DocumentCode :
614947
Title :
Deploying an Equipment Health monitoring dashboard and assessing predictive maintenance
Author :
Moyne, James ; Iskandar, Jimmy ; Hawkins, Parris ; Furest, Avi ; Pollard, Bryan ; Walker, Toysha ; Stark, Dylan
Author_Institution :
Appl. Mater.-Appl. Global Services, Santa Clara, CA, USA
fYear :
2013
fDate :
14-16 May 2013
Firstpage :
105
Lastpage :
110
Abstract :
Predictive maintenance (PdM) is cited by the ITRS as a critical technology to incorporate into production over the next five years to reduce unscheduled downtime and cycle time, maintain high quality, and reduce cost. Equipment Health monitoring (EHM) is a companion to PdM that provides a tracking indication of equipment health. The industry needs to deploy and assess PdM and EHM capabilities to determine best practices for the industry and the potential for cost reduction through deployment of these technologies. Applied Materials is working with both Micron Technology and Intel Corporation on EHM and PdM development and assessment projects, partially funded by ISMI. As a result of these projects a portable EHM solution has been designed and demonstrated that can be deployed “out-of-the-box” to track equipment health, but also updated as more information is ascertained on specific smart health indicators. Also, preliminary PdM results in both projects reveals an ability to predict key downtime event including particle monitor, throttle valve and liquid flow failures. Results were achieved on both CVD and etch tool types.
Keywords :
chemical vapour deposition; condition monitoring; cost reduction; failure analysis; maintenance engineering; nanotechnology; production equipment; quality control; CVD; EHM development; ISMI; ITRS; Intel Corporation; Micron Technology; PdM development; cost reduction; cycle time reduction; equipment health monitoring dashboard; equipment health tracking; etch tool types; key downtime event prediction; liquid flow failure; particle monitor failure; portable EHM solution; predictive maintenance assessment; quality maintenance; specific smart health indicators; throttle valve failure; unscheduled downtime reduction; Data models; Fault detection; Maintenance engineering; Metrology; Monitoring; Predictive models; Valves; Advanced Process Control; Equipment Health Monitoring; PHM; PdM; Predictive Maintenance; Prognostics Health Management; unscheduled downtime;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Semiconductor Manufacturing Conference (ASMC), 2013 24th Annual SEMI
Conference_Location :
Saratoga Springs, NY
ISSN :
1078-8743
Print_ISBN :
978-1-4673-5006-8
Type :
conf
DOI :
10.1109/ASMC.2013.6552784
Filename :
6552784
Link To Document :
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