Process system health index and method of using the same

• US 20040259276A1
• Filed: 03/26/2004
• Published: 12/23/2004
• Est. Priority Date: 05/16/2003
• Status: Active Grant

• Current Assignee: Tokyo Electron Limited
• Original Assignee: Tokyo Electron Limited
• Inventors: Lam, Hieu A., Yue, Hongyu
• Granted Patent: US 7,713,760 B2
• Time in Patent Office: Days
• US Class Current: 438/5
• CPC Class Codes: H01J 37/32935 Monitoring and controlling tubes by information coming from the object and/or discharge
• CPC Class Codes: H01J 37/3299 Feedback systems
• CPC Class Codes: H01L 21/67253 Process monitoring, e.g. flow or thickness monitoring
• CPC Class Codes: H01L 22/20 Sequence of activities consisting of a plurality of measurements, corrections, marking or sorting steps
• CPC Class Codes: H01L 2924/00 Indexing scheme for arrangements or methods for connecting or disconnecting semiconductor or solid-state bodies as covered by H01L24/00
• CPC Class Codes: H01L 2924/0002 Not covered by any one of groups H01L24/00, H01L24/00 and H01L2224/00

Abstract

A method of monitoring a processing system for processing a substrate during the course of semiconductor manufacturing is described. The method comprises acquiring data from the processing system for a plurality of observations. It further comprises constructing a principal components analysis (PCA) model from the data, wherein a weighting factor is applied to at least one of the data variables in the acquired data. The PCA mode is utilized in conjunction with the acquisition of additional data, and at least one statistical quantity is determined for each additional observation. Upon setting a control limit for the processing system, the at least one statistical quantity is compared with the control limit for each additional observation. When, for example, the at least one statistical quantity exceeds the control limit, a fault for the processing system is detected.

Claims

1. A method of monitoring a processing system for processing a substrate during the course of semiconductor manufacturing comprising:

acquiring data from said processing system for a plurality of observations, said data comprising a plurality of data variables;

determining one or more principal components of said data for said plurality of observations using principal components analysis;

weighting at least one of said plurality of data variables during said principal components analysis;

acquiring additional data from said processing system;

determining at least one statistical quantity from one or more scores calculated from a projection of said additional data onto said one or more principal components;

determining a control limit for said at least one statistical quantity; and

comparing said at least one statistical quantity to said control limit.

2. The method as recited in claim 1, wherein a process fault has occurred when said at least one statistical quantity exceeds said control limit.

3. The method as recited in claim 1, wherein said 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, a process step duration, focus ring RF hours, an optical emission spectrum, and RF harmonics

4. The method as recited in claim 1, wherein said data comprises at least one of an instantaneous value, a time average, a standard deviation, a third moment, a fourth moment, and a variance.

5. The method as recited in claim 1, wherein said statistical quantity comprises at least one of a distance to model parameter (DModX), and a Hotelling T² parameter.

6. The method as recited in claim 1, wherein said determining at least one statistical quantity further comprises a back-projection of said one or more scores with said one or more principal components to determine one or more residual errors.

7. The method as recited in claim 6, wherein said back-projection of said one or more scores with said one or more principal components comprises matrix multiplication.

8. The method as recited in claim 1, wherein said projection of said additional data onto said one or more principal components comprises matrix multiplication.

9. The method as recited in claim 1, wherein said weighting at least one of said plurality of data variables comprises applying a weighting factor.

10. The method as recited in claim 9, wherein said weighting factor is determined from at least one of a data standard deviation (S_o), a desired standard deviation of said data variable (S_d), a relative importance of said variable (f), and a data resolution (R).

11. The method as recited in claim 1, wherein said weighting at least one of said plurality of data variables comprises applying a group scaling method.

12. The method as recited in claim 1, further comprising:

accessing at least one of said data, said additional data, said at least one statistical quantity, and said control limit via at least one of an intranet, and an internet.

13. A processing system for processing a substrate during the course of semiconductor manufacturing comprising:

a process tool; and

a process performance monitoring system coupled to said process tool comprising a plurality of sensors coupled to said process tool, and a controller coupled to said plurality of sensors and said process tool, wherein said controller comprises

means for acquiring data from said plurality of sensors for a plurality of observations, said data comprising a plurality of data variables;

means for determining one or more principal components of said data for said plurality of observations using principal components analysis;

means for weighting at least one of said plurality of data variables during said principal components analysis;

means for acquiring additional data from said plurality of sensors;

means for determining at least one statistical quantity from one or more scores calculated from a projection of said additional data onto said one or more principal components;

means for determining a control limit for said at least one statistical quantity; and

means for comparing said at least one statistical quantity to said control limit.

14. The processing system as recited in claim 13, further comprising:

means for accessing at least one of said data, said additional data, said at least one statistical quantity, and said control limit.

15. The processing system as recited in claim 14, wherein said means for accessing comprises at least one of an intranet, and an internet.

16. A processing performance monitoring system to monitor a processing system for processing a substrate during the course of semiconductor manufacturing comprising:

a plurality of sensors coupled to said processing system; and

a controller coupled to said plurality of sensors and said processing system, wherein said controller comprises

means for acquiring data from said plurality of sensors for a plurality of observations, said data comprising a plurality of data variables;

means for determining one or more principal components of said data for said plurality of observations using principal components analysis;

means for weighting at least one of said plurality of data variables during said principal components analysis;

means for acquiring additional data from said plurality of sensors;

means for determining at least one statistical quantity from one or more scores calculated from a projection of said additional data onto said one or more principal components;

means for determining a control limit for said at least one statistical quantity; and

means for comparing said at least one statistical quantity to said control limit.

17. The process performance monitoring system as recited in claim 16, further comprising:

means for accessing at least one of said data, said additional data, said at least one statistical quantity, and said control limit.

18. The process performance monitoring system as recited in claim 17, wherein said means for accessing comprises at least one of an intranet, and an internet.