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

页码：301 / 306

页数：6

共 50 条

[31] Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning
Ko, Byungchan
Ok, Jungseul
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 35, NEURIPS 2022, 2022,
[32] A framework for scheduling in cloud manufacturing with deep reinforcement learning
Liu, Yongkui
Zhang, Lin
Wang, Lihui
Xiao, Yingying
Xu, Xun
Wang, Mei
2019 IEEE 17TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN), 2019, : 1775 - 1780
[33] Cellular Network Traffic Scheduling with Deep Reinforcement Learning
Chinchali, Sandeep
Hu, Pan
Chu, Tianshu
Sharma, Manu
Bansal, Manu
Misra, Rakesh
Pavone, Marco
Katti, Sachin
THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2018, : 766 - 774
[34] Adaptive DAG Tasks Scheduling with Deep Reinforcement Learning
Wu, Qing
Wu, Zhiwei
Zhuang, Yuehui
Cheng, Yuxia
ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2018, PT II, 2018, 11335 : 477 - 490
[35] Assessing the value of deep reinforcement learning for irrigation scheduling
Kelly, T. D.
Foster, T.
Schultz, David M.
SMART AGRICULTURAL TECHNOLOGY, 2024, 7
[36] Task Scheduling in Cloud Using Deep Reinforcement Learning
Swarup, Shashank
Shakshuki, Elhadi M.
Yasar, Ansar
12TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT) / THE 4TH INTERNATIONAL CONFERENCE ON EMERGING DATA AND INDUSTRY 4.0 (EDI40) / AFFILIATED WORKSHOPS, 2021, 184 : 42 - 51
[37] Geometric deep reinforcement learning for dynamic DAG scheduling
Grinsztajn, Nathan
Beaumont, Olivier
Jeannot, Emmanuel
Preux, Philippe
2020 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), 2020, : 258 - 265
[38] Beam Hopping Scheduling Based on Deep Reinforcement Learning
Deng, Huimin
Ying, Kai
Gui, Lin
2023 INTERNATIONAL CONFERENCE ON FUTURE COMMUNICATIONS AND NETWORKS, FCN, 2023,
[39] Decentralized Scheduling for Cooperative Localization With Deep Reinforcement Learning
Peng, Bile
Seco-Granados, Gonzalo
Steinmetz, Erik
Frohle, Markus
Wymeersch, Henk
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2019, 68 (05) : 4295 - 4305
[40] Multi-path Scheduling with Deep Reinforcement Learning
Molla Rosello, Marc
2019 EUROPEAN CONFERENCE ON NETWORKS AND COMMUNICATIONS (EUCNC), 2019, : 400 - 405

← 1 2 3 4 5 →