Performing radio frequency matching control using a model-based digital twin
A method includes receiving, from one or more sensors, sensor data associated with manufacturing equipment and updating one or more values of a digital replica associated with the manufacturing equipment based on the sensor data. The digital replica comprises a model reflecting a virtual representation of physical elements and dynamics of how the manufacturing equipment operates. One or more outputs indicative of predictive data is obtained from the digital replica and, based on the predictive data, performance of one or more corrective actions associated with the manufacturing equipment is caused.
1. A method comprising:
receiving, from one or more sensors, sensor data associated with manufacturing equipment;
updating one or more values of a digital replica associated with the manufacturing equipment based on the sensor data, wherein the digital replica comprises a trained machine-learning_model reflecting a virtual representation of physical elements and dynamics of how the manufacturing equipment operates;
obtaining, from the digital replica, one or more outputs indicative of predictive data; and
causing, based on the predictive data, performance of one or more corrective actions associated with the manufacturing equipment.
2. The method of claim 1 , further comprising:
responsive to determining that digital replica fails to satisfy an accuracy threshold criterion based on the sensor data, performing an optimization of the digital replica.
3. The method of claim 1 , wherein updating the one or more values of the digital replica comprises:
updating a processing chamber model associated with the digital replica using sensor data from an output sensor, wherein the output sensor is associated with a matching network of the manufacturing equipment.
4. The method of claim 1 , wherein updating the one or more values of the digital replica comprises:
updating a matching network model associated with the digital replica using sensor data from an input sensor and an output sensor, wherein the input sensor and the output sensor are associated with a matching network of the manufacturing equipment.
5. The method of claim 1 , wherein the predictive data comprises one or more tune settings for one or more variable capacitors associated with a matching network of the manufacturing equipment.
6. The method of claim 1 , wherein the corrective action comprises adjusting one or more variable capacitors associated with a matching network of the manufacturing equipment based on the predictive data.
7. A system comprising:
a memory; and
a processing device, coupled to the memory, to:
receive, from one or more sensors, sensor data associated with manufacturing equipment;
update one or more values of a digital replica associated with the manufacturing equipment based on the sensor data, wherein the digital replica comprises a trained machine-learning_model reflecting a virtual representation of physical elements and dynamics of how the manufacturing equipment operates;
obtain, from the digital replica, one or more outputs indicative of predictive data; and
cause, based on the predictive data, performance of one or more corrective actions associated with the manufacturing equipment.
8. The system of claim 7 , wherein the processing device is further to:
responsive to determining that digital replica fails to satisfy an accuracy threshold criterion based on the sensor data, perform an optimization of the digital replica.
9. The system of claim 7 , wherein to update the one or more values of the digital replica, the processing device is further to:
update a processing chamber model associated with the digital replica using sensor data from an output sensor, wherein the output sensor is associated with a matching network of the manufacturing equipment.
10. The system of claim 7 , wherein to update the one or more values of the digital replica, the processing device is further to:
update a matching network model associated with the digital replica using trace data from an input impedance sensor and an output impedance sensor, wherein the input impedance sensor and the output impedance sensor are associated with a matching network of the manufacturing equipment.
11. The system of claim 7 , wherein the predictive data comprises one or more tune settings for one or more variable capacitors associated with a matching network of the manufacturing equipment.
12. The system of claim 7 , wherein the corrective action comprises adjusting one or more variable capacitors associated with a matching network of the manufacturing equipment based on the predictive data.
13. A non-transitory machine-readable storage medium storing instructions which, when executed cause a processing device to perform operations comprising:
receiving, from one or more sensors, sensor data associated with manufacturing equipment;
updating one or more values of a digital replica associated with the manufacturing equipment based on the sensor data, wherein the digital replica comprises a trained machine-learning model reflecting a virtual representation of physical elements and dynamics of how the manufacturing equipment operates;
obtaining, from the digital replica, one or more outputs indicative of predictive data; and
causing, based on the predictive data, performance of one or more corrective actions associated with the manufacturing equipment.
14. The non-transitory machine-readable storage medium of claim 13 , wherein the operations further comprise:
responsive to determining that digital replica fails to satisfy an accuracy threshold criterion based on the sensor data, performing an optimization of the digital replica.
15. The non-transitory machine-readable storage medium of claim 13 , wherein updating the one or more values of the digital replica comprises:
updating a processing chamber model associated with the digital replica using sensor data from an output sensor, wherein the output sensor is associated with a matching network of the manufacturing equipment.
16. The non-transitory machine-readable storage medium of claim 13 , wherein updating the one or more values of the digital replica comprises:
updating a matching network model associated with the digital replica using sensor data from an input sensor and an output sensor, wherein the input sensor and the output sensor are associated with a matching network of the manufacturing equipment.
17. The non-transitory machine-readable storage medium of claim 13 , wherein the predictive data comprises one or more tune settings for one or more variable capacitors associated with a matching network of the manufacturing equipment, and wherein the corrective action comprises adjusting one or more variable capacitors associated with a matching network of the manufacturing equipment based on the predictive data.