Method and apparatus for equipment diagnostics and recovery with self-learning

• US 20040199361A1
• Filed: 04/01/2003
• Published: 10/07/2004
• Est. Priority Date: 04/01/2003
• Status: Abandoned Application

• Current Assignee: Ching-Shan Lu, Shi-Rung Chen
• Original Assignee: Ching-Shan Lu, Shi-Rung Chen
• Inventors: Lu, Ching-Shan, Chen, Shi-Rung
• Application Number:
  US10/405,007
• Publication Number:
  US 20040199361A1
• Time in Patent Office: Days
• US Class Current: 702/183
• CPC Class Codes: G05B 19/41875 characterised by quality surveillance of production
• CPC Class Codes: G05B 2219/31263 Imbedded learning for planner, executor, monitor, controller and evaluator
• CPC Class Codes: G05B 2219/32214 Display on screen what fault and which tool and what order to repair fault
• CPC Class Codes: G05B 2219/45031 Manufacturing semiconductor wafers
• CPC Class Codes: H01L 21/67288 Monitoring of warpage, curvature, damage, defects or the like
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

System and method for semiconductor fabrication equipment diagnostics and recovery with self-learning. A preferred embodiment comprises an abnormal inference engine (for example, abnormal inference engine 205) coupled to a data source (for example, data source 210) that includes sensors and measuring equipment. The abnormal inference engine receives data associated with a trigger event and evaluates the data to satisfy one or more diagnostic rules. The satisfied diagnostic rules are associated with root causes, which in turn, are diagnosed to provide a remedy. The remedy along with pertinent data is displayed on a display (for example, display terminal 215). Feedback (provided by an engineer via a data terminal (for example, abnormal handle graphical user interface 220)) related to the remedy is used to help make adjustments to the abnormal inference engine and to assist in the diagnosis of future trigger events, providing a measure of self-learning.

Claims

1. A method for diagnosing an abnormality comprising:

receiving data from a trigger event;

evaluating satisfaction of diagnostic rules using received data;

determining a root cause if at least one diagnostic rule is satisfied; and

displaying the root cause if at least one diagnostic rule is satisfied, else displaying the received data.

2. The method of claim 1, wherein the trigger event is a result of a monitored value that is outside of a specified range.

3. The method of claim 2, wherein the monitored value is from a piece of manufacturing equipment.

4. The method of claim 3, wherein the piece of manufacturing equipment is used in semiconductor manufacturing.

5. The method of claim 1, wherein a diagnostic rule is a unique combination of the received data.

6. The method of claim 5, wherein a diagnostic rule is satisfied when each piece of received data in its unique combination evaluated true.

7. The method of claim 5, wherein there are a plurality of diagnostic rules and each diagnostic rule is a unique combination of the received data.

8. The method of claim 1, wherein there is a plurality of diagnostic rules, and wherein there is a root cause associated with each diagnostic rule.

9. The method of claim 1, wherein more than one diagnostic rule can be satisfied with the received data.

10. The method of claim 1 further comprising diagnosing the root cause for a remedy if at least one diagnostic rule is satisfied.

11. The method of claim 10, wherein the displaying includes displaying the remedy.

12. A method for self-learning diagnostics comprising:

receiving data from a trigger event;

evaluating satisfaction of diagnostic rules using received data;

if at least one diagnostic rule is satisfied, then determining a root cause;

diagnosing a remedy for the root cause;

displaying the remedy;

receiving feedback information about the remedy; and

modifying the satisfied diagnostic rule with the feedback information.

13. The method of claim 12, wherein the feedback information provides a rating on the effectiveness of the remedy.

14. The method of claim 12, wherein the feedback information is also used to modify the diagnosing.

15. The method of claim 12, wherein the feedback information is provided by a user.

16. An equipment diagnosis system comprising:

a data source to provide information from sensors and measuring equipment;

an inference engine coupled to the data source, the inference engine containing circuitry to evaluate the information provided by the data source and to diagnose a root cause from the information;

a display coupled to the inference engine, the display to interface the inference engine with a user; and

a database coupled to the inference engine, the database to store information provided by the data source and the diagnosis generated by the inference engine.

17. The equipment diagnostic system of claim 16, wherein the data source provides information only when a trigger event occurs.

18. The equipment diagnostic system of claim 17, wherein a trigger event is when a sensor detects a value outside of a specified range.

19. The equipment diagnostic system of claim 16 further comprising a user interface coupled to the inference engine, the user interface to permit a user to input information.

20. The equipment diagnostic system of claim 19, wherein the input information includes feedback information regard the effectiveness of a diagnosis provided by the inference engine.

21. The equipment diagnosis system of claim 19, wherein the input information is used to make adjustments to the inference engine.