Utilizing an Adaptive Grey Model for Short-Term Time Series Forecasting: A Case Study of Wafer-Level Packaging
Utilizing an Adaptive Grey Model for Short-Term Time Series Forecasting: A Case Study of Wafer-Level Packaging
复制标题
DOI：
10.1155/2013/526806
复制
发表时间：
2013-01-01
影响因子：
--
通讯作者：
Chen, Chien-Chih
中科院分区：
文献类型：
--
作者：
Chang, Che-Jung;Li, Der-Chiang;Chen, Chien-Chih
晶片级封装工艺是半导体制造中的一项重要技术，如何有效地控制这一制造系统是封装企业面临的重要课题。帮助这一过程的一种方法是使用预测工具。然而，在这一过程的早期阶段收集的观测值通常太少，不能用于传统的预测技术，因此得到的结果不准确。这一问题的一个潜在解决方案是使用灰色系统理论，其特点是小数据集建模。因此，本研究使用AGM(1，1)灰色模型来解决包装过程中试运行阶段的预测问题。实验结果表明，灰色预测方法是一种适用于小数据集的有效预测工具，可用于改善晶圆级封装工艺。
The wafer-level packaging process is an important technology used in semiconductor manufacturing, and how to effectively control this manufacturing system is thus an important issue for packaging firms. One way to aid in this process is to use a forecasting tool. However, the number of observations collected in the early stages of this process is usually too few to use with traditional forecasting techniques, and thus inaccurate results are obtained. One potential solution to this problem is the use of grey system theory, with its feature of small dataset modeling. This study thus uses the AGM(1,1) grey model to solve the problem of forecasting in the pilot run stage of the packaging process. The experimental results show that the grey approach is an appropriate and effective forecasting tool for use with small datasets and that it can be applied to improve the wafer-level packaging process.