Fuzzy logic tuning of RF matching network

• US RE39,051 E1
• Filed: 11/14/2000
• Issued: 03/28/2006
• Est. Priority Date: 09/15/1997
• Status: Expired due to Term

• Current Assignee: MKS Instruments Incorporated
• Original Assignee: ENI Technology, Inc.
• Inventors: Harnett, Sean
• Primary Examiner(s): Wachsman, Hal D
• Application Number:
  US09/712,596
• Time in Patent Office: 1,960 Days
• Field of Search: 702/106, 702/107, 702/85, 702/57, 702/65, 702/72, 702/71, 702/189, 702/75, 333 171- 173, 333/32, 334/11, 334/12, 334/14, 334/71, 343861-864, 455/123, 455/125, 324/629, 324/630, 324/632, 324/638, 324/642, 706/1, 706 3- 5, 706/7, 706/8, 706/900, 706/903, 706/908, 706/909, 706/911, 706/916, 706/917, 700/47, 700/50
• US Class Current: 702/106
• CPC Class Codes: G05B 13/0275 using fuzzy logic only
• CPC Class Codes: H03H 7/40 Automatic matching of load impedance to source impedance
• CPC Class Codes: H05H 1/46 using applied electromagnetic fields, e.g. high frequency or microwave energy H05H1/26 takes precedence

Abstract

A fuzzy logic control arrangement is provided for an impedance match network of the type that is typically employed between a source of RF power at a given impedance, e.g., 50 ohms, and a non-linear load whose impedance can vary in magnitude and phase, e.g., an RF plasma. The fuzzy logic controller fuzzifies the phase and the magnitude error signals. The error signals are applied to a fuzzy logic interference function based on a number of fuzzy sets. The values of the error signals enjoy some degree of membership in one or more fuzzy sets. Fuzzy logic rules are applied to the phase and magnitude error signals. In a defuzzification stage, drive signal values are obtained for moving the tuning elements of the variable impedances. The drive signal values are weighted according to respective fuzzy inference functions for which the error signals enjoy membership. Then the weighted drive signal values are combined to produce output drive signals.

Claims

1. Fuzzy logic method of tuning an RF a radio frequency (RF) matching network of the type having an input at which is applied RF power at a given frequency and at a given impedance, and an output which applies said power to an RF load having a non-constant impedance, said matching network including a phase-magnitude error detector means providing a phase error signal and a magnitude error signal related respectively to impedance phase angle error and impedance magnitude error, and said matching network comprising at least a first variable impedance device having a driven element for varying the impedance thereof and a second variable impedance device having a driven element for varying the impedance thereof;

the method comprising;

supplying said phase and said magnitude error signals to a fuzzy logic controller, wherein each of said error signal signals has a magnitude and direction, ;

applying each of said phase and magnitude error signal signals to a fuzzy logic inference function based on a number of overlapping fuzzy sets, and where the a value of each of said phase and magnitude error signal signals enjoys membership in one or more fuzzy sets;

applying fuzzy logic rules to said phase and magnitude error signals according to the said one or more fuzzy sets for which said first and second phase and magnitude error signals enjoy membership;

obtaining drive signal values based on said fuzzy logic rules for each of said phase and magnitude error signals;

weighting said drive signal values according to the respective one or more fuzzy sets inference functions for which said phase and magnitude error signals enjoy membership; and

combining said weighted drive signal values to produce an output drive signal for the the driven element of said first variable impedance device driven element .

2. Fuzzy logic method of tuning an RF matching network according to claim 1, further comprising:

obtaining additional drive signal values based on additional fuzzy logic rules for each of said first and second phase and magnitude error signals;

weighting said additional drive signal values according to additional respective fuzzy inference functions; and

combining such said weighted additional drive signal values to produce an output drive signal for the driven element of said second variable impedance device driven element .

3. Fuzzy logic method for tuning an RF matching network according to claim 2, wherein said fuzzy logic rules and said additional fuzzy logic rules comprise a matrix of N×

M drive signal values, where N is the number of fuzzy sets of said phase error signal and M is the number of fuzzy sets of said magnitude error signal, and each of said drive signal value values and said additional drive signal values corresponds to a given set of said phase signal and a given set of said magnitude error signal.

4. Fuzzy logic method of tuning an RF matching network according to claim 1 wherein said number of overlapping fuzzy sets being are centered respectively about zero, a medium positive value, a medium negative value, a high positive value, and a high negative value.

