...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Enterprise information systems >Big data driven cycle time parallel prediction for production planning in wafer manufacturing
【24h】

Big data driven cycle time parallel prediction for production planning in wafer manufacturing

机译：大数据驱动周期时间并行预测，用于晶圆制造中的生产计划

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Cycle time forecasting (CTF) is one of the most crucial issues for production planning to keep high delivery reliability in semiconductor wafer fabrication systems (SWFS). This paper proposes a novel data-intensive cycle time (CT) prediction system with parallel computing to rapidly forecast the CT of wafer lots with large datasets. First, a density peak based radial basis function network (DP-RBFN) is designed to forecast the CT with the diverse and agglomerative CT data. Second, the network learning method based on a clustering technique is proposed to determine the density peak. Third, a parallel computing approach for network training is proposed in order to speed up the training process with large scaled CT data. Finally, an experiment with respect to SWFS is presented, which demonstrates that the proposed CTF system can not only speed up the training process of the model but also outperform the radial basis function network, the back-propagation-network and multivariate regression methodology based CTF methods in terms of the mean absolute deviation and standard deviation.
机译：周期时间预测（CTF）是生产计划中最关键的问题之一，以保持半导体晶圆制造系统（SWFS）的高交付可靠性。本文提出了一种新颖的具有并行计算功能的数据密集型周期时间（CT）预测系统，可以快速预测具有大型数据集的晶圆批的CT。首先，设计了基于密度峰的径向基函数网络（DP-RBFN），以利用各种聚集的CT数据预测CT。其次，提出了一种基于聚类技术的网络学习方法来确定密度峰值。第三，提出了一种用于网络训练的并行计算方法，以加快大规模CT数据的训练过程。最后，针对SWFS进行了实验，表明所提出的CTF系统不仅可以加快模型的训练过程，而且还优于基于径向基函数网络，反向传播网络和基于CTF的多元回归方法在平均绝对偏差和标准偏差方面的方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号