A Reinforcement Learning Approach for Improved Photolithography Schedules

Tao Zhang, Kamil Erkan Kabak, Cathal Heavey, Oliver Rose

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2023 Winter Simulation Conference, WSC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2136-2147
Number of pages12
ISBN (Electronic)9798350369663
DOIs
Publication statusPublished - 2023
Event2023 Winter Simulation Conference, WSC 2023 - San Antonio, United States
Duration: 10 Dec 202313 Dec 2023

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736

Conference

Conference2023 Winter Simulation Conference, WSC 2023
Country/TerritoryUnited States
CitySan Antonio
Period10/12/2313/12/23

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