基于自监督和强化学习的半导体工厂调度
Semiconductor Fab Scheduling with Self-Supervised and Reinforcement Learning
摘要：
众所周知，半导体制造是复杂且昂贵的多步骤工艺，涉及在昂贵且数量有限的设备上的长序列操作。最近的芯片短缺及其影响凸显了半导体在全球供应链中的重要性，以及我们日常生活对半导体的依赖程度。由于投资成本、环境影响和建造新工厂所需的时间规模，在需求激增时很难提高产量。这项工作介绍了一种方法，成功地学习调度半导体制造设施更有效地使用深度强化和自监督学习。我们提出了第一个自适应调度方法来处理复杂的，连续的，随机的，动态的，现代半导体制造模型。我们的方法优于传统的分层调度策略通常用于半导体制造厂，大大减少每个订单的延误和时间，直到完成。因此，我们的方法在半导体制造过程中产生了更好的资源分配。
英文摘要：
Semiconductor manufacturing is a notoriously complex and costly multi-step process involving a long sequence of operations on expensive and quantity-limited equipment. Recent chip shortages and their impacts have highlighted the importance of semiconductors in the global supply chains and how reliant on those our daily lives are. Due to the investment cost, environmental impact, and time scale needed to build new factories, it is difficult to ramp up production when demand spikes. This work introduces a method to successfully learn to schedule a semiconductor manufacturing facility more efficiently using deep reinforcement and self-supervised learning. We propose the first adaptive scheduling approach to handle complex, continuous, stochastic, dynamic, modern semiconductor manufacturing models. Our method outperforms the traditional hierarchical dispatching strategies typically used in semiconductor manufacturing plants, substantially reducing each order's tardiness and time until completion. As a result, our method yields a better allocation of resources in the semiconductor manufacturing process.
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