Controlling a material processing tool and performance data

• US 7,167,766 B2
• Filed: 06/27/2003
• Issued: 01/23/2007
• Est. Priority Date: 06/28/2002
• Status: Active Grant

• Current Assignee: Tokyo Electron Limited
• Original Assignee: Tokyo Electron Limited
• Inventors: Lam, Hieu A., Yue, Hongyu
• Primary Examiner(s): Bahta; Kidest
• Application Number:
  US10/512,863
• Publication Number:
  US 20050234574A1
• Time in Patent Office: 1,306 Days
• Field of Search: 700/121, 700/33, 700/268, 700/44, 700/109, 700/32, 700/96, 700/108, 700/29, 430/30, 702/84, 702/182, 438/795, 438/692, 156/345, 156/12, 156/13
• US Class Current: 700/96
• CPC Class Codes: G05B 19/41865 characterised by job scheduling, process planning, material flow
• CPC Class Codes: G05B 2219/31443 Keep track of nc program, recipe program
• CPC Class Codes: G05B 2219/32366 Line performance evaluation
• CPC Class Codes: G05B 2219/45031 Manufacturing semiconductor wafers
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

According to an embodiment of the present invention, a material processing systeme (1) including a process tool (10) and a process performance control system (100). The process performance control system (100) includes a process performance controller (55) coupled to the process tool (10), where the process performance controller (55) includes a process performance prediction model (110), a process recipe correction filter (120), a process controller (130), and process performance model correction algorithm (150). The process performance prediction model (110) is configured to receive tool data from a plurality of sensors coupled to process tool (10) and to predict process performance data. The process recipe correction filter (120) is coupled to the process performance prediction model (110) and configured to receive predicted process performance data and generate a process recipe correction for run-to-run process control. The process controller (130) is coupled to the process recipe correction filter (120) and is configured to update a process recipe according to the process recipe correction.

Claims

1. A material processing system comprising:

a process tool; and

a process performance control system configured to control said process tool, said process performance control system comprises a process performance controller coupled to said process tool, wherein said process performance controller comprises a process performance prediction model configured to receive tool data from a plurality of sensors associated with said process tool and predict process performance data based on said tool data, a process recipe correction filter configured to receive predicted process performance data from said process performance prediction model and determine a process recipe correction based on said predicted process performance data and a known value of the process recipe parameter to be corrected, said known value being measured from a preceding process run, and a process controller configured to update a process recipe for said process tool using said process recipe correction.

2. The material processing system as recited in claim 1, wherein said process performance data comprises at least one of etch depth, average etch depth, mean etch depth, and etch depth range.

3. The material processing system as recited in claim 1, wherein said tool data comprises at least one of a capacitor position, a forward radio frequency (RF) power, a reflected RF power, a voltage, a current, a phase, an impedance, a RF peak-to-peak voltage, a RF self-induced direct current bias, a chamber pressure, a gas flow rate, a temperature, a backside gas pressure, a backside gas flow rate, an electrostatic clamp voltage, an electrostatic clamp current, a focus ring thickness, RF hours, focus ring RF hours, and optical emission data.

4. The material processing system as recited in claim 1, wherein said process performance prediction model comprises an output from a partial least squares analysis.

5. The material processing system as recited in claim 1, wherein said process recipe correction filter comprises an exponentially weighted moving average filter.

6. The material processing system as recited in claim 1, wherein said process recipe correction comprises an etch time.

7. The material processing system as recited in claim 1, wherein said process performance control system further comprises a metrology tool and a process performance model correction algorithm.

8. The material processing system as recited in claim 7, wherein said metrology tool is coupled to said process tool and said process performance controller, and configured to measure process performance data.

9. The material processing system as recited in claim 8, wherein said process performance model correction filter comprises a partial least squares algorithm that uses said measured process performance data and corresponding tool data.

31. The material processing system of claim 5, wherein said exponentially weighted moving average filter is configured to correct an etch time based on a measured etch time of an immediately preceding etch process, a predicted value for the etch time and a filter constant.

10. A process performance control system for controlling a process tool comprising:

a process performance controller capable of being coupled to said process tool, said process performance controller comprises a process performance prediction model configured to receive tool data from a plurality of sensors associated with said process tool and predict process performance data based on said tool data, a process recipe correction filter configured to receive predicted process performance data from said process performance prediction model and determine a process recipe correction based on said predicted process performance data and a known value of the process recipe parameter to be corrected, said known value being measured from a preceding process run, and a process controller configured to update a process recipe for said process tool using said process recipe correction.

11. The process performance control system as recited in claim 10, wherein said process performance data comprises at least one of mean etch depth and etch depth range.

