are applied. For example, the application of the combined variable capacitance value Coptimum3 and the radio frequency RFoptimum1 @ Cstep2 precedes the application of the combined variable capacitance value Coptimum3 and radio frequency RFoptimum1 @ Coptimum. In addition, the application of the combined variable capacitance value Cstep2 and the radio frequency RFoptimum1 @ Cstep1 precedes the application of the combined variable capacitance value Coptimum3 and radio frequency RFoptimum1 @ Cstep2. Also, the application of the combined variable capacitance value Cstep1 and the radio frequency RFoptimum1 @ C1 precedes the application of the combined variable capacitance value Cstep2 and the radio frequency RFoptimum1 @ Cstep1.

In some embodiments, instead of applying the radio frequency RFoptimum directly from the radio frequency RF1 and instead of applying the combined variable capacitance value Coptimum1 directly from the combined variable capacitance value Cl, the combined variable capacitance value Cstep1 is applied to the radio frequency RFoptimum1 @ C1 (See FIG. 3), and then the combined variable capacitance value Cstep2 and the radio frequency RFoptimum1 @ Cstep1 are applied for a second time (see FIG. 5), and then the combined variable capacitance value Coptimum3 and radio frequency RFoptimum1 @ Cstep2 are applied for the third time (See FIG. 7), and then applying the finally combined variable capacitance value Coptimum3 and the radio frequency RFoptimum1 @ Coptimum (see FIG. 9).

In some embodiments, instead of generating an impedance, e.g., impedance Zmi1, from the voltage reflection coefficients received from sensor 124, e.g., Γmi1, Γmi2, Γmi3, Γmi4, Such as ΓL1, ΓL2, ΓL3, ΓL4, etc., at the output 144 of the matching network model 102. There is no need to convert from voltage reflection coefficient to impedance and vice versa.

In various embodiments, instead of the matching network model 102, the combination of the matching network model 102 and the RF transmission model may be used to change the capacitance of the impedance matching network 106 in a stepped manner, Lt; / RTI > For example, the load impedances ZL1, ZL2, ZL3, and ZL4 are calculated at the output of the RF transmission model instead of the output 144 of the matching network model 102. As another example, instead of using the matching network model 102 of FIGS. 2, 4, 6, and 8, both the matching network model 102 and the RF transmission model are used. The RF transmission model is coupled in series to the output 144 of the matching network model 102 and the matching network model 102 is derived from the RF transmission line 132 in a similar manner derived from the impedance matching network 106. For example, the RF transmission model has inductances, capacitances, and / or resistances derived from the inductances, capacitances, and / or resistances of the RF transmission line 132. As another example, the capacitance of the RF transmission model may match the capacitance of the RF transmission line 132, the inductance of the RF transmission model may match the inductance of the RF transmission line 132, and the resistance of the RF transmission model may correspond to the RF transmission line 132 ) ≪ / RTI >

In some embodiments, instead of the matching network model 102, a combination of the RF cable model, the matching network model 102, and the RF transmission model may be implemented in a step-wise manner, as described herein, It is used to change the capacitance. For example, the load impedances ZL1, ZL2, ZL3, and ZL4 are calculated at the output of the RF transmission model instead of the output 144 of the matching network model 102. As another example, instead of using the matching network model 102 of FIGS. 2, 4, 6, and 8, an RF cable model, a matching network model 102, and an RF transmission model are used. The RF cable model is coupled in series to the input 142 of the matching network model 102 and the matching network model 102 is derived from the RF cable 130 in a similar manner derived from the impedance matching network 106. For example, the RF cable model has inductances, capacitances, and / or resistances derived from the inductances, capacitances, and / or resistances of the RF cable 130. The capacitance of the RF cable model is matched with the capacitance of the RF cable 130 and the inductance of the RF cable model is matched with the inductance of the RF cable 130, Matching with the resistance of.

