Deep Reinforcement Learning for Semiconductor Production Scheduling

被引：0

作者：

Waschneck, Bernd ^{[1
]}

Reichstaller, Andre ^{[2
]}

Belzner, Lenz ^{[3
]}

Altenmueller, Thomas ^{[4
]}

Bauernhansl, Thomas ^{[5
]}

Knapp, Alexander ^{[2
]}

Kyek, Andreas ^{[4
]}

机构：

[1] Univ Stuttgart, GSaME, Nobelstr 12, D-70569 Stuttgart, Germany

[2] Univ Augsburg, Inst Software & Syst Engn, Augsburg, Germany

[3] Lenz Belzner AI Consulting, Munich, Germany

[4] Infineon Technol AG, Campeon 1-12, D-85579 Neubiberg, Germany

[5] Fraunhofer Inst Mfg Engn & Automat IPA, Nobelstr 12, D-70569 Stuttgart, Germany

来源：

2018 29TH ANNUAL SEMI ADVANCED SEMICONDUCTOR MANUFACTURING CONFERENCE (ASMC) | 2018年

关键词：

Production Scheduling; Reinforcement Learning; Machine Learning; Semiconductor Manufacturing;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

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页码：301 / 306

页数：6

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