12. The process performance control system as recited in claim 10, wherein said process performance prediction model is configured to receive tool data comprising at least one of a capacitor position, a forward RF power, a reflected RF power, a voltage, a current, a phase, an impedance, a RE peak-to-peak voltage, a RF self-induced DC bias, a chamber pressure, a gas flow rate, a temperature, a backside gas pressure, a backside gas flow rate, an electrostatic clamp voltage, an electrostatic clamp current, a focus ring thickness, RF hours, focus ring RF hours, and optical emission data.

13. The process performance control system as recited in claim 10, wherein said process performance prediction model comprises an output from a partial least squares analysis.

14. The process performance control system as recited in claim 10, wherein said process recipe correction filter comprises an exponentially weighted moving average filter.

15. The process performance control system as recited in claim 10, wherein said process recipe correction comprises an etch time, a RF power, a pressure, a flow rate, a concentration, and a temperature.

16. The process performance control system as recited in claim 10, wherein said process performance control system further comprises a metrology tool capable of being coupled to said process tool and coupled to said process performance controller, and a process performance model correction algorithm.

17. The process performance control system as recited in claim 16, wherein said metrology tool is configured to measure process performance data.

18. The process performance control system as recited in claim 17, wherein said process performance model correction algorithm employs partial least squares analysis using said measured process performance data and corresponding tool data.

32. The material processing system of claim 14, wherein said exponentially weighted moving average filter is configured to correct an etch time based on a measured etch time of an immediately preceding etch process, a predicted value for the etch time and a filter constant.

19. A material process system comprising:

a process tool; and

means for controlling said process tool, said means for controlling comprising means for predicting process performance data using tool data received from a plurality of sensors associated with said process tool, means for determining a process recipe correction using the predicted process performance data and a known value of the process recipe parameter to be corrected, the known value being measured from a preceding process run, and means for updating a process recipe for said process tool using said process recipe correction.

20. A method for controlling a process tool of a material processing system, the method comprising the steps of:

executing a first process in said process tool using a process recipe;

recording tool data for said first process, said tool data comprises a plurality of tool data parameters obtained from a plurality of sensors associated with the process tool;

predicting process performance data from said tool data for said first process using a process performance prediction model, said process performance data comprises one or more process performance data parameters;

determining a process recipe correction from said predicted process performance data and a known value of the process recipe parameter to be corrected, the known value being measured from a preceding process run using a process recipe correction filter;

updating said process recipe by incorporating said process recipe correction using a process controller coupled to said process tool; and

executing a second process in said process tool using said updated process recipe.

21. The method for controlling a process tool as recited in claim 20, wherein said method further comprises updating said process performance prediction model.

22. The method for controlling a process tool as recited in claim 21, wherein said updating said process performance prediction model comprises measuring process performance data for said first process in said process tool, and determining a correction to said process performance prediction model using said measured process performance data for said first process, said recorded tool data for said first process, and partial least squares analysis.

23. The method for controlling a process tool as recited in claim 20, wherein said process recipe comprises an etch time, a RF power, a pressure, a flow rate, a concentration, and a temperature.

24. The method for controlling a process tool as recited in claim 20, wherein said updated process recipe comprises an updated etch time.

25. The method for controlling a process tool as recited in claim 20, wherein said process performance data comprises at least one of mean etch depth and etch depth range.

26. The method for controlling a process tool as recited in claim 20, wherein said tool data comprises at least one of a capacitor position, a forward RE power, a reflected RF power, a voltage, a current, a phase, an impedance, a RE peak-to-peak voltage, a RE self-induced DC bias, a chamber pressure, a gas flow rate, a temperature, a backside gas pressure, a backside gas flow rate, an electrostatic clamp voltage, an electrostatic clamp current, a focus ring thickness, RE hours, focus ring RE hours, and optical emission data.

27. The method for controlling a process tool as recited in claim 20, wherein said process performance prediction model comprises an output from a partial least squares analysis.

28. The method for controlling a process tool as recited in claim 20, wherein said determining a process recipe comprises using an exponentially weighted moving average filter.

29. The method for controlling a process tool as recited in claim 20, wherein said method further comprises:

measuring process performance data using a metrology tool; and

correcting said process performance prediction model using said measured process performance data and said measured tool data.

30. The method for controlling a process tool as recited in claim 29, wherein said correcting said process performance prediction model comprises partial least squares analysis.

33. The method of claim 28, wherein said determining a process recipe comprises determining a corrected etch time based on a measured etch time of an immediately preceding etch process, a predicted value for the etch time and a filter constant.