Predicting Time-to-Failure of Plasma Etching Equipment using Machine Learning-Reference-Cited by-同舟云学术

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Predicting Time-to-Failure of Plasma Etching Equipment using Machine Learning

Published:2019-06 Issue: Volume: Page:
ISSN:
Container-title:2019 IEEE International Conference on Prognostics and Health Management (ICPHM)
language:
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Author:

Jalali Anahid¹,Heistracher Clemens¹,Schindler Alexander¹,Haslhofer Bernhard¹,Nemeth Tanja²,Glawar Robert²,Sihn Wilfried²,De Boer Peter³

Affiliation:

1. Austrian Institute of Technology, Vienna, Austria

2. Fraunhofer Austria, Vienna, Austria

3. Infineon Technologies Austria AG, Villach, Austria

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8809669/8819370/08819404.pdf?arnumber=8819404

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Data Cleansing With Minimum Distortion for ML-Based Equipment Anomaly Detection;IEEE Transactions on Semiconductor Manufacturing;2023-11

2. Feature Extraction for Predictive Maintenance for Semiconductor Plasma Etching Equipment;2023 62nd Annual Conference of the Society of Instrument and Control Engineers (SICE);2023-09-06

3. Binary-Convolution Data-Reduction Network for Edge–Cloud IIoT Anomaly Detection;Electronics;2023-07-26

4. Towards big industrial data mining through explainable automated machine learning;The International Journal of Advanced Manufacturing Technology;2022-02-10

5. Operations Management in Semiconductor and Computing Technology Industries: Capacity, Outsourcing, and Production;Creating Values with Operations and Analytics;2022

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