Abstract
Speeding up the simulation of discrete-event wafer-fabrication models is essential for fast decision-making to handle unexpected events in smart semiconductor manufacturing because decision-parameter optimization requires repeated simulation execution based on the current manufacturing situation. In this paper, we present a runtime abstraction-level conversion approach for discrete-event fab models to gain simulation speedup. During the simulation, if the fab's machine group model reaches a steady state, then the proposed method attempts to substitute this group model with a mean-delay model (MDM) as a high abstraction level model. The MDM abstracts detailed event-driven operations of subcomponents in the group into an average delay based on the queuing modeling, which can guarantee acceptable accuracy in predicting the performance of steady-state queuing systems. To detect the steadiness, the proposed abstraction-level converter (ALC) observes the queuing parameters of low-level groups to identify the statistical convergence of each group's work-in-progress (WIP) level. 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 MDM. When the ALC detects lot-arrival changes or any wafer processing status change (e.g., a machine-down), the high-level model is switched back to its corresponding low-level group model. During high-to-low level conversion, the ALC generates dummy wafer-lot events to re-initialize the machine states. The proposed method was applied to various case studies of wafer-fab systems and achieved simulation speedups up to about 4 times with 0.6 to 8.3% accuracy degradations.
Highlights
For smart manufacturing in wafer-fabrication systems, physical manufacturing-execution systems (MESs) collaborate with monitoring and controlling facilities (MCFs) to respond to rapid changes in production plans or machines’ statuses
This paper proposes a framework that abstracts detailed event-driven operations of the fab’s machine-group DE models into statistical mean-delay models (MDMs) when the group models turn into a steady state
If a bottleneck group adopts the WP policy, ηi can initialize the sfb based on a steady-state workload of the machine group
Summary
For smart manufacturing in wafer-fabrication (wafer-fab) systems, physical manufacturing-execution systems (MESs) collaborate with monitoring and controlling facilities (MCFs) to respond to rapid changes in production plans or machines’ statuses. B. PROPOSED ABSTRACTION-LEVEL CONVERSION CONCEPT During the simulation of DE fab models, wafer-lot events (e ) flow across multiple machine groups according to the required process sequence in their machines. Considering the dynamic e -flow change and other purposes, such as the abstraction-level synchronization among machine-sharing groups and the queuing-parameter sampling for the convergence and divergence test, we defined the system events for the ALC as follows. CONVERGENCE DETECTION TOWARD STEADY STATE AT LOW ABSTRACTION LEVEL At the low level, ALCs receive outgoing events of e from their current groups They extract the information of 1/λ, tw, td , and lot-type values from the event. ALCs attempt to detect whether the WIP levels in their machine groups reach a steady state based on the queuing-parameter observations using two consecutive samples.
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