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Deep reinforcement learning for semiconductor production scheduling

 
: Waschneck, Bernd; Reichstaller, Andre; Belzner, Lenz; Altenmüller, Thomas; Bauernhansl, Thomas; Knapp, Alexander; Kyek, Andreas

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Institute of Electrical and Electronics Engineers -IEEE-; Semiconductor Equipment and Materials International -SEMI-, San Jose/Calif.:
29th Annual SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2018 : April 30, 2018-May 3, 2018, Saratoga Springs, NY
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-3748-7
ISBN: 978-1-5386-3749-4
pp.301-306
Advanced Semiconductor Manufacturing Conference (ASMC) <29, 2018, Saratoga Springs/NY>
English
Conference Paper
Fraunhofer IPA ()
GSaME; Fertigungsplanung; maschinelles Lernen; Halbleiterfertigung; maschinelles Lernen

Abstract
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.

: http://publica.fraunhofer.de/documents/N-506965.html