View source for 20240055284. PREDICTIVE WAFER SCHEDULING FOR MULTI-CHAMBER SEMICONDUCTOR EQUIPMENT simplified abstract (Applied Materials, Inc.)

=PREDICTIVE WAFER SCHEDULING FOR MULTI-CHAMBER SEMICONDUCTOR EQUIPMENT=


==Organization Name==

[[:Category:Applied Materials, Inc.|Applied Materials, Inc.]]


[[Category:Applied Materials, Inc.]]

==Inventor(s)==

[[:Category:Adrian Rhee of Santa Clara CA (US)|Adrian Rhee of Santa Clara CA (US)]][[Category:Adrian Rhee of Santa Clara CA (US)]]

==PREDICTIVE WAFER SCHEDULING FOR MULTI-CHAMBER SEMICONDUCTOR EQUIPMENT - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240055284 titled 'PREDICTIVE WAFER SCHEDULING FOR MULTI-CHAMBER SEMICONDUCTOR EQUIPMENT

==Simplified Explanation==

The method described in the patent application involves identifying a set of wafers, determining if there is an idle wafer in the set that exceeds a predefined threshold duration, generating modified start times for the wafers if an idle wafer is found, and using a computer simulation to forecast processing using a wafer modification chamber and a wafer movement chamber based on the modified start times. The computer simulation utilizes a machine learning model trained on different durations for specific manufacturing tasks.

* Identifying a set of wafers associated with start times
* Checking for idle wafers exceeding a predefined threshold duration
* Modifying start times for idle wafers
* Using computer simulation to forecast processing using specific chambers
* Utilizing machine learning model trained on different durations for manufacturing tasks

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### Potential Applications
- Semiconductor manufacturing
- Wafer fabrication processes

### Problems Solved
- Optimizing production schedules
- Minimizing idle time in manufacturing processes

### Benefits
- Increased efficiency in manufacturing
- Improved resource utilization
- Enhanced forecasting accuracy


==Original Abstract Submitted==

a method includes identifying a set of wafers, wherein each wafer is associated with a respective start time of a set of start times, determining whether the set of wafers includes an idle wafer, in response to determining that the set of wafers includes an idle wafer that is idle for a duration that exceeds a predefined threshold value, generating a modified set of start times by modifying at least the start time for the idle wafer, and initiating a computer simulation forecasting processing of the set of wafers using a wafer modification chamber and a wafer movement chamber based on the modified set of start times. the computer simulation uses a machine learning model trained based on a first duration to perform a first manufacturing task using the wafer modification chamber and a second duration to perform a second manufacturing task using the wafer movement chamber.



[[Category:H01L21/67]]
[[Category:G05B19/4155]]
[[Category:G06N20/00]]