Analysis and prediction of sputtering yield using combined hierarchical clustering analysis and artificial neural network algorithms  

作　　者：Yu CHEN Jiawei LUO Wen LEI Yan SHEN Shuai CAO 陈煜;罗嘉伟;雷玟;沈岩;曹帅(School of Aeronautics and Astronautics,Sun Yat-sen University,Shenzhen 518107,People’s Republic of China;Shenzhen Key Laboratory of Intelligent Microsatellite Constellation,School of Aeronautics and Astronautics,Shenzhen 518107,People’s Republic of China)

机构地区：[1]School of Aeronautics and Astronautics,Sun Yat-sen University,Shenzhen 518107,People’s Republic of China [2]Shenzhen Key Laboratory of Intelligent Microsatellite Constellation,School of Aeronautics and Astronautics,Shenzhen 518107,People’s Republic of China

出　　处：《Plasma Science and Technology》2024年第11期162-169,共8页等离子体科学和技术（英文版）

基　　金：supported by the National Key Research and Development Program of China (No. 2020YFC2201101);the Shenzhen Key Laboratory of Intelligent Microsatellite Constellation (No. ZDSYS20210623091808026);Guangdong Basic and Applied Basic Research Foundation (No. 2021A1515110500)。

摘　　要：Sputtering is a crucial technology in fields such as electric propulsion, materials processing and semiconductors. Modeling of sputtering is significant for improving thruster design and designing material processing control algorithms. In this study we use the hierarchical clustering analysis algorithm to perform cluster analysis on 17 descriptors related to sputtering. These descriptors are divided into four fundamental groups, with representative descriptors being the mass of the incident ion, the formation energy of the incident ion, the mass of the target and the formation energy of the target. We further discuss the possible physical processes and significance involved in the classification process, including cascade collisions, energy transfer and other processes. Finally, based on the analysis of the above descriptors, several neural network models are constructed for the regression of sputtering threshold E_(th), maximum sputtering energy E_(max) and maximum sputtering yield SY_(max). In the regression model based on 267 samples, the four descriptor attributes showed higher accuracy than the 17 descriptors(R^(2) evaluation) in the same neural network structure, with the 5×5 neural network structure achieving the highest accuracy, having an R^(2) of 0.92. Additionally, simple sputtering test data also demonstrated the generalization ability of the 5×5 neural network model, the error in maximum sputtering yield being less than 5%.

关 键 词：SPUTTERING machine learning PLASMA 

分 类 号：TB30-4[一般工业技术—材料科学与工程] TP183[自动化与计算机技术—控制理论与控制工程]