Fault prognosis for batch production based on percentile measure and gamma process: Application to semiconductor manufacturing. (December 2016)

• Record Type: Journal Article
• Authors: Nguyen, T.B. Lien; Djeziri, Mohand; Ananou, Bouchra; Ouladsine, Mustapha; Pinaton, Jacques
• Is Part Of: Journal of process control. Volume 48(2016:Dec.)
• Page Start: 72
• Page End: 80
• Publication Date: 2016-12
• Subjects: RUL remaining useful life -- CI confidence interval -- pdf probability density function -- HI health index -- EMD empirical mode decomposition -- DWT discrete wavelet transform -- PECVD plasma enhanced chemical vapour deposition -- SACVD sub-atmospheric chemical vapour deposition; Batch manufacturing processes -- Fault prognosis -- Multidimensional data processing -- Percentile measure -- Stochastic process; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2016.10.003
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract : Highlights: A new data-driven prognostics method is developed for batch production with three steps. In preprocessing step, a novel method for the raw health index preprocessing is developed which allows to extract different monotonic profiles. In fault prognostic step, the RUL is estimated with an aggregate probability density function. The application results on industrial datasets highlight the effectiveness of the proposed method. Abstract: Batch manufacturing processes (BMP) play an important role in many production industries, such as in semiconductor, electronic and pharmaceutical industries. They generally exhibit some batch-to-batch or unit-to-unit variations due to many reasons such as variations in impurities and deviations of the process variables from their trajectories. The process monitoring for these systems has been considered as rather fault diagnosis than as fault prognosis, this latter has received scarce attention in the literature. This paper presents a data-driven prognostic method for BMP organized in three steps. The first step allows to reduce the data size and to extract a raw health index which represents the operating state of the system. In the second step, variations in the health index are processed by the percentile measure which is use in a way that gives rise to monotonic profiles. In the third step, these profiles are modelled by gamma process as it is the most appropriate for the stochastic modelling of monotonic and gradual deterioration. The remaining useful life (RUL) is then estimated using an aggregate probability density function (pdf) with a confidence interval (CI) that ensures the safety margins in industry. Finally, the proposed method is applied on semiconductor manufacturing equipment with two industrial datasets provided by STMicroelectronics.