User configurable multivariate time series reduction tool control method

• US 6,442,445 B1
• Filed: 03/19/1999
• Issued: 08/27/2002
• Est. Priority Date: 03/19/1999
• Status: Expired due to Fees

• Current Assignee: GlobalFoundries, Inc.
• Original Assignee: International Business Machines Corporation
• Inventors: Surendra, Maheswaran, Ghias, Asif, Colt, John Z. Jr., Pascoe, Nancy T., Bunkofske, Raymond J., Taubenblatt, Marc A., McGill, James J.
• Primary Examiner(s): Picard, Leo
• Assistant Examiner(s): Garland, Steven R.
• Application Number:
  US09/272,434
• Time in Patent Office: 1,257 Days
• Field of Search: 700/108, 700/28, 700/51, 700/33, 700/34, 700/52, 700/67, 700/73, 700/79, 700/80, 700/121, 700/174, 700/175, 700/177, 702/179, 702/181, 702/182, 702/185, 430/30, 430/428
• US Class Current: 700/108
• CPC Class Codes: G05B 2219/45031 Manufacturing semiconductor wafers
• CPC Class Codes: G05B 23/0221 Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
• CPC Class Codes: H01L 22/20 Sequence of activities consisting of a plurality of measurements, corrections, marking or sorting steps

Abstract

A method and structure for controlling a manufacturing tool includes measuring different manufacturing parameters of the tool, transforming a plurality of time series of the manufacturing parameters into intermediate variables based on restrictions and historical reference statistics, generating a surrogate variable based on the intermediate variables, if the surrogate variable exceeds a predetermined limit, identifying a first intermediate variable, of the intermediate variables, that caused the surrogate variable to exceed the predetermined limit and identifying a first manufacturing parameter associated with the first intermediate variable, and inhibiting further operation of the tool until the first manufacturing parameter has been modified to bring the surrogate value within the predetermined limit.

Claims

1. A method of controlling a manufacturing tool, comprising:

measuring different manufacturing parameters of said tool;

transforming a plurality of time series of said manufacturing parameters into intermediate variables based on restrictions and historical reference statistics;

generating at least one surrogate variable based on said intermediate variables;

if said surrogate variable exceeds a predetermined limit, identifying a first intermediate variable, of said intermediate variables, that caused said surrogate variable to exceed said predetermined limit and identifying a first manufacturing parameter associated with said first intermediate variable; and

inhibiting further operation of said tool until said first manufacturing parameter has been modified to bring said surrogate value within said predetermined limit.

2. The method according to claim 1, wherein said transforming reduces a dimensionality of said time series of said manufacturing parameters.

3. The method according to claim 1, wherein said transforming includes one or more of selecting, modifying and combining said time series of said manufacturing parameters based on said restrictions and said historical reference statistics.

4. A method of controlling a manufacturing tool, comprising:

measuring different manufacturing parameters of said tool;

transforming a plurality of time series of said manufacturing parameters into intermediate variables based on restrictions and historical reference statistics;

generating at least one surrogate variable based on said intermediate variables;

if said surrogate variable exceeds a predetermined limit, identifying a first intermediate variable, of said intermediate variables, that caused said surrogate variable to exceed said predetermined limit and identifying a first manufacturing parameter associated with said first intermediate variable; and

inhibiting further operation of said tool until said first manufacturing parameter has been modified to bring said surrogate value within said predetermined limit, wherein said surrogate variable comprises T², where;

$T^2={\left(\frac{\left(x-\stackrel{\_}{x}\right)}{\mathrm{\sigma <br><br>}}\right)}^T<br><br>R^{-1}<br><br>\left(\frac{\left(x-\stackrel{\_}{x}\right)}{\mathrm{\sigma <br><br>}}\right)=z^T<br><br>R^{-1}<br><br>z,$

where x comprises said intermediate variables, {overscore (x)} comprises a historical sensor value, a comprises a historical standard deviation sensor value, R<sup>−

1</sup> comprises an inverse correlation matrix and z comprises mean and standard deviation normalized values.

5. The method according to claim 4, wherein x and σ

are user adjustable.

6. A method of controlling a manufacturing tool comprising:

measuring different manufacturing parameters of said tool;

transforming a plurality of time series of said manufacturing, parameters into intermediate variables based on restrictions and historical reference statistics;

generating at least one surrogate variable based on said intermediate variables;

if said surrogate variable exceeds a predetermined limit, identifying a first intermediate variable of said intermediate variables, that caused said surrogate variable to exceed said predetermined limit and identifying a first manufacturing parameter associated with said first intermediate variable; and

inhibiting further operation of said tool until said first manufacturing parameter has been modified to bring said surrogate value within said predetermined limit, wherein said restrictions comprise user-defined, batch-specific restrictions.

