Method and apparatus for analyzing manufacturing data

• US 20030061212A1
• Filed: 07/12/2002
• Published: 03/27/2003
• Est. Priority Date: 07/16/2001
• Status: Active Grant

• Current Assignee: Applied Materials Incorporated
• Original Assignee: Applied Materials Incorporated
• Inventors: Clements, William R. III, Grigsby, Brian P., Pham, Hung J., Davis, Tony L., Yedatore, Manjunath S., Smith, Shawn B.
• Granted Patent: US 6,965,895 B2
• Time in Patent Office: Days
• US Class Current: 707/6
• CPC Class Codes: G06F 16/2453 Query optimisation
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: G06F 16/254 Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access
• CPC Class Codes: Y10S 707/99936 Pattern matching access
• CPC Class Codes: Y10S 707/99942 Manipulating data structure, e.g. compression, compaction, compilation
• CPC Class Codes: Y10S 707/99945 Object-oriented database structure processing

Abstract

A method for data mining information obtained in an integrated circuit fabrication factory (“fab”) that includes steps of: (a) gathering data from the fab from one or more of systems, tools, and databases that produce data in thefab or collect data from the fab; (b) formatting the data and storing the formatted data in a source database; (c) extracting portions of the data for use in data mining in accordance with a user specified configuration file; (d) data mining the extracted portions of data in response to a user specified analysis configuration file; (e) storing results of data mining in a results database; and (f) providing access to the results.

Claims

1. A method for data mining information obtained in an integrated circuit fabrication factory (“

fab”

) that includes steps of;

gathering data from the fab from one or more of systems, tools, and databases that produce data in the fab or collect data from the fab;

formatting the data and storing the formatted data in a source database;

extracting portions of the data for use in data mining in accordance with a user specified configuration file;

data mining the extracted portions of data in response to a user specified analysis configuration file;

storing results of data mining in a results database; and

providing access to the results.

2. The method of claim 1 wherein the step of gathering includes gathering in real time over a network on demand or on a scheduled basis.

3. The method of claim 1 wherein the step of providing access includes providing access over a network.

4. The method of claim 3 wherein the step of providing access over a network includes providing access using a browser.

5. The method of claim 1 wherein the step of gathering occurs in real time, and the steps of formatting the data and storing the formatted data in a source database occurs in real time.

6. The method of claim 1 wherein the step of gathering includes gathering data transmitted in customer-dictated and/or tool-dictated formats.

7. The method of claim 1 wherein the step of gather includes gathering encrypted data.

8. The method of claim 1 wherein the step of formatting the data includes formatting the data into a leveling scheme including WidgetID, Where?, When?, What?, and Value levels.

9. The method of claim 8 wherein Widget ID is identified by one or more of:

LotID, WaferID, SlotID, ReticleID, DieID, and Sub-die x,y Cartesian coordinate.

10. The method of claim 8 wherein Where? is identified by one or more of:

process flow/assembly line manufacturing step; and

sub-step.

11. The method of claim 8 wherein When? is identified by one or more of date/time of the measurement.

12. The method of claim 1 wherein the step of formatting includes translating temporal-based operating condition data generated within processing tools in the fab during wafer processing into key integrated circuit specific statistics in accordance with a configuration file.

13. The method of claim 1 wherein the step of storing further includes extracting data from the source database and storing the extracted data in a hybrid database that comprises a relational database component and a file system component.

14. The method of claim 13 wherein the relational database component uses a hash-index algorithm to create access keys to discrete data stored in the file system component.

15. The method of claim 14 wherein the step of extracting includes utilizing a hash-join algorithm to accumulate data from the hybrid database.

16. The method of claim 14 wherein the step of extracting includes obtaining a hypercube definition using the configuration file;

using the hypercube definition to create a vector cache definition, and creating a vector cache of information.

17. The method of claim 16 wherein the step of creating a vector cache of information includes:

(a) retrieving a list of files and data elements identified by the vector cache definition from the relational database component using hash-index keys;

(b) retrieving the files from the file system component; and

(c) populating the vector cache with data elements identified in the vector cache definition.

18. The method of claim 17 wherein the step of extracting further includes generating hypercubes from the vector cache information using the hypercube definition.

19. The method of claim 1 wherein the step of data mining includes utilizing one or more of self organized map data mining, Rules Induction data mining, data mining to correlate numerical data to categorical or attributes data, and data mining to correlate categorical or attributes data to numerical data.

20. The method of claim 1 wherein the step of data mining includes self organized map data mining to form clusters, Map Matching analysis on output from the SOM data mining to perform cluster matching, Rules Induction data mining on output from the SOM data mining analysis a rules explanations of clusters, correlating categorical data to numerical data on output from the Rules Induction data mining;

and , correlating numerical data to categorical data on output from the Map Matching data mining.

21. The method of claim 20 wherein the step of SOM data mining automatically organizes data, and identifies separate and dominant data clusters that represent different “

fab issues”

within a data set, and Map Matching analysis describes an “

of interest”

variable in terms of any historical data that is known to be impacting the behavior of the “

of interest”

variable on a cluster by cluster basis.