Deep reinforcement learning for semiconductor production scheduling

Despite producing tremendous success stories by identifying cat videos [1] or solving computer as well as board games [2], [3], the adoption of deep learning in the semiconductor industry is moderatre. In this paper, we apply Google DeepMind's Deep Q Network (DQN) agent algorithm for Reinforcem...

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Bibliographic Details
Main Authors: Waschneck, Bernd, Reichstaller, Andre, Belzner, Lenz, Altenmuller, Thomas, Bauernhansl, Thomas, Knapp, Alexander, Kyek, Andreas
Format: Conference Proceeding
Language:English
Subjects:
Dispatching
Job shop scheduling
Machine learning
Neural networks
Optimization
Production facilities
Production Scheduling
Reinforcement Learning
Semiconductor Manufacturing
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Summary:Despite producing tremendous success stories by identifying cat videos [1] or solving computer as well as board games [2], [3], the adoption of deep learning in the semiconductor industry is moderatre. In this paper, we apply Google DeepMind's Deep Q Network (DQN) agent algorithm for Reinforcement Learning (RL) to semiconductor production scheduling. In an RL environment several cooperative DQN agents, which utilize deep neural networks, are trained with flexible user-defined objectives. We show benchmarks comparing standard dispatching heuristics with the DQN agents in an abstract frontend-of-line semiconduc­tor production facility. Results are promising and show that DQN agents optimize production autonomously for different targets.
ISSN:2376-6697
DOI:10.1109/ASMC.2018.8373191