Abstract
In smart manufacturing, engineers typically analyze unexpected real-time problems using digitally cloned discrete-event (DE) models for wafer fabrication. To achieve a faster response to problems, it is essential to increase the speed of DE simulations because making optimal decisions for addressing the issues requires repeated simulations. This paper presents a hierarchical aggregation/disaggregation (A/D) method that substitutes complex event-driven operations with two-layered abstracted models—single-group mean-delay models (SMDMs) and multi-group MDMs (MMDMs)—to gain simulation speedup. The SMDM dynamically abstracts a DE machine group’s behaviors into observed mean-delay constants when the group converges into a steady state. The MMDM fast-forwards the input lots by bypassing the chained processing steps in multiple steady-state groups until it schedules the lots for delivery to subsequent unsteady groups after corresponding multi-step mean delays. The key component, the abstraction-level converter (ALC), has the roles of MMDM allocation, deallocation, extension, splitting, and controls the flow of each group’s input lot by deciding the destination DE model, SMDM, and MMDMs. To maximize the reuse of previously computed multi-step delays for the dynamically changing MMDMs, we propose an efficient method to manage the delays using two-level caches. Each steady-state group’s ALC performs statistical testing to detect the lot-arrival change to reactivate the DE model. However, fast-forwarding (FF) results in incorrect test results of the bypassed group’s ALCs due to the missed observations of the bypassed lots. Thus, we propose a method for test-sample reinitialization that considers the bypassing. Moreover, since a bypassed group’s unexpected divergence can change the multi-step delays of previously scheduled events, a method for examination of FF history is designed to trace the highly influenced events. This proposed method has been applied in various case studies, and it has achieved speedups of up to about 5.9 times, with 2.5 to 8.3% degradation in accuracy.
Highlights
Semiconductor manufacturers have applied industry 4.0-based smart manufacturing concepts to their waferfabrication plants for efficient manufacturing management [1]–[3]
We proposed an event-rescheduling method to cancel the previously forwarded events and make new forwarded events based on the new steady-state situations, using FF history tables
To reduce the redundant calculation of multi-step delays under the dynamic multi-group mean-delay models (MDMs) (MMDMs) extension, we proposed an efficient delay-management method using two-level caches
Summary
Semiconductor manufacturers have applied industry 4.0-based smart manufacturing concepts to their waferfabrication plants (commonly called fabs) for efficient manufacturing management [1]–[3]. In the smart fab case, if new operational parameter values vary outside the bound of the DoE coverages, caused by a production demand for new products, a deadline/priority change, or unexpected machine downs, surrogate models cannot guarantee satisfactory results. To resolve these static abstraction problems for various complex scenarios, we previously presented the conducted studies to adjust machine groups’ abstraction level in runtime adaptively [25], [26]. The new layer supporting the group bypassing reduces the number of events scheduled at the wafer-lot exchanges between steady-state groups, which results in more speed than the single-group A/D approach (that employs only SMDMs) does.
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