Thesis of Maxime Gasse

Statistical learning of Bayesian networks applied to process control in the semiconductor industry

Defense date: 13/01/2017

Advisor: Alexandre Aussem
Coadvisor: Haytham Elghazel


Design of algorithms for Bayesian networks structure learning in large data volumes. Application to process control in the semiconductor industry, with a view towards in-line equpments drift detection, which may have an impact on cycle duration, performance and manufacturing costs. This work is part of the Integrated Solutions for Agile Manufacturing in High-mix Semiconductor Fabs european project (INTEGRATE), which was initiated by the European Nanoelectronics Initiative Advisory Council (ENIAC) and is funded by the EU’s Seventh Framework Programme for Research (FP7).

Christophe GONZALESProfesseur(e)Université Paris 6Rapporteur(e)
Jose M. PEÑAProfesseur(e) associé(e)Linköping UniversityRapporteur(e)
Elisa FROMONTMaître de conférenceUniversité Jean MonnetExaminateur​(trice)
Willem WAEGEMANProfesseur(e)Ghent UniversityExaminateur​(trice)
Veronique DELCROIXMaître de conférenceUniversité de ValenciennesExaminateur​(trice)
Céline ROBARDETProfesseur(e)INSA LyonExaminateur​(trice)
Alexandre AUSSEMProfesseur(e)Université Lyon 1Directeur(trice) de thèse
Haytham ELGHAZELMaître de conférencePolytech LyonCo-directeur (trice)