Nondestructive monitoring of annealing and chemical-mechanical planarization behavior using ellipsometry and deep learning  

作　　者：Qimeng Sun Dekun Yang Tianjian Liu Jianhong Liu Shizhao Wang Sizhou Hu Sheng Liu Yi Song 

机构地区：[1]The Institute of Technological Sciences,Wuhan University,Wuhan,China [2]School of Mechanical Science and Engineering,Huazhong University of Science and Technology,Wuhan,China [3]Hongyi Honor College of Wuhan University,Wuhan,China [4]school of Power and Mechanical Engineering,Wuhan University,Wuhan,China [5]The School of Microelectronics,Wuhan University,Wuhan,China

出　　处：《Microsystems & Nanoengineering》2023年第2期345-354,共10页微系统与纳米工程（英文）

基　　金：supported by the National Key Research and Development Program of China(2022YFB3206000);the Key Research and Development Program of Hubei(2021BAA173)。

摘　　要：The Cu-flling process in through-silicon via(TSV-Cu)is a key technology for chip stacking and three-dimensional vertical packaging.During this process,defects resulting from chemical-mechanical planarization(CMP)and annealing severely affect the reliability of the chips.Traditional methods of defect characterization are destructive and cumbersome.In this study,a new defect inspection method was developed using Mueller matrix spectroscopic ellipsometry.TSV-Cu with a 3-μm-diameter and 8-μm-deep Cu filling showed three typical types of characteristics:overdishing(defect-OD),protrusion(defect-P),and defect-free.The process dimension for each defect was 13 nm.First,the three typical defects caused by CMP and annealing were investigated.With single-channel deep learning and a Mueller matrix element(MME),the TSV-Cu defect types could be distinguished with an accuracy rate of 99.94%.Next,seven effective MMEs were used as independent channels in the artificial neural network to quantify the height variation in the Cu flling in the z-direction.The accuracy rate was 98.92%after training,and the recognition accuracy reached 1 nm.The proposed approach rapidly and nondestructively evaluates the annealing bonding performance of CMP processes,which can improve the reliability of high-density integration.

关 键 词：ANNEALING DEFECT CHEMICAL 

分 类 号：TG115.28[金属学及工艺—物理冶金]