Pruning Quantized Unsupervised Meta-Learning DegradingNet Solution for Industrial Equipment and Semiconductor Process Anomaly Detection and Prediction

Pruning Quantized Unsupervised Meta-Learning DegradingNet Solution for Industrial Equipment and Semiconductor Process Anomaly Detection and Prediction

Machine- and deep-learning methods are used for industrial applications in prognostics and health management (PHM) for semiconductor processing and equipment anomaly detection to achieve proactive equipment maintenance and prevent process interruptions or equipment downtime. This study proposes a Pr...

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Bibliographic Details
Main Authors: Yi-Cheng Yu, Shiau-Ru Yang, Shang-Wen Chuang, Jen-Tzung Chien, Chen-Yi Lee
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Applied Sciences
Subjects:
anomaly detection
deep learning
prediction
pruning
edge computing
intelligent equipment management
Online Access:https://www.mdpi.com/2076-3417/14/5/1708

Internet

https://www.mdpi.com/2076-3417/14/5/1708

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