关键词: 晶圆批处理调度, 并行批处理机, 动态调度, 逆向强化学习, 强化学习, 生产周期, 重入加工

Abstract: Aiming at the scheduling problem of wafer batch processing equipment,to minimize the production cycle as the optimization goal,considering the characteristics of dynamic wafer arrival,reentry processing and incompatibility constraints,a scheduling optimization method of wafer batch processing equipment combining Inverse Reinforcement Learning and Reinforcement Learning (IRL-RL) was proposed.According to the processing characteristics of batch processing equipment,the problem was decomposed into two sub-problems:group batch and batch assignment.Due to the complex internal correlation characteristics of sub-problems,the internal mechanism of wafer batch processing equipment scheduling was unclear,and the design of global reward function was difficult,reverse reinforcement learning is introduced to guide the design of reward function.According to the reentry processing characteristics of wafer lot,the key state variables of expected flow time and remaining waiting time were designed.The urgency of the task and the difference in production preparation time caused by process type switching was taken into account of the batch assignment agent to make comprehensive decisions meet the incompatibility constraint of batch equipment process type.By designing the nonlinear characteristics of the key parameters of the reward function,the complex rheological relationship between the number of remaining machining layers and the expected flow time of wafer lot was explained.The experimental data of 24 sets of standard examples showed that the optimization results and computational efficiency of the IRL-RL algorithm were better than those of the general reinforcement learning algorithm and better rules.According to the data of enterprise instance,the wafer production cycle was shortened by 15%.

Key words: wafer batch scheduling, parallel batch processor, dynamic scheduling, inverse reinforcement learning, reinforcement learning, production cycle, reentrant processing