Tool Condition Diagnosis With a Recipe-Independent Hierarchical Monitoring Scheme

Jakey Blue (1) , Dietmar Gleispach , Agnès Roussy (1) , Scheibelhofer Peter

1 SFL-ENSMSE - Département Sciences de la Fabrication et Logistique

Tool condition evaluation and prognosis has been an arduous challenge in modern semiconductor manufacturing environment. More and more embedded and external sensors are installed to capture the genuine tool status for fault identification. Therefore, tool condition analysis based on real-time equipment data becomes not only promising but also more complex with the rapidly increased number of sensors. In this paper, the idea of Generalized Moving Variance (GMV) is employed to consolidate the pure variations within tool Fault Detection and Classification (FDC) data into one single indicator. A hierarchical monitoring scheme is developed to generate an overall tool indicator that can be coherently drilled down into the GMVs within functional sensor groups. Therefore, we will be able to classify excursions found in the overall tool condition into sensor groups and make the tool fault detection and identification more efficient.

Mots clés: Fault Detection and Classification FDC; Hotelling T2; Moving Variance and Covariance; Tool Condition Diagnosis

Domaines: Sciences de l'ingénieur [physics]; Statistiques [stat]; Micro et nanotechnologies/Microélectronique

Jakey Blue, Dietmar Gleispach, Agnès Roussy, Scheibelhofer Peter. Tool Condition Diagnosis With a Recipe-Independent Hierarchical Monitoring Scheme. IEEE Transactions on Semiconductor Manufacturing, 2013, 26 (1), pp.82-91. ⟨10.1109/TSM.2012.2230279⟩. ⟨hal-01079929⟩