7. A computer implemented method of controlling a tool using multivariate analysis, comprising:

measuring a set of tool variables with sensors;

storing values of said variables in a computer connected to receive data from said sensors, said computer generating a surrogate variable having a value representative of whether said tool is in control, said surrogate variable being a function of a time series of manufacturing parameters transferred into intermediate variables;

determining if said value of said surrogate variable is outside of a predetermined limit, said computer identifying an out-of-control intermediate variable of said intermediate variables that primarily contributed to said value of said surrogate variable;

identifying an out-of-control tool variable of said tool variables that primarily contributed to said out-of-control intermediate variable; and

inhibiting further operation of said tool until said out-of-control tool variable has been modified to bring said surrogate value within said predetermined limit.

8. The method according to claim 7, further comprising transforming a plurality of time series of said tool variables into said intermediate variables based on restrictions and historical reference statistics.

9. The method according to claim 8, wherein said transforming reduces a dimensionality of said time series of said tool variables.

10. The method according to claim 8, wherein said transforming includes one or more of selecting, modifying and combining said time series of said tool variables based on said restrictions and said historical reference statistics.

11. A computer implemented method of controlling a tool using multivariate analysis, comprising:

measuring a set of tool variables with sensors;

storing values of said variables in a computer connected to receive data from said sensors, said computer generating a surrogate variable having a value representative of whether said tool is in control, said surrogate variable being a function of a plurality of intermediate variables;

determining if said value of said surrogate variable is outside of a predetermined limit, said computer identifying an out-of-control intermediate variable of said intermediate variables that primarily contributed to said value of said surrogate variable;

identifying an out-of-control tool variable of said tool variables that primarily contributed to said out-of-control intermediate variable; and

inhibiting further operation of said tool until said out-of-control tool variable has been modified to bring said surrogate value within said predetermined limit, wherein said surrogate variable comprises T², where;

$T^2={\left(\frac{\left(x-\stackrel{\_}{x}\right)}{\mathrm{\sigma <br><br>}}\right)}^T<br><br>R^{-1}<br><br>\left(\frac{\left(x-\stackrel{\_}{x}\right)}{\mathrm{\sigma <br><br>}}\right)=z^T<br><br>R^{-1}<br><br>z,$

where x comprises said intermediate variables, {overscore (x)} comprises a historical sensor value, σ

comprises a historical standard deviation sensor value, R<sup>−

1</sup> comprises an inverse correlation matrix and z comprises mean and standard deviation normalized values.

12. The method according to claim 11, wherein x and σ

are user adjustable.

13. A computer implemented method of controlling a tool using multivariate analysis, comprising:

measuring a set of tool variables with sensors;

storing values of said variables in a computer connected to receive data from said sensors, said computer generating a surrogate variable having a value representative of whether said tool is in control, said surrogate variable being a function of a plurality of intermediate variables;

determining if said value of said surrogate variable is outside of a predetermined limit, said computer identifying an out-of-control intermediate variable of said intermediate variables that primarily contributed to said value of said surrogate variable;

identifying an out-of-control tool variable of said tool variables that primarily contributed to said out-of-control intermediate variable;

inhibiting further operation of said tool until said out-of-control tool variable has been modified to bring said surrogate value within said predetermined limit; and

transforming a plurality of time series of said tool variables into said intermediate variables based on restrictions and historical reference statistics, wherein said restrictions comprise user-defined, batch-specific restrictions.

14. A computer implemented method of controlling a tool using multivariate analysis, comprising:

measuring a set of tool variables with sensors;

storing values of said variables in a computer connected to receive data from said sensors, said computer generating a surrogate variable having a value representative of whether said tool is in control, said surrogate variable being a function of a plurality of intermediate variables;

determining if said value of said surrogate variable is outside of a predetermined limit, said computer identifying an out-of-control intermediate variable of said intermediate variables that primarily contributed to said value of said surrogate variable;

identifying an out-of-control tool variable of said tool variables that primarily contributed to said out-of-control intermediate variable;

inhibiting further operation of said tool until said out-of-control tool variable has been modified to bring said surrogate value within said predetermined limit wherein said surrogate variable comprises T², where;

$T^2={\left(\frac{\left(x-\stackrel{\_}{x}\right)}{\mathrm{\sigma <br><br>}}\right)}^T<br><br>R^{-1}<br><br>\left(\frac{\left(x-\stackrel{\_}{x}\right)}{\mathrm{\sigma <br><br>}}\right)=z^T<br><br>R^{-1}<br><br>z,$

where x comprises said intermediate variables, {overscore (x)} comprises a historical sensor value, σ

comprises a historical standard deviation sensor value, R<sup>−

1</sup> comprises an inverse correlation matrix and z comprises mean and standard deviation normalized values,

inputting reference statistics and a transformation program into said computer;

inputting raw data from process runs into said computer;

using said transformation program to compute said intermediate variables from said raw data; and

applying said reference statistics to a scaled version of a Hotelling function and computing said T² for said surrogate variable based on said intermediate variables.