• 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