5. A fuzzy logic controller for tuning an RF a radio frequency (RF) matching network, wherein said matching network is positioned between a source of applied RF power at a given frequency and at a given impedance, and an RF load having a non-constant impedance, said matching network including a phase-magnitude error detector means providing a phase error signal and a magnitude error signal related respectively to impedance phase angle error and impedance magnitude error, and said matching network comprising at least a first variable impedance device having a driven element for varying the impedance thereof and a second variable impedance device having a driven element for varying the impedance thereof;

the fuzzy logic controller comprising;

an input means receiving values of said phase and magnitude error signals;

means for applying the values of said phase and magnitude error signals to a fuzzy logic inference function based on a number of overlapping fuzzy sets, and where a the values value of each of said phase and magnitude error signals enjoy membership in one or more fuzzy sets;

means for applying fuzzy logic rules to said phase and magnitude error signals according to fuzzy sets for which said error signals enjoy membership;

means for obtaining drive signal values according to said fuzzy logic rules for each set of said fuzzy sets for which said error signals enjoy membership;

means for weighting said drive signal values according to the respective fuzzy inference functions for the values of said phase and magnitude error signals; and

means for combining said weighted drive signal values to produce an output drive signal for said first variable impedance device driven element.

6. Fuzzy logic controller according to claim 5, further comprising:

means for obtaining additional drive signal values based on additional fuzzy logic rules for each of said phase and magnitude error signals;

means for weighting said additional drive signal values according to additional respective fuzzy inference functions; and

means for combining such said weighted additional drive signal values to produce an output drive signal for said second variable impedance device driven element.

7. Fuzzy logic method of tuning a tunable RF radio frequency (RF) device of the type having an input at which is applied RF power at a given frequency and at a given impedance, and an output, including an error detector means providing a first error signal and a second error signal, and said tunable RF means device including at least a first variable impedance device having a driven element for varying the impedance thereof and a second variable impedance device having a driven element for varying the impedance thereof;

the method comprising;

supplying said first and said second error signals to a fuzzy logic controller, wherein each of said first and said second error signal signals has a magnitude and direction, ;

applying each of said first and said second error signal signals to a fuzzy logic inference function based on a number of overlapping fuzzy sets, and generating a membership value that corresponds to the an amount of overlapping membership of the error signal value signals in one or more fuzzy sets;

applying a plurality of fuzzy logic rules to said first and second error signals according to the fuzzy sets for which said first and second error signals enjoy membership;

obtaining a plurality of drive signal values based on said plurality of fuzzy logic rules for each of said first and second error signals;

weighting said drive signal values according to the respective membership values for said first and second error signals; and

combining said weighted drive signal values to produce an output drive signal for said first variable impedance having said first variable impedance device driven element.

8. Fuzzy logic method of tuning a tunable RF device according to claim 7, further comprising:

obtaining a plurality of additional drive signal values based on additional fuzzy logic rules for each of said first and second error signals;

weighting said additional drive signal values according to a plurality of additional respective fuzzy inference functions; and

combining such weighted additional drive signal values to produce an output drive signal for said second variable impedance device driven element.

9. An electrical network comprising:

a radio frequency (RF) generator for generating an RF signal, the RF generator having a source impedance;

a load receiving the RF signal, the RF signal providing a driving energy to the load, the load having a variable load impedance;

a matching network interposed between the RF generator and the load, the matching network having a variable network impedance, the matching network detecting at least one of an impedance phase and an impedance magnitude error and generating at least one of a respective phase error signal and a magnitude error signal, the matching network varying at least one of the impedance phase and the impedance magnitude error in order to vary the network impedance;

a fuzzy inference module receiving the at least one of the respective phase and magnitude error signals and defining a membership value that varies in accordance with membership in at least one fuzzy set; and

a controller receiving the at least one respective phase error signal and magnitude error signal, the controller applying fuzzy logic rules to the at least one of the respective impedance phase error signal and the impedance magnitude error signal according to the fuzzy sets for which said error signals enjoy membership in order to generate at least one control signal to vary the network impedance, thereby matching the source impedance and the load impedance.

10. The network of claim 9 wherein the controller further comprises a rules module having a set of rules applied in accordance with the membership value, the rules module generating at least one fuzzy output.

11. The network of claim 10, wherein the controller further comprises a defuzzification module, the defuzzification module converting the at least one fuzzy output to the at least one control signal.

12. The network of claim 9 wherein the matching network further comprises at least one of a variable capacitance and a variable inductance.

