...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Production Research >Online parameter estimation and run-to-run process adjustment using categorical observations
【24h】

Online parameter estimation and run-to-run process adjustment using categorical observations

机译：使用分类观测值进行在线参数估计和运行过程调整

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

Categorical observations are frequently observed in run-to-run processes where obtaining accurate measurements of quality characteristics is difficult. In such circumstances, the use of categorical observations to estimate a process model and generate an adjustment recipe becomes inevitable. However, most conventional run-to-run controllers cannot be applied if no continuous observations are available; some parameter estimation methods that can handle categorical data only use historical dataset in an offline manner. In practice, it is common to see observations collected following a time sequence in a run-to-run process. Taking the lapping process in semiconductor manufacturing as an example, this paper develops an online approach for parameters estimation and run-to-run process adjustment using categorical observations. The proposed method optimises a penalised Maximum Likelihood (ML) function and updates parameters step by step when new categorical observations become available. A control strategy is also derived to generate receipts for process update between runs. The computational results of performance evaluation show that the proposed method is capable of estimating unknown parameters and control output quality online when initial bias exists.
机译：在难以获得准确的质量特征度量的逐次运行过程中，经常观察到分类观察。在这种情况下，不可避免地要使用分类观察来估计过程模型并生成调整配方。但是，如果没有连续的观测值，大多数常规的运行到运行控制器将无法应用。某些可以处理分类数据的参数估计方法仅以脱机方式使用历史数据集。在实践中，通常会在运行过程中看到按时间顺序收集的观察结果。以半导体制造中的研磨工艺为例，本文开发了一种在线方法，用于使用分类观测值进行参数估计和运行至生产过程的调整。所提出的方法优化了惩罚最大似然（ML）函数，并在有新的分类观测值可用时逐步更新参数。还导出控制策略以生成收据，以在两次运行之间进行过程更新。性能评估的计算结果表明，该方法能够在存在初始偏差的情况下，估计未知参数并在线控制输出质量。

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