Production-Level Artificial Intelligence Applications in Semiconductor Manufacturing-Reference-Cited by-同舟云学术

Production-Level Artificial Intelligence Applications in Semiconductor Manufacturing

Published:2021-12-12 Issue: Volume: Page:
ISSN:
Container-title:2021 Winter Simulation Conference (WSC)
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Author:

Chien Chen-Fu¹,Ehm Hans²,Fowler John³,Monch Lars⁴,Wu Cheng-Hung⁵

Affiliation:

1. National Tsing Hua University,Department of Industrial Engineering and Engineering Management,Hsinchu,TAIWAN,30013

2. Infineon Supply Chain Innovation Infineon Technologies AG,Neubiberg,GERMANY,85579

3. Arizona State University,Department of Supply Chain Management,Tempe,AZ,USA,85249

4. University of Hagen,Department of Mathematics and Computer Science,Hagen,GERMANY,58097

5. College of Management National Taiwan University,Dist Taipei City,TAIWAN,106

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9715283/9715284/09715399.pdf?arnumber=9715399

Reference36 articles.

1. Order Release Planning with Predictive Lead Times: a Machine Learning Approach;schneckenreither;International Journal of Production Research,2020

2. Bi-criteria Parallel Batch Machine Scheduling to Minimize Total Weighted Tardiness and Electricity Cost;rocholl;Journal of Economics and Business,2020

3. Heuristic approaches for master planning in semiconductor manufacturing

4. Exploit the Value of Production Data to Discover Opportunities for Saving Power Consumption of Production Tools

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1. A Novel Out-of-Control Action Plan (OCAP) for Optimizing Efficiency and Quality in the Wafer Probing Process for Semiconductor Manufacturing;Sensors;2024-08-07

2. An AI-Enabled Framework for Smart Semiconductor Manufacturing;2024 Design, Automation & Test in Europe Conference & Exhibition (DATE);2024-03-25

3. Industry 5.0: Research Areas and Challenges With Artificial Intelligence and Human Acceptance;IEEE Industrial Electronics Magazine;2024

4. Production-Level Artificial Intelligence Applications in Semiconductor Supply Chains;IEEE Transactions on Semiconductor Manufacturing;2023-11

5. Leveraging Machine Learning for Capacity and Cost on a Complex Toolset: A Case Study;IEEE Transactions on Semiconductor Manufacturing;2023-11