Towards Bayesian network methodology for predicting the equipment health factor of complex semiconductor systems

This paper presents a general methodology to improve risk assessment in the specific workshops of semiconductor manufacturers. We are concerned, in this case, with the problem of equipment failures and drifts. These failures are generally observed, with delay, during the product metrology phase. To improve the reactivity of the control system, we propose a predictive approach based on the Bayesian technique. Increased use of these techniques is the result of the advantages obtained. This approach allows early action to maintain, for example, the equipment before it can drift. Also, our contribution consists in proposing a generic model to predict the equipment health factor (EHF), which will define decision support strategies on preventive maintenance to avoid unscheduled equipment downtime. Following the proposed methodology, a data extraction and processing prototype is also designed to identify the real failure modes which will instantiate the Bayesian model. EHF results are decision support elements. They can be further used to improve production performance: reduced cycle time, improved yield and enhanced equipment effectiveness.