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 ...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.展开更多
基金supported by the National Natural Science Foundation of China(No.60904053)the Natural Science Foundation of Jiangsu(No. SBK201123307)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)
文摘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.
