Virtual Metrology for Etch Profile in Silicon Trench Etching With SF6/O2/Ar Plasma
Virtual Metrology for Etch Profile in Silicon Trench Etching With SF6/O2/Ar Plasma
复制标题
DOI：
10.1109/tsm.2021.3138918
复制
发表时间：
2022-02-01
影响因子：
2.7
通讯作者：
Hong, Sang Jeen
中科院分区：
文献类型：
--
作者：
Choi, Jeong Eun;Park, Hyoeun;Hong, Sang Jeen
本研究对SF6/O-2/Ar等离子体刻蚀硅深槽过程中的刻蚀轮廓和刻蚀深度进行了虚拟测量。基于机器学习的VM模型构成了硅槽刻蚀轮廓的分类模型和刻蚀深度的预测模型。利用基于配方的设备状态变量识别(SVID)数据，采用随机森林和XgBoost机器学习算法对刻蚀剖面进行分类。用神经网络模型构建的预测刻蚀深度模型同时使用了设备的SVID数据和光学发射光谱(OES)数据，这些数据提供了刻蚀过程中等离子体的化学信息。等离子体Vm模型，将OES数据扩展到SVID数据，提高了预测刻蚀轮廓的准确性。刻蚀过程中增强的唯象等离子体信息有助于在等离子体过程中建立更准确的VM模型。通过每个模型的排列重要度来确定变量的重要性。以SF6/O-2/Ar等离子体的刻蚀反应为例，对重要变量的实际工艺结果进行了分析。
This study practiced virtual metrology (VM) for the etch profile and depth in the deep silicon trench etching with SF6/O-2/Ar plasma. Machine learning-based VM models constitute the classification models of etch profile and the prediction models of etch depth from the silicon trench etch. Machine learning algorithms of random forest and Xgboost were used for classifying etch profiles by employing recipe-based equipment status variable identification (SVID) data. Predictive etch depth models constructed with neural network models employed both equipment SVID data and optical emission spectroscopy (OES) data, which provide chemistry information of the plasma during the etch process. Plasma VM model, augmenting OES data to SVID data presented improved accuracy in predicting etch profile. The augmented phenomenological plasma information during the etch process helped to establish a more accurate VM model in the plasma process. The importance of variables was identified through the permutation importance of each model. Additionally, the actual process results of the variables with high importance were analyzed with the etching reaction of SF6/O-2/Ar plasma.