Speeding up the simulation of discrete-event wafer fab models is essential because optimizing the scheduling and dispatching policies under various circumstances requires repeated evaluation of the decision candidates during parameter-space exploration. In this paper, we present a runtime abstraction-level conversion approach for discrete-event wafer-fabrication (wafer-fab) models to gain simulation speedup. During the simulation, if a machine group of the wafer fab models reaches a steady state, then the proposed approach attempts to substitute this group model with a mean-delay model (MDM) as a high abstraction level model. The MDM abstracts the detailed operations of the group's sub-component models into an average delay based on the queueing modeling, which can guarantee acceptable accuracy under steady state. The proposed abstraction-level converter (ALC) observes the queueing parameters of low-level groups to identify the convergence of each group's work-in-progress (WIP) level through a statistical test. When a group's WIP level is converged, the output-to-input couplings between the models are revised to change a wafer-lot process flow from the low-level group to a mean-delay model. When the ALC detects a divergence caused by a re-entrant flow or a machine-down, the high-level model is switched back to its corresponding low-level group model. The ALC then generates dummy wafer-lot events to synchronize the busyness of high-level steady state. The proposed method was applied to case studies of wafer-fab systems and achieves simulation speedup from 6.1 to 11.8 times with corresponding 2.5 to 5.9% degradation inaccuracy.