  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.
