Data Cleansing With Minimum Distortion for ML-Based Equipment Anomaly Detection-Reference-Cited by-同舟云学术

Data Cleansing With Minimum Distortion for ML-Based Equipment Anomaly Detection

Published:2023-11 Issue:4 Volume:36 Page:506-514
ISSN:0894-6507
Container-title:IEEE Transactions on Semiconductor Manufacturing
language:
Short-container-title:IEEE Trans. Semicond. Manufact.

Author:

Hsieh Yun-Che¹,Chen Chieh-Yu¹,Liao Da-Yin²^ORCID,Lin Kuan-Chun¹,Chang Shi-Chung³^ORCID

Affiliation:

1. Department of Electrical Engineering, National Taiwan University, Taipei City, Taiwan

2. Straight & Up Intelligent Innovations Group Company, San Jose, CA, USA

3. Department of Electrical Engineering, Graduate Institute of Industrial Engineering, Graduate Institute of Communication Engineering, National Taiwan University, Taipei City, Taiwan

Funder

Ministry of Science and Technology, Taiwan

Taiwan Semiconductor Manufacturing Company Ltd.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Condensed Matter Physics,Electronic, Optical and Magnetic Materials

Link

http://xplorestaging.ieee.org/ielx7/66/10299929/10086611.pdf?arnumber=10086611

Reference19 articles.

1. Anomaly Detection for Semiconductor Tools Using Stacked Autoencoder Learning

2. Data Lifecycle Challenges in Production Machine Learning

3. Equipment Sensor Data Cleansing Algorithm Design for ML-Based Anomaly Detection

4. Effective detection of prominent anomalies from semiconductor equipment sensory data by using unsupervised learning;chen;Proc 18th Int Conf Autom Technol (Automation),2021

5. Equipment Anomaly Detection for Semiconductor Manufacturing by Exploiting Unsupervised Learning from Sensory Data

Cited by 1 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. Guest Editorial Special section on the 2022 International Symposium on Semiconductor Manufacturing;IEEE Transactions on Semiconductor Manufacturing;2023-11