TY - GEN
T1 - A Reinforcement Learning Approach for Improved Photolithography Schedules
AU - Zhang, Tao
AU - Kabak, Kamil Erkan
AU - Heavey, Cathal
AU - Rose, Oliver
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - A Reinforcement Learning (RL) model is applied for photolithography schedules with direct consideration of reentrant visits. The photolithography process is mainly regarded as a bottleneck process in semiconductor manufacturing, and improving its schedules would result in better performances. Most RL-based research do not consider revisits directly or guarantee convergence. A simplified discrete event simulation model of a fabrication facility is built, and a tabular Q-learning agent is embedded into the model to learn through scheduling. The learning environment considers states and actions consisting of information on reentrant flows. The agent dynamically chooses one rule from a pre-defined rule set to dispatch lots. The set includes the earliest stage first, the latest stage first, and 8 more composite rules. Finally, the proposed RL approach is compared with 7 single and 8 hybrid rules. The method presents a validated approach in terms of overall average cycle times.
AB - A Reinforcement Learning (RL) model is applied for photolithography schedules with direct consideration of reentrant visits. The photolithography process is mainly regarded as a bottleneck process in semiconductor manufacturing, and improving its schedules would result in better performances. Most RL-based research do not consider revisits directly or guarantee convergence. A simplified discrete event simulation model of a fabrication facility is built, and a tabular Q-learning agent is embedded into the model to learn through scheduling. The learning environment considers states and actions consisting of information on reentrant flows. The agent dynamically chooses one rule from a pre-defined rule set to dispatch lots. The set includes the earliest stage first, the latest stage first, and 8 more composite rules. Finally, the proposed RL approach is compared with 7 single and 8 hybrid rules. The method presents a validated approach in terms of overall average cycle times.
UR - http://www.scopus.com/inward/record.url?scp=85185383512&partnerID=8YFLogxK
U2 - 10.1109/WSC60868.2023.10408616
DO - 10.1109/WSC60868.2023.10408616
M3 - Conference contribution
AN - SCOPUS:85185383512
T3 - Proceedings - Winter Simulation Conference
SP - 2136
EP - 2147
BT - 2023 Winter Simulation Conference, WSC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 Winter Simulation Conference, WSC 2023
Y2 - 10 December 2023 through 13 December 2023
ER -