Modern semiconductor manufacturing involves intricate production processes consisting of hundreds of operations, which can take several months from lot release to completion. The high-tech machines used in these processes are diverse, operate on individual wafers, lots, or batches in multiple stages, and necessitate product-specific setups and specialized maintenance procedures. This situation is different from traditional job-shop scheduling scenarios, which have less complex production processes and machines, and mainly focus on solving highly combinatorial but abstract scheduling problems. In this work, we address the scheduling of realistic semiconductor manufacturing processes by modeling their specific requirements using hybrid Answer Set Programming with difference logic, incorporating flexible machine processing, setup, batching and maintenance operations. Unlike existing methods that schedule semiconductor manufacturing processes locally with greedy heuristics or by independently optimizing specific machine group allocations, we examine the potentials of large-scale scheduling subject to multiple optimization objectives.

翻译：现代半导体制造涉及由数百道工序组成的复杂生产流程，从晶圆批次释放到制造完成可能耗时数月。这些流程中使用的高科技设备种类繁多，可在多个阶段对单个晶圆、晶圆批次或批量进行操作，并且需要针对特定产品的配置和专门的维护程序。这一情况不同于传统的作业车间调度场景——后者生产流程与设备复杂性较低，主要侧重于解决高度组合但抽象的调度问题。在本研究中，我们采用结合差分逻辑的混合回答集编程对实际半导体制造过程进行建模，纳入灵活的机器加工、配置、批处理和维护操作，从而解决其调度问题。与现有方法通过贪婪启发式算法或独立优化特定设备组分配来局部调度半导体制造过程不同，我们探讨了在多重优化目标下进行大规模调度的潜力。