Autonomous Scheduling in Semiconductor Back-End Manufacturing

• Published in: 2022 Winter Simulation Conference (WSC) pp. 3489 - 3500
• Main Authors: Adan, Jelle, Akcay, Alp, Fowler, John, Albers, Marc, Geurtsen, Michael
• Format: Conference Proceeding
• Language: English
• Published: IEEE 11-12-2022
• Subjects: Data models; Dynamic scheduling; Job shop scheduling; Production; Schedules; Semiconductor device measurement; Semiconductor device modeling
• ISSN: 1558-4305
• DOI: 10.1109/WSC57314.2022.10015252

Production scheduling decisions have a large impact on efficiency and output, especially in complex environments such as those with sequence- and machine-dependent setup times. In practice, these scheduling problems are usually solved for a fixed time ahead. In semiconductor back-end manufacturing, given the dynamics of the environment, it is commonly observed that a schedule is no longer optimal soon after it is made. Here, we propose time-based rescheduling heuristics that can mitigate the effect of these deviations from the schedules. We build a simulation model to represent the dynamics of the shop floor as well as its interaction with the upper management level that decides how orders are released. The simulation model, which is built and validated using real-world data, enables us to evaluate the performance of the rescheduling heuristics. By comparing the results to the case without rescheduling, it is shown that rescheduling can significantly improve relevant performance measures.