Big Data Management and Mining
Session Chairs: Armando Anaya, Samira Bagheri
The availability of vast amounts of data is now being met with increasing compute capabilities and algorithmic approaches to predict properties of final products, equipment health and metrology results, and influence WIP flow decisions.
Wednesday, May 15, 2024
9:00AM ET
10.1 Semiconductor Manufacturing Data Synthesis Through Generative Adversarial Networks
B. Tse, T. Wright, E. Nsiye, D. Medina, S. Mondesire, T. Azinord, University of Central Florida
10.2 Probabilistic Modeling and Machine Learning for Semiconductor Manufacturing Tool State Prediction
T. Wright, B. Tse, E. Nsiye, T. Azinord, D. Medina, S. Mondesire, University of Central Florida
10.3 Predictive Analysis and Root Cause Identification of Equipment Failures Using Event Log Patterns: A Focus on EUV/DUV Equipment
W. Ki, M. Choi, J. Park, Y. Lee, J. Park, J. Park, Y. Kim, Samsung Electronics
10.4 Cloud Big Data Lake for Advanced Analytics in Semiconductor Manufacturing
S. Sun, J. Ye, H. Schwarthoff, J. Rosin, V. Vakkalagadda, J. Chang, S. Ubbara, A. Chinthakindi, Micron Technology
10.5 Big Data Variability Study of Advanced 3D NAND Memory Using Python
M. Agam, H. Mebrahtu, H. Dey, Intel Corporation