A Gaussian Mixture Model Clustering Ensemble Regressor for Semiconductor Manufacturing Final Test Yield Prediction
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[1] Alípio Mário Jorge,et al. Ensemble approaches for regression: A survey , 2012, CSUR.
[2] Chang Ouk Kim,et al. A Convolutional Neural Network for Fault Classification and Diagnosis in Semiconductor Manufacturing Processes , 2017, IEEE Transactions on Semiconductor Manufacturing.
[3] Jie Zhang,et al. Fuzzy neural network based yield prediction model for semiconductor manufacturing system , 2010 .
[4] Chen-Fu Chien,et al. Analysing semiconductor manufacturing big data for root cause detection of excursion for yield enhancement , 2017, Int. J. Prod. Res..
[5] Joydeep Ghosh,et al. Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions , 2002, J. Mach. Learn. Res..
[6] Toly Chen. Embedding a back propagation network into fuzzy c-means for estimating job cycle time: wafer fabrication as an example , 2016, J. Ambient Intell. Humaniz. Comput..
[7] Jie Zhang,et al. Big data analytics for cycle time related feature selection in the semiconductor wafer fabrication system , 2020, Comput. Ind. Eng..
[8] Dong Ni,et al. A practical yield prediction approach using inline defect metrology data for system-on-chip integrated circuits , 2017, 2017 13th IEEE Conference on Automation Science and Engineering (CASE).
[9] Jee-Hyong Lee,et al. A wafer map yield model based on deep learning for wafer productivity enhancement , 2018, 2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC).
[10] Shi-Chung Chang,et al. SHEWMA: an end-of-line SPC scheme using wafer acceptance test data , 2000 .
[11] Andi Buzo,et al. An accurate yield estimation approach for multivariate non-normal data in semiconductor quality analysis , 2017, 2017 14th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD).
[12] Sungzoon Cho,et al. Using Wafer Map Features to Better Predict Die-Level Failures in Final Test , 2015, IEEE Transactions on Semiconductor Manufacturing.
[13] Toly Chen,et al. Job cycle time estimation in a wafer fabrication factory with a bi-directional classifying fuzzy-neural approach , 2011 .
[14] Ryohei Orihara,et al. A Comprehensive Big-Data-Based Monitoring System for Yield Enhancement in Semiconductor Manufacturing , 2017, IEEE Transactions on Semiconductor Manufacturing.
[15] Junliang Wang,et al. A Data Driven Cycle Time Prediction With Feature Selection in a Semiconductor Wafer Fabrication System , 2018, IEEE Transactions on Semiconductor Manufacturing.
[16] Sirish L. Shah,et al. Fault detection and diagnosis in process data using one-class support vector machines , 2009 .
[17] Gad Rabinowitz,et al. Cycle-Time Key Factor Identification and Prediction in Semiconductor Manufacturing Using Machine Learning and Data Mining , 2011, IEEE Transactions on Semiconductor Manufacturing.
[18] R. Suganya,et al. Fuzzy C- Means Algorithm- A Review , 2012 .
[19] Giuseppe De Nicolao,et al. Multilevel Lasso applied to Virtual Metrology in semiconductor manufacturing , 2011, 2011 IEEE International Conference on Automation Science and Engineering.
[20] Madhu Nashipudimath,et al. Comparative Analysis Of Fuzzy Clustering Algorithms In Data Mining , 2012 .
[21] Muhammad Saqlain,et al. A Voting Ensemble Classifier for Wafer Map Defect Patterns Identification in Semiconductor Manufacturing , 2019, IEEE Transactions on Semiconductor Manufacturing.
[22] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[23] Toly Chen. A PCA-FBPN Approach for Job Cycle Time Estimation in a Wafer Fabrication Factory , 2012, Int. J. Fuzzy Syst. Appl..
[24] Tao Yuan,et al. Yield Prediction for Integrated Circuits Manufacturing Through Hierarchical Bayesian Modeling of Spatial Defects , 2011, IEEE Transactions on Reliability.
[25] Chi-Hyuck Jun,et al. Variable Selection Under Missing Values and Unlabeled Data in Semiconductor Processes , 2019, IEEE Transactions on Semiconductor Manufacturing.
[26] Bernd Barak,et al. Data Mining and Support Vector Regression Machine Learning in Semiconductor Manufacturing to Improve Virtual Metrology , 2013, 2013 46th Hawaii International Conference on System Sciences.
[27] Balázs Kégl,et al. The return of AdaBoost.MH: multi-class Hamming trees , 2013, ICLR.
[28] Ponani S. Gopalakrishnan,et al. Clustering via the Bayesian information criterion with applications in speech recognition , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).
[29] Yoshua Bengio,et al. Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..
[30] Cheong-Sool Park,et al. Data Mining Approaches for Packaging Yield Prediction in the Post-fabrication Process , 2013, 2013 IEEE International Congress on Big Data.
[31] Ron Kohavi,et al. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.
[32] Jie Zhang,et al. Hybrid Feature Selection for Wafer Acceptance Test Parameters in Semiconductor Manufacturing , 2020, IEEE Access.
[33] Kuang-Ku Chen,et al. Integrating support vector machine and genetic algorithm to implement dynamic wafer quality prediction system , 2010, Expert Syst. Appl..
[34] Jong-Seong Kim,et al. A Wafer Map Yield Prediction Based on Machine Learning for Productivity Enhancement , 2019, IEEE Transactions on Semiconductor Manufacturing.