10 is an embodiment of a graph 1000 for illustrating step-by-step tuning of the impedance matching network 106 and step-by-step tuning of the RF generator 104. FIG. The graph 1000 plots the frequency of the RF signal generated by the RF generator 104 for the combined variable capacitance of the impedance matching network 106. The graph 1000 plots exemplary contours of the voltage reflection coefficient Γ as a function of the frequency of the RF signal generated by the RF generator 104 and the combined variable capacitance of the impedance matching network 106. Beginning at point B where the magnitude of the voltage reflection coefficient is approximately equal to 0.5, the matching network model 102 determines that the magnitude of Γ is approximately equal to zero and the value of the resistance at input 126 (FIG. 1) is 50 Ω Is an optimal tuning point. If the frequency of the RF signal generated by the RF generator 104 and the combined variable capacitance of the impedance matching network 106 are changed to the maximum achievable rates then the frequency will be lower than the variable tuning capacitance of the impedance matching network 106 Before changing to move, the magnitude of the voltage reflection coefficient Γ drops very quickly to a point C which is much lower. In stepped tuning, the combined variable capacitance of the impedance matching network 106 is changed from point B to point A, but through points D, E, and F, and the frequency of the RF signal is at points D, E, Tuned for each of the variable capacitances. At each of the points D, E, and F, the local optimal frequency of the RF signal relative to the minimum magnitude of the voltage reflection coefficient Γ is determined.

It should be noted that in some of the embodiments described above, an RF signal is applied to the lower electrode of the chuck 118 and the upper electrode 116 is grounded. In various embodiments, an RF signal is applied to the upper electrode 116 and a lower electrode of the chuck 118 is grounded.

The embodiments described herein may be practiced with a variety of computer system configurations, including portable hardware units, microprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, have. The embodiments described herein may also be practiced in distributed computing environments where tasks are performed by remote processing hardware units linked through a computer network.

In some embodiments, the controller is part of a system that may be part of the above described examples. The system includes semiconductor processing equipment, including processing tools or tools, chambers or chambers, processing platforms or platforms, and / or specific processing components (wafer pedestal, gas flow system, etc.). The system is incorporated into an electronic device for controlling its operation prior to, during, and after the processing of a semiconductor wafer or substrate. An electronic device is referred to as a "controller" that may control various components or sub-components of the system. The controller may control the delivery of process gases, temperature settings (e.g., heating and / or cooling), pressure settings, vacuum settings, power settings, etc., depending on the processing requirements and / , RF generator setups, RF matching circuit setups, frequency setups, flow rate setups, fluid delivery setups, location and operation setups, tools and other delivery tools and / or systems Such as wafer transfers into and out of interfaced load locks.

Generally speaking, in various embodiments, the controller may be implemented with various integrated circuits, logic, memory, memory, etc. that receive instructions and issue instructions, control operations, enable cleaning operations, enable end point measurements, And / or software. The integrated circuits may be implemented as chips in the form of firmware that stores program instructions, digital signal processors (DSPs), chips defined as application specific integrated circuits (ASICs), PLDs, program instructions (e.g., One or more microprocessors, or microcontrollers. Program instructions are instructions that are passed to the controller in the form of various individual settings (or program files) that define operating parameters for executing a process on a semiconductor wafer or a semiconductor wafer. In some embodiments, the operating parameters may be varied to achieve one or more processing steps during fabrication of one or more layers, materials, metals, oxides, silicon, silicon dioxide, surfaces, circuits, and / It is part of the recipe specified by the engineer.

The controller is, in some embodiments, coupled to or part of a computer that is integrated into the system, coupled to the system, or otherwise networked to the system, or a combination thereof. For example, the controller is in all or part of a factory host computer system or "cloud" that enables remote access of wafer processing. The controller monitors the current progress of manufacturing operations, examines the history of past manufacturing operations, examines trends or performance metrics from a plurality of manufacturing operations, changes the parameters of the current processing, Enable remote access to the system to set up or start a new process.

In some embodiments, a remote computer (e.g., a server) provides process recipes to the system via a local network or a computer network including the Internet. The remote computer includes a user interface that allows input or programming of parameters and / or settings to be communicated from the remote computer to the system at a later time. In some instances, the controller receives instructions in the form of settings for processing wafers. It should be understood that these settings are specific to the type of tool that the controller will control or interfere with and the type of process to be performed on the wafer. Thus, as discussed above, the controller is distributed, for example, by including one or more individual controllers, such as those that are networked together and function together for a common purpose, e.g., to implement the processes described herein. Examples of decentralized controllers for these purposes include one or more integrations on a chamber communicating with one or more integrated circuits remotely located (e.g., at the platform level or as part of a remote computer) Circuits.

