Equipment Sensor Data Cleansing Algorithm Design for ML-Based Anomaly Detection
Author:
Affiliation:
1. National Taiwan University,Taipei,Taiwan,10617
2. Straight & Up Intelligent Innovations Group Co.,San Jose,CA,USA,95113
Funder
Ministry of Science and Technology, Taiwan, R.O.C.
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10026850/10026886/10027125.pdf?arnumber=10027125
Reference7 articles.
1. Statistics pattern analysis: A new process monitoring framework and its application to semiconductor batch processes
2. A Convolutional Neural Network for Fault Classification and Diagnosis in Semiconductor Manufacturing Processes
3. Equipment Condition Diagnosis and Fault Fingerprint Extraction in Semiconductor Manufacturing
4. Effective Detection of Prominent Anomalies from Semiconductor Equipment Sensory Data by Using Unsupervised Learning;chen;Proc of The 18th International Conference on Automation Technology (Automation 2021),0
5. Recipe-Independent Indicator for Tool Health Diagnosis and Predictive Maintenance
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. Data Cleansing With Minimum Distortion for ML-Based Equipment Anomaly Detection;IEEE Transactions on Semiconductor Manufacturing;2023-11
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