深度反应离子蚀刻过程的统计关键变量分析与推理控制

随着集成电路（IC）的小型化和智能化的需求不断增加，集成电路的电子封装已成为一个新的研究热点。 3D IC利用硅通孔技术（TSV）满足了这一需求，利用深度反应离子蚀刻（DRIE）过程制造TSV也越来越成熟。然而，能够预测和控制硅蚀刻工艺参数对蚀刻质量的影响是DRIE过程的关键。本研究提出一种基于变量分析建立DRIE过孔深度虚拟测量(VM)模型方法。利用特征提取算法减少过程数据的维度，以便于后续模型辨识的需要。在VM模型的基础上，设计推论控制器，提高晶元的深孔质量。实际工业数据验证表明：该方法可以将误差减少到9*10（-4），大大的改善现有的DRIE工艺。

Statistical key variable analysis and model-based control for improvement performance in a deep reactive ion etching process

This paper proposes to develop a data-driven via's depth estimator of the deep reactive ion etching process based on statistical identification of key variables. Several feature extraction algorithms are presented to reduce the high-dimensional data and effectively undertake the subsequent virtual metrology (VM) model building process. With the available on-line VM model, the model-based controller is hence readily applicable to improve the quality of a via's depth. Real operational data taken from a industrial manufacturing process are used to verify the effectiveness of the proposed method. The results demonstrate that the proposed method can decrease the MSE from 2.2×10^-2 to 9×10^-4 and has great potential in improving the existing DRIE process.