In a non-limiting embodiment, the system includes a plasma etch chamber, a deposition chamber, a spin-rinse chamber, a metal plating chamber, a cleaning chamber, a bevel edge etch chamber, a physical vapor deposition (PVD) chamber, a chemical vapor deposition (CVD) A chamber, an atomic layer deposition (ALD) chamber, an atomic layer etch (ALE) chamber, an ion implantation chamber, a track chamber, and any other semiconductor processing chamber used or associated with the manufacture and / .

Although the above described operations are described with respect to parallel plate plasma chambers, e.g., capacitively coupled plasma chambers, etc., in some embodiments, the operations described above may be applied to other types of plasma chambers, It is further noted that the present invention is applied to a plasma chamber including a combined plasma (ICP) reactor, a transformer coupled plasma (TCP) reactor, conductor tools, dielectric tools, an electron cyclotron resonance (ECR) For example, the x ㎒ RF generator, the y ㎒ RF generator, and the z ㎒ RF generator are coupled to the inductor in the ICP plasma chamber.

As described above, in accordance with the process operations to be performed by the tool, the controller may be used in other tool circuits, such as those used in material transfer to move containers of wafers to / from tool positions and / Or tools, tools, modules or modules, other tool components, cluster tools, other tool interfaces, adjacent tools, neighboring tools, tools located all over the plant, main computer, other controller or tools.

In view of the above embodiments, it should be understood that some of the embodiments employ various computer-implemented operations involving data stored in computer systems. These computer-implemented operations are operations that manipulate physical quantities.

Some of the embodiments also relate to a hardware unit or device for performing these operations. The device is specially configured as a special purpose computer. When specified as a special purpose computer, the computer also performs other processing, program executions or routines that are not part of a particular purpose, but may still operate for a special purpose.

In some embodiments, the operations described herein may be performed by an optionally activated computer, or by one or more computer programs stored in a computer memory, or obtained over a network. When data is acquired over a computer network, the data may be processed by a cloud of other computers, e.g., computing resources, on the computer network.

One or more embodiments described herein may also be made as computer readable code on non-volatile computer-readable media. The non-volatile computer-readable medium is any data storage hardware unit, such as a memory device, that stores data that is thereafter read by a computer system. Examples of non-transitory computer-readable media include hard drives, network attached storage (NAS), read-only memory (ROM), random access memory (RAM), compact disc-ROMs, CD- rewritables, magnetic tapes, and other optical and non-optical data storage hardware units. In some embodiments, the non-transitory computer-readable medium includes a computer-readable type of medium distributed over network coupled computer systems in which the computer readable code is stored and executed in a distributed manner.

Although some of the method operations described above have been presented in a particular order, in various embodiments, other housekeeping operations may be performed between method operations, or method operations may be tuned such that method operations occur at slightly different times Or distributed in a system that allows the generation of method operations at various intervals, or in a different order than that described above.

It should be noted that in one embodiment, one or more features of any other embodiment are combined with one or more features from any of the embodiments described above without departing from the scope of the various embodiments described in this disclosure do.

Although the foregoing embodiments have been described in some detail for purposes of clarity of understanding, it will be apparent that certain changes and modifications may be practiced within the scope of the appended claims. Accordingly, the embodiments are to be considered as illustrative rather than restrictive, and the embodiments are not intended to be limited to the details given herein, but may be modified within the scope and equivalents of the appended claims.

Claims (21)

