[1]吕佑龙, 许鸿伟, 郑城,等. 基于混合式特征选择模型的晶圆允收测试关键参数识别方法[J].中国机械工程, 2020, 31(16):1978-1984.
LYU Youlong, XU Hongwei, ZHENG Cheng, et al. Wafer Acceptance Test key Parameter Identification Based on Hybrid Feature Selection Method[J]. China Mechanical Engineering, 2020, 31(16):1978-1984.
[2]朱雪初, 乔非. 基于工业大数据的晶圆制造系统加工周期预测方法[J]. 计算机集成制造系统, 2017,23(10):2172-2179.
ZHU Xuechu, QIAO Fei. Process Cycle Prediction Method for Wafer Manufacturing System Based on Industrial Big Data[J]. Computer Integrated Manufacturing System, 2017, 23(10):2172-2179.
[3]WANG Junliang, YANG Jungang, ZHANG Jie, et al. Big Data Driven Cycle Time Parallel Prediction for Production Planning in Wafer Manufacturing[J]. Enterprise Information System, 2018, 12(6):714-732.
[4]TAI Y, PEARN W, LEE J. Cycle Time Estimation for Semiconductor Final Testing Processes with Weibull-distributed Waiting Time[J]. International Journal of Production Research, 2012, 50(2):581-592.
[5]YANG F, ANKENMAN B. Estimating Cycle Time Percentile Curves for Manufacturing Systems via Simulation[J]. INFORMS Journal on Computing, 2008, 20(4):628-643.
[6]YANG F, ANKENMAN B, NELSON L. Efficient Generation of Cycle Time-throughput Curves through Simulation and Metamodeling[J]. Naval Research Logistics, 2007, 54(1):78-93.
[7]HSIEH L, CHANG K. Efficient Development of Cycle Time Response Surfaces Using Progressive Simulation Metamodeling[J]. International Journal of Production Research, 2014, 52(10):3097-3109.
[8]SCHELASIN R. Using Static Capacity Modeling and Queuing Theory Equation to Predict Factory Cycle Time Performance in Semiconductor[C]∥IEEE Proceedings of the 2011 Winter Simulation Conference (WSC). Phoenix, 2011:2040-2049.
[9]CHUANG S, HUANG H. Cycle Time Estimation for Wafer Fab with Engineering Lots[J]. IIE Transactions, 2022, 34(2):105-118.
[10]WANG Junliang, ZHANG Jie, WANG Xiaoxi. Bilateral LSTM:a Two-dimensional Long Short-term Memory Model with Multiply Memory Units for Short-term Cycle Time Forecasting in Re-entrant Manufacturing Systems[J]. IEEE Transactions on Industrial Informatics. 2018, 14(2):748-758.
[11]CHEN Toly, WANG Yichi. An Iterative Procedure for Optimizing the Performance of the Fuzzy-Neural Job Cycle Time Estimation Approach in a Wafer Fabrication Factory[J]. Mathematical Problems in Engineering, 2013,2013:740478.
[12]CHIEN C, HSU C, HSIAO C. Manufacturing Intelligence to Forecast and Reduce Semiconductor Cycle Time[J]. Journal of Intelligent Manufacturing, 2012, 23(6):2281-2294.
[13]TIRKEL L. Forecasting Flow Time in Semiconductor Manufacturing Using Knowledge Discovery in Database[J]. International Journal of Production Research, 2013, 51(18):5536-5548.
[14]WANG Junliang, ZHENG Peng, ZHANG Jie. Big Data Analytics for Cycle Time Related Feature Selection in the Semiconductor Wafer Fabrication System[J]. Computers & Industrial Engineering, 2014, 6(2):409-426.
[15]PALMA M, RODRIGUEZ D. Distributed Relief F-based Feature Selection in Spark[J]. Knowledge and Information Systems, 2018, 57(1):1-20.
[16]WANG C, PEDRYCZ W, YANG J, et al. Wavelet Frame-based Fuzzy C-means Clustering for Segmenting Images on Graphs[J]. IEEE Transactions on Cybernetics, 2020, 50(9):3938-3949.
[17]GAO Shuai, HUANG Yuefei, ZHANG Shuo, et al. Short-term Runoff Prediction with GRU and LSTM Networks without Requiring Time Step Optimization during Sample Generation[J]. Journal of Hydrology, 2020, 589:125188.
[18]YAIR M, BOAZ L,GAD R, et al. Cycle-time Key Factor Identification and Prediction in Semiconductor Manufacturing Using Machine Learning and Data Mining[J]. IEEE Transactions on Semiconductor Manufacturing, 2011, 24(2):237-248.
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