Applied Materials, Inc.

Manjunath Yedatore

Tony Davis

Michael Johnson

Shawn B Smith

John R Swyers

William R Clements, Iii

Deepak Bhatia

Brian P Grigsby

John Prescott

Hung J Pham

Donn Turner

A method and system for automated corrective and predictive maintenance is described herein. In one embodiment, a failure notification associated with a tool is received, wherein the failure notification indicates one or more failure events for the tool. Maintenance data associated with the tool is obtained, wherein the maintenance data includes one or more maintenance events for the tool. An optimal schedule is created for the tool by merging the failure notification and the maintenance data.

Automated corrective and predictive maintenance system

A server receives equipment data from third party support equipment during operation of the third party support equipment in a manufacturing environment. The server receives the equipment data via a first port on the third party support equipment. The third party support equipment communicates with a process tool via a second port on third party support equipment. The server determines a future state of the third party support equipment based on the equipment data and notifies a user of the future state of the third party support equipment.

Predictive maintenance for third party support equipment

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 the fab 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.

Method and apparatus for analyzing manufacturing data

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Austin, TX, United States of America

Manjunath Yedatore