A method for tuning an impedance matching network in a step-wise manner,
The method of claim 1, further comprising: when a radio frequency (RF) generator operates with a first parameter value and the impedance matching network has a first variable measurable factor, Receiving an input parameter value;
Initializing one or more models with the first variable measurable factor and the first parameter value, the one or more models including a matching network model of the impedance matching network;
Calculating a first output parameter value using the one or more models from the first measured input parameter value when the one or more models have the first variable measurable factor and the first parameter value;
Using the first output parameter value and the one or more models to calculate an optimal variable measurable parameter and an optimal parameter value with a reflection coefficient of zero at the input of the one or more models;
Using the first output parameter value and the one or more models to calculate a first suitable parameter value at which the reflection coefficient at the input of the one or more models is at a minimum value;
Operating the RF generator with the first suitable parameter value; And
Establishing the impedance matching network to have a first step variable measurable factor such that the first step variable measurable factor is tuned to the optimum Wherein the impedance matching network is closer to the variable measurable factor of the impedance matching network. The method according to claim 1,
When the RF generator is operated with the first suitable parameter value and the impedance matching network is set to have the first step variable measurable factor, between the output of the RF generator and the input of the impedance matching network Receiving the sensed second measured input parameter value;
Setting the one or more models to have the first step variable measurable factor and the first suitable parameter value;
Calculating the second output parameter value using the one or more models from the second measured input parameter value when the one or more models have the first step variable measurable factor and the first suitable parameter value;
Using the second output parameter value and the one or more models to calculate a second suitable parameter value at which the reflection coefficient at the input of the one or more models is at a minimum value;
Operating the RF generator with the second suitable parameter value; And
And setting the impedance matching network to have a second step variable measurable factor. ≪ Desc / Clms Page number 21 > 3. The method of claim 2,
Wherein the second suitable parameter value is the optimal parameter value. ≪ Desc / Clms Page number 21 > 3. The method of claim 2,
When the RF generator is operated with the second suitable parameter value and the impedance matching network is set to have the second step variable measurable factor, the output of the RF generator and the input of the impedance matching network Receiving a sensed third measured input parameter value;
Setting the one or more models to have the second step variable measurable factor and the second suitable parameter value;
Calculating a third output parameter value using the one or more models from the third measured input parameter value;
Using the third output parameter value and the one or more models to calculate a third suitable parameter value at which the reflection coefficient at the input of the one or more models is at a minimum value; And
And operating the RF generator with the third suitable parameter value. ≪ Desc / Clms Page number 21 > 5. The method of claim 4,
Wherein the third suitable parameter value is the optimal parameter value. ≪ Desc / Clms Page number 21 > 5. The method of claim 4,
Wherein the third suitable parameter value is different from the optimal parameter value. ≪ Desc / Clms Page number 19 > The method according to claim 1,
Wherein the first measured parameter value is sensed by a sensor coupled to the output of the RF generator and wherein the first measured parameter value is an impedance or a reflection coefficient, a method for tuning the impedance matching network in a step- . The method according to claim 1,
Wherein the first output parameter value is calculated by propagating the first measured input parameter value in a forward direction through circuit elements of the one or more models. ≪ Desc / Clms Page number 21 > The method according to claim 1,
Wherein the optimal parameter value and the optimal variable measurable factor are calculated by propagating the first output parameter value in the reverse direction through circuit elements of the one or more models to achieve the reflection coefficient of zero, A method for tuning a network. A system for tuning an impedance matching network in a stepwise manner,
A first measured input parameter value sensed between an output of the RF generator and an input of the impedance matching network when the RF generator operates with a first parameter value and the impedance matching network has a first variable measurable factor, A processor configured to receive,
Wherein the processor is configured to initialize one or more models with the first variable measurable factor and the first parameter value, wherein the one or more models comprise a model of the impedance matching network; And
A memory device coupled to the processor, the memory device configured to store the one or more models,
When the one or more models have the first variable measurable factor and the first parameter value, the processor is configured to calculate a first output parameter value using the one or more models from the first measured input parameter value And,
Wherein the processor is configured to use the first output parameter value and the one or more models to calculate an optimal variable measurable factor and an optimal parameter value with a reflection coefficient of zero at an input of the one or more models,
Wherein the processor is configured to calculate a first suitable parameter value at which the reflection coefficient at the input of the one or more models is at a minimum value, using the first output parameter value and the one or more models,
Wherein the processor is configured to operate the RF generator with the first suitable parameter value,
Wherein the processor is configured to set the impedance matching network to have a first step variable measurable factor, wherein the first step variable measurable factor is determined by comparing the first variable measurable factor such that the impedance matching network is tuned in a step- Wherein the system is tuned to the optimum variable measurable factor to provide a tunable impedance matching network. 11. The method of claim 10,
When the RF generator is operated with the first suitable parameter value and the impedance matching network is set to have the first step variable measurable factor, And to receive a second measured input parameter value sensed between inputs,
Wherein the processor is configured to set the one or more models to have the first step variable measurable factor and the first suitable parameter value,
