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.

翻译：半导体制造是一个公认的复杂且成本高昂的多步骤过程，涉及在昂贵且数量受限的设备上执行一系列长周期操作。近期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。由于建厂所需的投资成本、环境影响和时间跨度，当需求激增时难以快速提升产能。本文提出一种方法，利用深度强化学习和自监督学习成功学习如何更高效地调度半导体制造设施。我们首次提出一种自适应调度方法，以应对现代半导体制造中复杂、连续、随机、动态的建模需求。该方法优于半导体制造工厂中传统使用的分层调度策略，显著缩短了每个订单的延迟时间和完工周期。因此，我们的方法在半导体制造过程中实现了更优的资源分配。