Semiconductor Fab Scheduling with Self-Supervised and Reinforcement Learning
This work addresses the complex and costly scheduling challenges in semiconductor manufacturing, which is critical for global supply chains, but it appears to be an incremental improvement over existing methods.
The paper tackles the problem of scheduling semiconductor manufacturing facilities more efficiently by introducing an adaptive scheduling approach using deep reinforcement and self-supervised learning, which outperforms traditional hierarchical dispatching strategies by substantially reducing order tardiness and completion time.
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.