When the one or more models have the first step variable measurable factor and the first suitable parameter value, the processor calculates the second output parameter value using the one or more models from the second measured input parameter value Lt; / RTI >
Wherein the processor is configured to use the second output parameter value and the one or more models to calculate a second suitable parameter value at which the reflection coefficient at the input of the one or more models is at a minimum value,
Wherein the processor is configured to operate the RF generator with the second suitable parameter value, and
Wherein the processor is configured to set the impedance matching network to have a second step variable measurable factor. 12. The method of claim 11,
When the RF generator is operated with the second suitable parameter value and the impedance matching network is set to have the second step variable measurable factor, the processor is further operable to determine, based on the output of the RF generator, And to receive a third measured input parameter value sensed between inputs,
Wherein the processor is configured to set the one or more models to have the second step variable measurable factor and the second suitable parameter value,
Wherein the processor is configured to calculate a third output parameter value using the one or more models from the third measured input parameter value,
Wherein the processor is configured to calculate a third suitable parameter value at which the reflection coefficient at the input of the one or more models is at a minimum value using the third output parameter value and the one or more models,
And wherein the processor is configured to operate the RF generator with the third suitable parameter value. 13. The method of claim 12,
And wherein the third suitable parameter value is the optimal parameter value. 13. The method of claim 12,
Wherein the third suitable parameter value is different from the optimal parameter value. ≪ Desc / Clms Page number 13 > 11. The method of claim 10,
Wherein the first measured parameter value is sensed by a sensor coupled to the output of the RF generator and wherein the first measured parameter value is an impedance or a reflection coefficient, . A system for tuning an impedance matching network in a stepwise manner,
An RF generator having an output;
An impedance matching network coupled to the output of the RF generator;
A plasma chamber connected to the impedance matching network via an RF transmission line; And
A processor coupled to the RF generator,
Wherein when the RF generator is operating with a first parameter value and the impedance matching network has a first variable measurable factor, the processor is operable to detect a first variable sensible parameter between the output of the RF generator and an input of the impedance matching network, And to receive the measured input parameter value,
Wherein the processor is configured to initialize one or more models with the first variable measurable factor and the first parameter value, wherein the one or more models comprise a matching network model of the impedance matching network,
When the one or more models have the first variable measurable factor and the first parameter value, the processor is configured to calculate a first output parameter value using the one or more models from the first measured input parameter value And,
Wherein the processor is configured to use the first output parameter value and the one or more models to calculate an optimal variable measurable factor and an optimal parameter value with a reflection coefficient of zero at an input of the one or more models,
Wherein the processor is configured to calculate a first suitable parameter value at which the reflection coefficient at the input of the one or more models is at a minimum value, using the first output parameter value and the one or more models,
Wherein the processor is configured to operate the RF generator with the first suitable parameter value,
Wherein the processor is configured to set the impedance matching network to have a first step variable measurable factor, wherein the first step variable measurable factor is determined by comparing the first variable measurable factor such that the impedance matching network is tuned in a step- Wherein the system is tuned to the optimum variable measurable factor to provide a tunable impedance matching network. 17. The method of claim 16,
When the RF generator is operated with the first suitable parameter value and the impedance matching network is set to have the first step variable measurable factor, And to receive a second measured input parameter value sensed between inputs,
Wherein the processor is configured to set the one or more models to have the first step variable measurable factor and the first suitable parameter value,
When the one or more models have the first step variable measurable factor and the first suitable parameter value, the processor calculates the second output parameter value using the one or more models from the second measured input parameter value Lt; / RTI >
Wherein the processor is configured to use the second output parameter value and the one or more models to calculate a second suitable parameter value at which the reflection coefficient at the input of the one or more models is at a minimum value,
Wherein the processor is configured to operate the RF generator with the second suitable parameter value, and
Wherein the processor is configured to set the impedance matching network to have a second step variable measurable factor. 18. The method of claim 17,
When the RF generator is operated with the second suitable parameter value and the impedance matching network is set to have the second step variable measurable factor, the processor is further operable to determine, based on the output of the RF generator, And to receive a third measured input parameter value sensed between inputs,
Wherein the processor is configured to set the one or more models to have the second step variable measurable factor and the second suitable parameter value,
Wherein the processor is configured to calculate a third output parameter value using the one or more models from the third measured input parameter value,
Wherein the processor is configured to calculate a third suitable parameter value using the third output parameter value and the one or more models, wherein the reflection coefficient at the input of the one or more models is at a minimum value, and
And wherein the processor is configured to operate the RF generator with the third suitable parameter value. 19. The method of claim 18,
And wherein the third suitable parameter value is an optimal parameter value. 19. The method of claim 18,
And wherein the third suitable parameter value is different from the optimal parameter value. 18. The method of claim 17,
Wherein the first measured parameter value is sensed by a sensor coupled to the output of the RF generator and wherein the first measured parameter value is an impedance or a reflection coefficient, .

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