A predictive maintenance system for integral type faults based on support vector machines: An application to ion implantation
Automation Science and Engineering（2013）
摘要
In semiconductor fabrication processes, effective management of maintenance operations is fundamental to decrease costs associated with failures and downtime. Predictive Maintenance (PdM) approaches, based on statistical methods and historical data, are becoming popular for their predictive capabilities and low (potentially zero) added costs. We present here a PdM module based on Support Vector Machines for prediction of integral type faults, that is, the kind of failures that happen due to machine usage and stress of equipment parts. The proposed module may also be employed as a health factor indicator. The module has been applied to a frequent maintenance problem in semiconductor manufacturing industry, namely the breaking of the filament in the ion-source of ion-implantation tools. The PdM has been tested on a real production dataset.
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关键词
failure analysis,ion implantation,maintenance engineering,production engineering computing,production equipment,semiconductor device manufacture,statistical analysis,support vector machines,equipment failure,integral type faults,ion implantation,machine downtime,machine usage,maintenance management,predictive maintenance system,semiconductor fabrication processes,semiconductor manufacturing industry,statistical methods,support vector machines,Classification Methods,Ion-Implantation,Predictive Maintenance,Semiconductor Manufacturing,Support Vector Machines
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