Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing
Authors
Keywords
-
Journal
JOURNAL OF INTELLIGENT MANUFACTURING
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2020-05-19
DOI
10.1007/s10845-020-01591-0
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- A hybrid information model based on long short-term memory network for tool condition monitoring
- (2020) Weili Cai et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Similarity matching of wafer bin maps for manufacturing intelligence to empower Industry 3.5 for semiconductor manufacturing
- (2020) Chia-Yu Hsu et al. COMPUTERS & INDUSTRIAL ENGINEERING
- A case study of conditional deep convolutional generative adversarial networks in machine fault diagnosis
- (2020) Jia Luo et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Development of a speed invariant deep learning model with application to condition monitoring of rotating machinery
- (2020) Wo Jae Lee et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Fault Detection and Diagnosis Using Self-Attentive Convolutional Neural Networks for Variable-Length Sensor Data in Semiconductor Manufacturing
- (2019) Eunji Kim et al. IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING
- Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations
- (2019) Zhiwen Huang et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Automatic equipment fault fingerprint extraction for the fault diagnostic on the batch process data
- (2018) Hamideh Rostami et al. APPLIED SOFT COMPUTING
- Data-driven prognostic method based on self-supervised learning approaches for fault detection
- (2018) Tian Wang et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Literature review of Industry 4.0 and related technologies
- (2018) Ercan Oztemel et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation
- (2018) Xiang Li et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Translation-Invariant Multiscale Energy-Based PCA for Monitoring Batch Processes in Semiconductor Manufacturing
- (2017) Tiago J. Rato et al. IEEE Transactions on Automation Science and Engineering
- A Deep Learning Model for Robust Wafer Fault Monitoring With Sensor Measurement Noise
- (2017) Hoyeop Lee et al. IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING
- A Convolutional Neural Network for Fault Classification and Diagnosis in Semiconductor Manufacturing Processes
- (2017) Ki Bum Lee et al. IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING
- Data based segmentation and summarization for sensor data in semiconductor manufacturing
- (2013) Eunjeong L. Park et al. EXPERT SYSTEMS WITH APPLICATIONS
- Semiconductor fault detection and classification for yield enhancement and manufacturing intelligence
- (2012) Chen-Fu Chien et al. Flexible Services and Manufacturing Journal
- Fault Detection Using Principal Components-Based Gaussian Mixture Model for Semiconductor Manufacturing Processes
- (2011) Jianbo Yu IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING
- Heartbeat Time Series Classification With Support Vector Machines
- (2009) A. Kampouraki et al. IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE
- Fault detection and diagnosis in process data using one-class support vector machines
- (2009) Sankar Mahadevan et al. JOURNAL OF PROCESS CONTROL
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreFind the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
Search