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.

中文翻译：

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用于智能半导体制造中加速离散事件仿真的自适应抽象级转换框架

加速离散事件晶圆制造模型的仿真对于快速决策以处理智能半导体制造中的突发事件至关重要，因为决策参数优化需要根据当前制造情况重复执行仿真。在本文中，我们提出了一种用于离散事件 fab 模型的运行时抽象级转换方法，以提高仿真速度。在仿真过程中，如果晶圆厂的机器组模型达到稳定状态，则所提出的方法尝试用平均延迟模型（MDM）代替该组模型作为高抽象级别模型。MDM基于排队建模将组内子组件的详细事件驱动操作抽象为平均延迟，这可以保证在预测稳态排队系统的性能时具有可接受的准确性。为了检测稳定性，所提出的抽象级转换器 (ALC) 观察低级组的排队参数，以识别每个组的在制品 (WIP) 级别的统计收敛性。当一个组的 WIP 级别收敛时，模型之间的输出到输入耦合会被修改，以将晶圆批次工艺流程从低级别组更改为 MDM。当 ALC 检测到批次到货变化或任何晶圆加工状态变化（例如，机器停机）时，高级模型会切换回其相应的低级组模型。在高电平到低电平转换期间，ALC 生成虚拟晶圆批次事件以重新初始化机器状态。