A real-time wafer lithography area scheduling approach integrating transformer with double deep Q-network

Lithography area scheduling is challenging due to its dynamic, real-time, multi-constraint and multi-objective characteristics. To address this issue, this paper proposes a lithography area scheduling method integrating Transformer with double deep Q-network (ITDDQN). A double deep Q-network model is constructed to make multi-objective real-time decisions on dynamic events in the lithography area. A feature enhancement module is proposed to improve the double deep Q-network’s global optimisation capability. The unconstrained rules are designed to enhance the practicability of the scheduling approach. Experiments demonstrate that ITDDQN outperforms traditional reinforcement learning methods and heuristic rules in terms of makespan and wafer on-time delivery ratio.