A predictive maintenance system based on regularization methods for ion-implantation

Ion Implantation is one of the most sensitive processes in Semiconductor Manufacturing. It consists in impacting accelerated ions with a material substrate and is performed by an Implanter tool. The major maintenance issue of such tool concerns the breaking of the tungsten filament contained within the ion source of the tool. This kind of fault can happen on a weekly basis, and the associated maintenance operations can last up to 3 hours. It is important to optimize the maintenance activities by synchronizing the Filament change operations with other minor maintenance interventions. In this paper, a Predictive Maintenance (PdM) system is proposed to tackle such issue; the filament lifetime is estimated on a statistical basis exploiting the knowledge of physical variables acting on the process. Given the high-dimensionality of the data, the statistical modeling has been based on Regularization Methods: Lasso, Ridge Regression and Elastic Nets. The predictive performances of the aforementioned regularization methods and of the proposed PdM module have been tested on actual productive semiconductor data.