13. The network of claim 9, wherein the matching network further comprises a circuit for varying the network impedance.

14. The network of claim 9 wherein the load is a RF plasma chamber.

15. An electrical network comprising:

a radio frequency (RF) generator for generating an RF signal, the RF generator having a source impedance;

a load receiving the RF signal, the RF signal providing a driving energy to the load, the load having a variable load impedance;

a matching network interposed between the RF generator and the load, the matching network having a variable network impedance, the matching network detecting at least one network parameter and generating at least one sensed signal, the matching network varying the network impedance in order to match the variable load impedance and the source impedance, wherein the at least one sensed signal comprises at least one of an impedance phase error signal and an impedance magnitude error signal;

a fuzzy inference module receiving the at least one sensed signal and defining a membership value that varies in accordance with membership in at least one fuzzy set; and

a controller receiving the at least one sensed signal, the controller applying fuzzy logic rules to the at least one sensed signal according to the fuzzy sets for which said phase and magnitude error signals enjoy membership in order to generate at least one control signal to vary the network impedance, thereby matching the source impedance and the load impedance.

16. The network of claim 15 wherein the controller further comprises a rules module having a set of rules applied in accordance with the membership value, the rules module generating at least one fuzzy output.

17. The network of claim 15 wherein the controller further comprises a defuzzification module, the defuzzification module converting at least one fuzzy output to the at least one control signal.

18. The network of claim 15 wherein the matching network includes at least one of a variable capacitance and a variable inductance.

19. The network of claim 15 wherein the matching network further comprises a circuit for varying the network inductance.

20. The network of claim 15 wherein the matching network further comprise a circuit for varying the network impedance.

21. The network of claim 15, wherein the load is a RF plasma chamber.

22. A method of tuning a radio frequency (RF) impedance matching network having an input which receives RF power and an output which applies the power to a RF load, the matching network having a variable impedance, comprising the steps of:

determining an impedance phase error and an impedance magnitude error and generating a corresponding phase error signal and a corresponding magnitude error signal;

applying the impedance phase and impedance magnitude errors to a fuzzy logic inference function, the phase and magnitude error signals each having at least one respective membership value in at least one fuzzy set; and

applying fuzzy logic rules to the impedance phase and impedance magnitude error signals according to the fuzzy sets for which said error signals enjoy membership to generate fuzzy output signals based upon the phase and the magnitude error signals and generating a control signal to adjust the variable impedance of the matching network.

23. The method of claim 22 wherein the step of applying fuzzy logic further comprises applying logic rules to the at least one respective membership value to generate at least one respective fuzzy output value.

24. The method of claim 22, wherein the step of applying logic rules further comprises the step of weighting at least one respective fuzzy output value according to the at least one respective membership value.

25. The method of claim 24 wherein the step of applying logic rules further comprises the step of combining said weighted at least one respective fuzzy output values to produce the control signal.

26. The method of claim 22, wherein the logic rules comprise a matrix of N×

M fuzzy output values, where N is the number of fuzzy sets of a first sensed signal and M is the number of fuzzy sets of a second sensed signal, and each fuzzy output value corresponds to a predetermined set of the first sensed signal and a predetermined set of the second sensed signal.

27. The method of claim 22 wherein the at least one fuzzy set comprises a plurality of fuzzy sets centered respectively about zero, a medium positive value, a medium negative value, a high positive value, and a high negative value.

28. A method of tuning a radio frequency (RF) impedance matching network having an input which receives RF power and an output which applies the power to a RF load, the matching network having a variable impedance, comprising the steps of:

determining a network parameter and generating a corresponding sensed signal that varies in accordance with the network parameter;

applying the corresponding sensed signal to a fuzzy logic inference function, the corresponding sensed signal having at least one respective membership value in at least one fuzzy set; and

applying fuzzy logic rules to the corresponding sensed signal according to fuzzy sets for which said sensed signal enjoys membership;

generating fuzzy output signals based upon the corresponding sensed signal; and

generating a control signal to adjust the variable impedance of the matching network based upon the fuzzy output signals.

29. The method of claim 28 wherein the step of applying fuzzy logic rules further comprises applying logic rules to the at least one respective membership value to generate at least one respective fuzzy output value.

30. The method of claim 28, wherein the step of applying fuzzy logic rules further comprises the step of weighting at least one respective fuzzy output value according to the at least one respective membership value.

31. The method of claim 30 wherein the step of applying fuzzy logic rules further comprises the step of combining said weighted at least one respective fuzzy output value to produce the control signal.

32. The method of claim 28, wherein the fuzzy logic rules comprise a matrix of N×

M fuzzy output values, where N is the number of fuzzy sets of the corresponding sensed signal and M is the number of fuzzy sets of a second sensed signal, and each fuzzy output value corresponds to a predetermined set of the sensed signal and a predetermined set of the second sensed signal.

33. The method of claim 28 wherein the at least one fuzzy set comprises a plurality of fuzzy sets centered respectively about zero, a medium positive value, a medium negative value, a high positive value, and a high negative value.