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Dependability of complex semiconductor systems: Learning Bayesian networks for decision support

The production of microelectronic components is characterized by an important complexity of production context, high technology renewal cadence, a strong customer requirement and an uncertain environment. This has an impact on the cycle time, costs and equipment efficiencies. This paper presents a general methodology to manage the risks of complex semiconductors systems. A literature review about process control, risk analysis methods and Bayesian networks is presented. A first structure of the predictive behavior model is proposed, this model is based on Bayesian learning methods.