用强化学习优化芯片制造设备级产能，提升1.8%吞吐量
Learning to Optimize Capacity Planning in Semiconductor Manufacturing
- 基于异构图神经网络的策略模型，直接捕捉设备间复杂关系
- 在最大测试场景中，吞吐量与周期时间分别提升约1.8%
- 适合关注制造系统智能调度的研究者与工业界工程师
在制造领域，产能规划是根据可变需求分配生产资源的过程。当前半导体制造行业通常采用启发式规则来优先处理任务，例如未来变更清单中涉及的设备和工艺专有化。然而，尽管具有可解释性，启发式方法难以应对流程中复杂的相互作用，这些作用可能逐步导致瓶颈形成。本文提出一种基于神经网络的机器级产能规划模型，采用深度强化学习进行训练。通过使用异构图神经网络表示策略，模型能够直接捕捉设备与加工步骤之间的多样化关系，实现主动决策。为确保充分的可扩展性以应对海量可能的机器级操作，我们采取了多种措施。评估结果涵盖英特尔的小规模Minifab模型及SMT2020基准测试的初步实验。在最大测试场景中，训练出的策略使吞吐量和周期时间均提升约1.8%。
原文摘要 · Abstract (English)
In manufacturing, capacity planning is the process of allocating production resources in accordance with variable demand. The current industry practice in semiconductor manufacturing typically applies heuristic rules to prioritize actions, such as future change lists that account for incoming machine and recipe dedications. However, while offering interpretability, heuristics cannot easily account for the complex interactions along the process flow that can gradually lead to the formation of bottlenecks. Here, we present a neural network-based model for capacity planning on the level of individual machines, trained using deep reinforcement learning. By representing the policy using a heterogeneous graph neural network, the model directly captures the diverse relationships among machines and processing steps, allowing for proactive decision-making. We describe several measures taken to achieve sufficient scalability to tackle the vast space of possible machine-level actions. Our evaluation results cover Intel's small-scale Minifab model and preliminary experiments using the popular SMT2020 testbed. In the largest tested scenario, our trained policy increases throughput and decreases cycle time by about 1.8% each.
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